WO2021259339A1 - 一种x射线成像设备的建模方法和装置 - Google Patents

一种x射线成像设备的建模方法和装置 Download PDF

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WO2021259339A1
WO2021259339A1 PCT/CN2021/101923 CN2021101923W WO2021259339A1 WO 2021259339 A1 WO2021259339 A1 WO 2021259339A1 CN 2021101923 W CN2021101923 W CN 2021101923W WO 2021259339 A1 WO2021259339 A1 WO 2021259339A1
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ray
conversion
energy
image
optimal
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PCT/CN2021/101923
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English (en)
French (fr)
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姚鹏
袁洲
张文日
冷官冀
崔凯
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上海联影医疗科技股份有限公司
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Priority claimed from CN202010582483.6A external-priority patent/CN111783292B/zh
Priority claimed from CN202010580387.8A external-priority patent/CN111914392B/zh
Application filed by 上海联影医疗科技股份有限公司 filed Critical 上海联影医疗科技股份有限公司
Publication of WO2021259339A1 publication Critical patent/WO2021259339A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/40—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management of medical equipment or devices, e.g. scheduling maintenance or upgrades
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/10—Numerical modelling

Definitions

  • This application relates to the field of medical imaging technology, in particular to a modeling method and device for X-ray imaging equipment.
  • the X-ray imaging device needs to be modeled when it is necessary to calculate and analyze the X-ray dose distribution and the image generated by the X-ray imaging device.
  • the traditional approach is to use Monka software to simulate X-ray imaging equipment.
  • the efficiency of using Monka software to simulate X-ray imaging equipment is very low.
  • the mammography machine when the X-ray imaging equipment is a mammography machine, the mammography machine has 21 voltage options ranging from 20KV to 40KV and two types of filtration (rhodium filtration and silver filtration), so theoretically the mammography machine may produce 42 energy spectra. If you want to calculate and analyze the dose distribution and images produced by the mammography machine, you need to model the mammography machine.
  • the traditional method is to use the Monka software to simulate the entire equipment of the breast machine. Under the premise of fixed voltage and filtering, if the simulated image is consistent with the measured image, it can be considered that the current simulation program for the breast machine is correct.
  • One of the embodiments of the present application provides a modeling method of an X-ray imaging device
  • the X-ray imaging device includes an X-ray source for emitting X-rays and for converting X-rays penetrating a scanning object into X-ray images
  • the detector characterized in that, the modeling method includes: obtaining the optimal energy spectrum of the X-ray emitted by the X-ray source; obtaining the optimal conversion function of the detector; and according to the optimal energy spectrum
  • the X-ray source is modeled, and the detector is modeled according to the optimal conversion function to obtain a model of the X-ray imaging device.
  • the obtaining the optimal energy spectrum of the X-ray emitted by the X-ray source and obtaining the optimal conversion function of the detector to the X-ray includes: obtaining an initial energy spectrum and a first initial energy spectrum. Conversion matrix; obtain a single-energy dose nucleus; based on the initial energy spectrum, splice multiple different single-energy dose nuclei into a multi-energy dose nucleus; obtain the X-ray dose of the multi-energy dose nucleus in the scanning object Distribution; obtain an X-ray image of the scanned object according to the first initial conversion matrix and the dose distribution;
  • the optimal energy spectrum is determined from the initial energy spectra and the optimal conversion matrix is determined from a plurality of the first initial conversion matrices; wherein, the optimal conversion function indicates that the detector divides the dose distribution Converted to the conversion relationship of the X-ray image, and the optimal conversion matrix is a coefficient in the optimal conversion function.
  • the acquiring the initial energy spectrum and the first initial conversion matrix includes: configuring the initial energy spectrum and the first initial conversion matrix according to the performance parameters of the X-ray imaging device.
  • the acquiring the X-ray dose distribution of the multi-energy dose nucleus in the scanning object includes: configuring a flux matrix according to the performance parameters of the X-ray imaging device; acquiring photon flux The initial value of the distribution, and according to the initial value of the photon flux distribution, the photon flux distribution of the X-ray of the multi-energy dose nucleus is obtained; the initial value of the photon flux distribution is the X-ray of each single energy dose nucleus According to the attenuation law, the flux matrix and the X-ray photon flux distribution of the multi-energy dose nucleus, obtain the attenuated photon flux distribution after penetrating the scanning object; and The attenuated photon flux distribution is convolved with the multi-energy dose nucleus to obtain the dose distribution.
  • the obtaining the optimal energy spectrum of the X-ray emitted by the X-ray source and obtaining the optimal conversion function of the detector to the X-ray includes: obtaining an initial energy spectrum and an initial conversion function Obtain a single-energy dose nucleus; based on the initial energy spectrum, splice a plurality of different single-energy dose nuclei into a multi-energy dose nucleus; obtain the X-ray of the multi-energy dose nucleus after penetrating the scanning object attenuated Photon flux distribution; obtain the X-ray image of the scanned object according to the initial conversion function and the attenuated photon flux distribution; obtain the measured image, and find the X-ray closest to the measured image Image to determine the optimal energy spectrum among the plurality of initial energy spectra and determine the optimal conversion function among the plurality of initial energy spectra; wherein, the optimal conversion function represents the detector A conversion relationship of converting X-rays penetrating the attenuated multi-energy dose nucleus of
  • the obtaining the initial conversion function includes: obtaining the initial value of the conversion function of the detector for each single-energy photon according to the drawing of the detector; obtaining the attenuated X-rays penetrating the scanning object Energy spectrum; and based on the attenuated X-ray energy spectrum, splicing a plurality of the single-energy photons into the attenuated X-rays of the multi-energy dose nuclear, and obtaining the initial conversion function according to the initial value of the conversion function .
  • the obtaining the optimal conversion function of the detector to the X-ray includes: obtaining a second initial conversion matrix of the scanning object of each thickness; and according to the optimal energy spectrum
  • the X-ray source is modeled, and the detector is modeled according to the second initial conversion matrix to obtain the X-ray image of the scanned object; the measured image is acquired, and the measured image is found
  • the closest X-ray image is used to determine the optimal transformation matrix corresponding to the scanning object of different thickness in the second initial transformation matrix; wherein, the optimal transformation function indicates that the detector will
  • the X-ray emitted by the X-ray source is converted into a conversion relationship of the X-ray image of the scanning object, and the optimal conversion matrix is a coefficient in the optimal conversion function.
  • the obtaining the optimal energy spectrum of the X-rays emitted by the X-ray source includes: using a Monte Carlo algorithm to simulate the X-ray source to obtain the optimal energy spectrum; or, using The X-ray source of the X-ray imaging device is tested to obtain the optimal energy spectrum.
  • the method further includes: using a model of the X-ray imaging device to obtain an X-ray image of the scanned object.
  • the X-ray imaging equipment includes an X-ray source for emitting X-rays and for converting X-rays penetrating a scanning object into X-ray images
  • the detector characterized in that, the modeling device includes: an energy spectrum acquisition module for acquiring the optimal energy spectrum of X-rays emitted by the X-ray source; a conversion function acquisition module for acquiring the detector The optimal conversion function; a modeling module for modeling the X-ray source according to the optimal energy spectrum, and modeling the detector according to the optimal conversion function to obtain the Model of X-ray imaging equipment.
  • the conversion function obtaining module is further used to: obtain an initial energy spectrum and a first initial conversion matrix; obtain a single-energy dose nucleus; based on the initial energy spectrum, splice a plurality of different single-energy dose nuclei Into a multi-energy dose nucleus; acquiring the X-ray dose distribution of the multi-energy dose nucleus in the scanning object; obtaining an X-ray image of the scanning object according to the first initial conversion matrix and the dose distribution; And acquiring the actual measurement image, and finding the X-ray image that is closest to the actual measurement image, so as to determine the optimal energy spectrum among the plurality of initial energy spectra and among the plurality of first initial conversion matrices Determine an optimal conversion matrix; wherein, the optimal conversion function represents a conversion relationship of the detector to convert the dose distribution into the X-ray image, and the optimal conversion matrix is the value in the optimal conversion function coefficient.
  • the conversion function obtaining module is further used to: obtain an initial energy spectrum and an initial conversion function; obtain a single-energy dose nucleus; based on the initial energy spectrum, splice a plurality of different single-energy dose nuclei into multiple Energy dose nucleus; obtain the attenuated photon flux distribution of the X-ray of the multi-energy dose nucleus after penetrating the scanning object; obtain the scan according to the initial transfer function and the attenuated photon flux distribution The X-ray image of the object; obtain a measured image, and find the X-ray image closest to the measured image, so as to determine the optimal energy spectrum among the plurality of initial energy spectra and in the plurality of initial energy spectra The optimal conversion function is determined in the conversion function; wherein, the optimal conversion function represents the conversion relationship of the X-ray that penetrates the attenuated multi-energy dose nucleus of the scanning object into an X-ray image by the detector.
  • the conversion function obtaining module is further configured to: obtain the second initial conversion matrix of the scanning object of each thickness separately; model the X-ray source according to the optimal energy spectrum, The detector is modeled according to the second initial conversion matrix to obtain the X-ray image of the scanned object; the measured image is acquired, and the X-ray image closest to the measured image is found to In the second initial conversion matrix, the optimal conversion matrix corresponding to the scanning object of different thicknesses is determined; wherein, the optimal conversion function indicates that the detector converts the X-rays emitted by the X-ray source into For the conversion relationship of the X-ray image of the scanning object, the optimal conversion matrix is a coefficient in the optimal conversion function.
  • One of the embodiments of the present application provides a modeling device for X-ray imaging equipment.
  • the device includes at least one processor and at least one memory; the at least one memory is used to store computer instructions; the at least one processor is used to execute At least part of the computer instructions are used to implement the X-ray imaging device modeling method provided in one of the embodiments of the present application.
  • One of the embodiments of the present application provides a computer-readable storage medium that stores computer instructions. After the computer reads the computer instructions in the storage medium, the computer executes the X-ray imaging device provided in one of the embodiments of the present application. The modeling method.
  • One of the embodiments of the present application provides a modeling method for X-ray imaging equipment, including: obtaining the simulated flux distribution and energy spectrum of the X-ray imaging equipment without grid, and the tested one with grid Measured images of multiple phantoms with different thicknesses; respectively simulate the multi-energy dose nucleus of each phantom according to the energy spectrum, and calculate the calculated image of each phantom according to calculation factors, wherein, The calculation factors include the flux distribution, each of the multi-energy dose nuclei, and the conversion relationship to be adjusted; compare the differences between each of the calculated images and each of the measured images, and determine the conversion according to the comparison result Whether the relationship needs to be adjusted.
  • the judging whether the conversion relationship needs to be adjusted according to the comparison result includes: if it is determined according to the comparison result that the conversion relationship needs to be adjusted, adjusting the conversion relationship according to the comparison result, and according to The adjusted conversion relationship updates the conversion relationship; and the step of separately calculating the calculated image of each phantom according to calculation factors is repeated until the comparison result meets the preset modeling condition.
  • the multi-energy dose nucleus is a gridless multi-energy dose nucleus
  • the conversion relationship includes a first conversion relationship corresponding to each of the phantoms
  • the method further includes: obtaining a target conversion relationship according to each of the first conversion relationships, where the target conversion relationship is a conversion relationship corresponding to each of the phantoms.
  • the obtaining the target conversion relationship according to each of the first conversion relationships includes: respectively determining the second conversion relationship of each untested phantom that is not tested according to each of the first conversion relationships; The first conversion relationship and each of the second conversion relationships obtain a target conversion relationship.
  • the multi-energy dose core is a gridded multi-energy dose core
  • the conversion relationship is a third conversion relationship corresponding to each of the phantoms
  • the calculation factor further includes each of the phantoms Corresponding attenuation ratio; said adjusting the conversion relationship according to the comparison result, and updating the conversion relationship according to the adjusted conversion relationship, including: adjusting each of the attenuation ratios according to the comparison result, and according to the adjusted each Each of the attenuation ratios is updated by the attenuation ratio; the third conversion relationship is adjusted according to each of the attenuation ratios, and the third conversion relationship is updated according to the adjusted third conversion relationship; After the result satisfies the preset modeling condition, it further includes: fitting each of the attenuation ratios to obtain a fitting result between the attenuation ratio and the thickness.
  • the method further includes: adjusting each parameter in a preset flux distribution fitting function according to the comparison result, and updating according to the adjusted fitting result of the preset flux distribution fitting function The flux distribution.
  • the separately comparing the differences between each of the calculated images and each of the measured images includes: calculating pixels at corresponding positions between the calculated image and the measured image corresponding to the same phantom The difference value, and the comparison result is determined according to the pixel difference value corresponding to each phantom.
  • the conversion relationship includes a conversion matrix and/or a multi-energy conversion function
  • the multi-energy conversion function is based on the irradiation of each single-energy photon simulated by a detector in the X-ray imaging device on the It is obtained by combining the single-energy conversion functions on the detector.
  • the method further includes: obtaining the attenuation degree of the simulated X-ray irradiated on the object to be simulated, and obtaining the thickness of the object to be simulated according to the attenuation degree and the adjusted conversion relationship; Based on the multi-energy dose nucleus corresponding to the thickness of the object to be simulated, the flux distribution, and the adjusted conversion relationship, a simulated image of the object to be simulated is calculated.
  • One of the embodiments of the present application provides a modeling device for X-ray imaging equipment, including: a data acquisition module for acquiring the simulated flux distribution and energy spectrum of the X-ray imaging equipment without grid, and tested Measured images of multiple phantoms with different thicknesses with a grid; an image calculation module for simulating the multi-energy dose nucleus of each phantom according to the energy spectrum, and respectively calculating according to the calculation factors The calculated images of each of the phantoms, wherein the calculation factors include the flux distribution, each of the multi-energy dose nuclei, and the conversion relationship to be adjusted; the adjustment judgment module is used to compare each of the calculated images and The difference between the actual measured images is determined, and the conversion relationship needs to be adjusted according to the comparison result.
  • the adjustment judgment module is further configured to: if it is determined according to the comparison result that the conversion relationship needs to be adjusted, adjust the conversion relationship according to the comparison result, and update the conversion relationship according to the adjusted conversion relationship.
  • the conversion relationship repeating the step of separately calculating the calculated image of each phantom according to the calculation factors, until the comparison result meets the preset modeling condition.
  • the adjustment and judgment module is further configured to: calculate the pixel difference of the corresponding position between the calculated image and the measured image corresponding to the same phantom, and according to all the corresponding phantoms. The pixel difference value determines the comparison result.
  • the conversion relationship includes a conversion matrix and/or a multi-energy conversion function
  • the multi-energy conversion function is simulated according to the blueprint of the detector in the X-ray imaging device.
  • the single-energy conversion function on the detector is spliced.
  • the device further includes: an analog image acquisition module for acquiring the attenuation degree of the simulated X-ray irradiated on the object to be simulated, and obtaining the attenuation degree according to the attenuation degree and the adjusted conversion relationship.
  • an analog image acquisition module for acquiring the attenuation degree of the simulated X-ray irradiated on the object to be simulated, and obtaining the attenuation degree according to the attenuation degree and the adjusted conversion relationship.
  • One of the embodiments of the present application provides a modeling device for X-ray imaging equipment.
  • the device includes at least one processor and at least one memory; the at least one memory is used to store computer instructions; the at least one processor is used to execute At least part of the computer instructions are used to implement a modeling method of an X-ray imaging device as provided in one of the embodiments of the present application.
  • One of the embodiments of the present application provides a computer-readable storage medium, wherein the storage medium stores computer instructions. After the computer reads the computer instructions in the storage medium, the computer executes the A modeling method for X-ray imaging equipment.
  • Fig. 1 is a schematic diagram of an application scenario of an exemplary X-ray imaging device modeling apparatus according to some embodiments of the present application;
  • Fig. 2 is an exemplary flowchart of a modeling method of an X-ray imaging device according to some embodiments of the present application
  • Fig. 3 is an exemplary flowchart of a modeling method of an X-ray imaging device according to some embodiments of the present application
  • Fig. 4 is an exemplary flow chart for obtaining the dose distribution of the X-ray of the multi-energy dose nucleus in the scanned object according to some embodiments of the present application;
  • Fig. 5 is a schematic diagram of the photon flux distribution of X-rays of a multi-energy dose nucleus according to some embodiments of the present application.
  • Fig. 6a is a schematic diagram of a first initial conversion matrix according to some embodiments of the present application.
  • FIG. 6b is a schematic diagram of the fitting effect of the first initial conversion matrix on the X-ray dose distribution of the multi-energy dose nuclear in the scanned object according to some embodiments of the present application;
  • Fig. 7 is an exemplary flowchart of a modeling method of an X-ray imaging device according to some embodiments of the present application.
  • Fig. 8 is an exemplary flow chart of obtaining an initial conversion function according to some embodiments of the present application.
  • Fig. 9 is an exemplary flowchart of a modeling method of an X-ray imaging device according to some embodiments of the present application.
  • Fig. 10 is an exemplary block diagram of a modeling device of an X-ray imaging device according to some embodiments of the present application.
  • Fig. 11 is an exemplary flowchart of a modeling method of an X-ray imaging device according to some embodiments of the present application.
  • FIG. 12a is a schematic diagram of a conversion relationship of a modeling method of an X-ray imaging device according to some embodiments of the present application.
  • FIG. 12b is a comparative schematic diagram of modeling methods of X-ray imaging equipment according to some embodiments of the present application.
  • FIG. 13 is a schematic diagram of flux in a modeling method of an X-ray imaging device according to some embodiments of the present application.
  • Fig. 14 is an exemplary flowchart of a modeling method of an X-ray imaging device according to some embodiments of the present application.
  • FIG. 15 is an exemplary flowchart of a modeling method of an X-ray imaging device according to some embodiments of the present application.
  • Fig. 16 is an exemplary block diagram of a modeling device of an X-ray imaging device according to some embodiments of the present application.
  • Fig. 17 is an exemplary block diagram of a device according to some embodiments of the present application.
  • system is a method for distinguishing different components, elements, parts, parts, or assemblies of different levels.
  • the words can be replaced by other expressions.
  • Fig. 1 is a schematic diagram of an application scenario of an exemplary X-ray imaging device modeling apparatus according to some embodiments of the present application.
  • the modeling apparatus 100 of an X-ray imaging device may include an X-ray imaging device 110, a network 120, a terminal 130, a processing device 140 and a storage device 150.
  • the X-ray imaging device 110 may be used for X-ray imaging devices.
  • the X-ray imaging device 110 may include a digital radiography (DR) device, a computer tomography (Computed Tomography, CT) device, and a cone beam projection computer tomography (Cone Beam Computer) device. Tomography, CBCT) equipment, breast machine, etc.
  • the X-ray imaging apparatus 110 may include an X-ray source and a detector. During the imaging process, the scanned object is located between the X-ray source and the detector, the X-ray source emits X-rays to the scanned object, and the detector converts the X-rays penetrating the scanned object into an X-ray image.
  • the attenuation degree is different, and the information carried by X-rays that penetrate the scanning object when they reach the detector is also different, so The X-ray image converted by the detector will also show a difference, so as to facilitate the identification of the position, shape and size of the lesion in the breast.
  • the X-ray imaging device 110 may be a mammography machine.
  • the mammography machine can have 21 voltage options (for example, 20-40KV) and two kinds of filtration (for example, rhodium filtration and silver filtration), and can generate 42 energy spectra. If you want to calculate and analyze the dose distribution and images produced by the mammography machine, you need to model each phantom under each energy spectrum. If the X-ray imaging device 110 is modeled based on the Monte Carlo (ie Monte Carlo) software, under the condition of fixed voltage and filtering, if the calculated images of each phantom generated by the Monte Carlo software and the corresponding measured images If they are consistent, it is considered that the current Monte Carlo modeling scheme is correct.
  • Monte Carlo Monte Carlo
  • the modeling device 100 of the X-ray imaging device can be used to model the mammography machine, so as to improve the efficiency and accuracy of modeling the mammography machine.
  • the network 120 may include any suitable network capable of facilitating the exchange of information and/or data of the X-ray imaging equipment modeling apparatus 100, and may also be a hospital network HIS (Hospital Information System) or PACS (Picture archiving and communication systems) or other Part of the hospital network or connected to it despite being independent of HIS or PACS or other hospital networks.
  • HIS Healthcare Information System
  • PACS Picture archiving and communication systems
  • one or more components of the X-ray imaging device modeling apparatus 100 can communicate with the X-ray imaging device via the network 120. Information and/or data are exchanged between one or more components of the modeling apparatus 100 of the equipment.
  • the processing device 140 may obtain actual measurement images from the X-ray imaging device 110 through the network 120.
  • the network 120 may include a public network (such as the Internet), a private network (such as a local area network (LAN), a wide area network (WAN), etc.), a wired network (such as an Ethernet), a wireless network (such as an 802.11 network, a wireless Wi-Fi network) Etc.), cellular network (for example, Long Term Evolution (LTE) network), frame relay network, virtual private network (VPN), satellite network, telephone network, router, hub, server computer, etc., one or several combinations.
  • LTE Long Term Evolution
  • VPN virtual private network
  • network 120 may include wired networks, fiber optic networks, telecommunications networks, local area networks, wireless local area network (WLAN), metropolitan area network (MAN), public switched telephone network (PSTN), Bluetooth (TM) network, the ZigBee TM network, a near field communication ( NFC) network and other one or a combination of several.
  • the network 120 may include one or more network access points.
  • the network 120 may include wired and/or wireless network access points, such as base stations and/or Internet exchange points, through which one or more components of the X-ray imaging equipment modeling apparatus 100 can be connected to the network 120 to exchange data and/or information.
  • the terminal 130 may include a mobile device 131, a tablet computer 132, a notebook computer 133, etc., or any combination thereof.
  • the terminal 130 may interact with other components in the modeling apparatus 100 of the X-ray imaging device through a network.
  • the terminal 130 may send one or more control instructions to the X-ray imaging device 110 to control the X-ray imaging device 110 to scan according to the instructions.
  • the terminal 130 may receive the processing result of the processing device 140.
  • the terminal 130 may receive a model of an X-ray imaging device.
  • the terminal 130 may receive the calculated simulation image of the object to be simulated based on the multi-energy dose nucleus corresponding to the thickness of the object to be simulated, the flux distribution, and the adjusted conversion relationship.
  • the mobile device 131 may include a smart home device, a wearable device, a mobile device, a virtual reality device, an augmented reality device, etc., or any combination thereof.
  • the smart home device may include a smart lighting device, a smart electrical appliance control device, a smart monitoring device, a smart TV, a smart camera, a walkie-talkie, etc., or any combination thereof.
  • the wearable device may include bracelets, footwear, glasses, helmets, watches, clothes, backpacks, smart accessories, etc., or any combination thereof.
  • the mobile device may include a mobile phone, a personal digital assistant (PDA), a game device, a navigation device, a POS device, a notebook computer, a tablet computer, a desktop computer, etc., or any combination thereof.
  • the virtual reality device and/or the augmented reality device may include a virtual reality helmet, virtual reality glasses, virtual reality patch, augmented reality helmet, augmented reality glasses, augmented reality patch, etc., or any combination thereof.
  • the virtual reality device and/or augmented reality device may include Google Glass (TM) , Oculus Rift (TM) , HoloLens (TM) or Gear VR (TM), etc.
  • the terminal 130 may be part of the processing device 140.
  • the processing device 140 may process data and/or information obtained from the X-ray imaging device 110, the terminal 130, and/or the storage device 150. For example, the processing device 140 may obtain the performance parameters of the X-ray imaging device 110 from the X-ray imaging device 110. For another example, the processing device 140 may obtain the initial energy spectrum and the initial conversion function from the storage device 150. For another example, the processing device 140 may obtain the tested images of multiple phantoms with different thicknesses with a grid from the X-ray imaging device 110. For another example, the processing device 140 may obtain the simulated flux distribution and energy spectrum of the X-ray imaging device 110 without the grid from the storage device 150. In some embodiments, the processing device 140 may include a single server or a group of servers.
  • the server group can be centralized or distributed.
  • the processing device 140 may be local or remote.
  • the processing device 140 may access information and/or data from the X-ray imaging device 110, the terminal 130, and/or the storage device 150 through the network 120.
  • the processing device 140 may be directly connected to the X-ray imaging device 110, the terminal 130, and/or the storage device 150 to access information and/or data.
  • the processing device 140 may be implemented on a cloud platform.
  • a cloud platform may include one or a combination of private clouds, public clouds, hybrid clouds, community clouds, distributed clouds, cross-clouds, and multi-clouds.
  • the storage device 150 can store data (for example, the initial energy spectrum, the first initial conversion matrix, the performance parameters of the X-ray imaging device, the simulated flux distribution and energy spectrum of the X-ray imaging device without the grid, and the tested Measured images of multiple phantoms with different thicknesses with a grid, etc.), instructions and/or any other information.
  • the storage device 150 may store data obtained from the X-ray imaging device 110, the terminal 130, and/or the processing device 140.
  • the storage device 150 may store the actual measurement of the scanned object obtained from the X-ray imaging device 110. image.
  • the storage device 150 may store data and/or instructions executed or used by the processing device 140 to perform the exemplary methods described in this application.
  • the storage device 150 may store the conversion relationship adjusted according to the comparison result.
  • the storage device 150 may also store an optimal energy spectrum determined from a plurality of initial energy spectra, and an optimal conversion function determined from a plurality of initial conversion functions.
  • the storage device 150 may include one or a combination of a mass memory, a removable memory, a volatile read-write memory, a read-only memory (ROM), and the like.
  • Mass storage can include magnetic disks, optical disks, solid state drives, and mobile storage.
  • Removable storage may include flash drives, floppy disks, optical disks, memory cards, ZIP disks, tapes, and so on.
  • Volatile read-write memory may include random access memory (RAM).
  • RAM can include dynamic random access memory (DRAM), double data rate synchronous dynamic random access memory (DDR-SDRAM), static random access memory (SRAM), thyristor random access memory (T-RAM), zero capacitance random access memory Access memory (Z-RAM), etc.
  • ROM can include mask read-only memory (MROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), optical disk read-only memory Storage (CD-ROM), digital versatile disc, etc.
  • the storage device 150 may be implemented by the cloud platform described in this application.
  • a cloud platform may include one or a combination of private clouds, public clouds, hybrid clouds, community clouds, distributed clouds, cross-clouds, and multi-clouds.
  • the storage device 150 may be connected to the network 120 to implement communication with one or more components (for example, the processing device 140, the terminal 130, etc.) in the modeling apparatus 100 of the X-ray imaging device.
  • One or more components in the modeling apparatus 100 of the X-ray imaging device can read data or instructions in the storage device 150 through the network 120.
  • the storage device 150 may be a part of the processing device 140, or may be independent and directly or indirectly connected to the processing device 140.
  • the storage device 150 may be a data storage device including a cloud computing platform, such as a public cloud, a private cloud, a community, and a hybrid cloud.
  • a cloud computing platform such as a public cloud, a private cloud, a community, and a hybrid cloud.
  • X-ray imaging equipment includes direct digital flat panel X-ray imaging system (DR, Digital Radiography), computer tomography (CT, Computed Tomography), cone beam CT (CBCT, Cone beam CT), and so on.
  • DR Digital Radiography
  • CT Computed Tomography
  • CBCT cone beam CT
  • the photon flux distribution of X-rays generated by the X-ray source, the dose distribution of X-rays in the scanned object, the X-ray image of the scanned object, etc. can be obtained, so as to analyze the X-ray imaging equipment , In order to optimize it, which is beneficial to reduce the radiation to the scanned object and improve the quality of X-ray images.
  • the X-ray imaging device may include an X-ray source and a detector. During the imaging process, the scanned object is located between the X-ray source and the detector, the X-ray source emits X-rays to the scanned object, and the detector converts the X-rays penetrating the scanned object into an X-ray image.
  • the attenuation degree is different, and the information carried by X-rays that penetrate the scanning object when they reach the detector is also different, so The X-ray image converted by the detector will also show a difference, so as to facilitate the identification of the position, shape and size of the lesion in the breast.
  • Fig. 2 is an exemplary flowchart of a modeling method of an X-ray imaging device according to some embodiments of the present application.
  • the modeling method 200 of the X-ray imaging device may be executed by the modeling device 100 (such as the processing device 140) of the X-ray imaging device.
  • the modeling method 200 of an X-ray imaging device may be stored in a storage device (such as the storage device 150) in the form of a program or instruction.
  • the modeling device 100 such as the processing device 140
  • the modeling method 200 of the X-ray imaging device executes the program or When instructed, the modeling method 200 of the X-ray imaging device can be implemented.
  • the operation schematic diagram of the modeling method 200 of the X-ray imaging apparatus presented below is illustrative. In some embodiments, one or more undescribed additional operations and/or one or more undiscussed operations may be utilized to complete the process. In addition, the order of operations of the process 200 shown in FIG. 2 and described below is not intended to be limiting.
  • Step 210 Obtain the optimal energy spectrum of the X-ray emitted by the X-ray source.
  • the optimal energy spectrum can be related to the performance parameters of the X-ray imaging equipment, and the X-ray source model established according to the optimal energy spectrum can well reflect the characteristics of the X-ray imaging equipment, thereby facilitating the analysis of the X-ray imaging equipment.
  • the X-ray source can be simulated based on the Monte Carlo algorithm or the optimal energy spectrum of the X-ray emitted by the X-ray source can be directly obtained based on the test of the X-ray imaging device.
  • the optimal energy spectrum of the X-ray emitted by the X-ray source can also be obtained indirectly through calculation.
  • the optimal energy spectrum may be the true energy spectrum of a theoretically existing X-ray imaging device.
  • the optimal energy spectrum can be calculated by mathematical methods.
  • Step 220 Obtain the optimal conversion function of the detector to X-rays.
  • the optimal conversion function of the detector is obtained.
  • the optimal conversion function can represent the conversion relationship in which the detector directly converts the X-rays emitted by the X-ray source into the X-ray image of the scanned object.
  • the conversion function can be related to the scanned object, specifically the thickness of the scanned part of the scanned object, Therefore, the detector model established according to the optimal transfer function can well reflect the characteristics of the scanned object, which is beneficial to the analysis of the scanned object; the optimal transfer function can also indicate that the detector will penetrate the scanned object X-rays are converted to X-ray images of the scanned object, or the dose distribution of X-rays emitted by the X-ray source in the scanned object is converted to the X-ray image of the scanned object.
  • the conversion function can be compared with the X-ray
  • the performance parameters of the imaging equipment are related, and the detector is modeled according to the optimal conversion function.
  • the detector model can well reflect the characteristics of the X-ray imaging equipment, thereby facilitating the analysis of the X-ray imaging equipment.
  • step 230 the X-ray source is modeled according to the optimal energy spectrum, and the detector is modeled according to the optimal conversion function to obtain a model of the X-ray imaging device.
  • the X-ray source model can be obtained after modeling the X-ray source according to the optimal energy spectrum
  • the detector model can be obtained after modeling the detector according to the optimal conversion function, so that a complete X-ray imaging device can be obtained Model.
  • the foregoing X-ray imaging device modeling method 200 models the X-ray source according to the obtained optimal energy spectrum of the X-ray emitted by the X-ray source, and detects the X-ray according to the obtained optimal conversion function of the detector to the X-ray.
  • the model of the X-ray imaging device is obtained by modeling the X-ray imaging device. According to the model of the X-ray imaging device, an X-ray image that is closer to the measured image can be obtained (in some embodiments, the X-ray image may be a calculated image), This facilitates the analysis of X-ray imaging equipment.
  • the efficiency of traditional Monka software to simulate X-ray imaging equipment is very low, and because the image generated by the detector is related to the energy level and energy of the photons received by each detector crystal, it is not a completely linear relationship, such as a detection
  • the device crystal receives 10 20KeV photons and 20 10KeV photons. Although the total energy received is 200KeV, the final image gray level is somewhat different. Therefore, the traditional Monte Carlo software method is simulating X X-ray imaging equipment needs to simulate the X-ray source and detector respectively.
  • the above modeling method adopts the optimal conversion function to express the conversion relationship between the X-ray or X-ray dose distribution in the scanned object and the X-ray image, instead of the traditional Monte software simulation method
  • the simulation part of the detector reduces the part of the simulation using the Monka software, so that the modeling efficiency is improved.
  • the photons received by the detector of the X-ray imaging device will first penetrate the wire grid of the X-ray imaging device. Therefore, when modeling the X-ray imaging device, except for the entire X-ray imaging device In addition to the modeling of the radiographic imaging equipment, the detector and the wire grid in the X-ray imaging equipment can also be modeled. For further instructions on modeling the detector and the grid of the X-ray imaging equipment, please refer to this application Figure 11 and related descriptions.
  • Fig. 3 is an exemplary flowchart of a modeling method of an X-ray imaging device according to some embodiments of the present application. As shown in FIG. 3, the modeling method of the X-ray imaging device specifically includes the following steps. Wherein, step 210 and step 220 in the embodiment of FIG. 3 may both include step 310 to step 360.
  • Step 310 Obtain an initial energy spectrum and a first initial conversion matrix.
  • the initial energy spectrum and the first initial conversion matrix may be configured according to the performance parameters of the X-ray imaging device.
  • the X-ray source may include a tube and additional filters.
  • the performance parameters include the voltage range between the cathode and anode of the bulb and the material of the additional filter.
  • the initial energy spectrum of the X-ray emitted by the X-ray source and the first initial conversion matrix of the detector can be configured according to the voltage range between the cathode and the anode of the tube and the additional filter material.
  • the anode In X-ray sources, the anode is usually made of high atomic number metal target materials (such as molybdenum, tungsten, etc.).
  • the cathode has a filament made of tungsten and other materials.
  • the filament of the cathode is heated to release electrons, and the cathode and anode A high-voltage electric field is applied between to accelerate the electrons released by the cathode, and the accelerated electrons bombard the metal target surface of the anode to generate X-rays.
  • the voltage range between the cathode and the anode of the bulb refers to the voltage of the high-voltage electric field between the cathode and the anode.
  • the voltage range between the cathode and the anode of the tube is roughly divided into 21 voltage ranges between 20KV and 40KV, and different voltage ranges can generate different X-rays.
  • the additional filter located between the tube and the scanning object is used to filter out some unnecessary X-rays from the X-rays emitted by the tube.
  • Additional filters of different materials can filter out different X-rays, thereby reducing the need for patients X-ray radiation absorbed dose.
  • rhodium filtration and silver filtration can be used, and different additional filter materials can obtain different X-rays.
  • the performance parameters of the X-ray imaging system can be various combinations of these 21 voltage levels and two kinds of filtering, and configure a corresponding for each combination.
  • the initial energy spectrum and the corresponding first initial conversion matrix can be various combinations of these 21 voltage levels and two kinds of filtering, and configure a corresponding for each combination.
  • the initial energy spectrum and the corresponding first initial conversion matrix can be various combinations of these 21 voltage levels and two kinds of filtering, and configure a corresponding for each combination.
  • the initial energy spectrum and the corresponding first initial conversion matrix because some special-shaped additional filters can also generate X-rays with specific spectra, they can match the absorption spectra of the scanning part of the scanned object to a certain extent, thereby selectively increasing the scanning object's
  • the contrast intensity of each part in the scanning part therefore, the corresponding initial energy spectrum and the corresponding first initial conversion matrix can also be configured according to the voltage range between the cathode and the anode of the bulb and the shape of the additional filter.
  • Step 320 Obtain a single energy dose core.
  • the Monte Carlo algorithm can be used to simulate multiple different single-energy dose nuclei, which is more efficient than directly simulating the entire X-ray source.
  • Monte Carlo software such as EGSnrc and Geant4 can be used for simulation, or the Monte Carlo algorithm developed by oneself can be used for simulation.
  • the X-ray of a single energy dose nuclear is a single energy X-ray.
  • step 330 based on the initial energy spectrum, a plurality of different single-energy dose nuclei are spliced into a multi-energy dose nucleus.
  • the initial energy spectrum is the energy spectrum of the X-rays emitted by the X-ray source, and the X-rays emitted by the X-ray source are multi-energy X-rays, that is, the X-rays of the multi-energy dose nuclear. Therefore, the initial energy spectrum is also the multi-energy dose nuclear The energy spectrum of the X-ray. Using Monte Carlo algorithm to simulate the energy spectrum of single energy X-rays obtained by simulating multiple different single energy dose nuclei as the initial value, based on the initial energy spectrum, multiple different single energy dose nuclei are combined to obtain a multi-energy dose nucleus.
  • Step 340 Obtain the dose distribution of the X-ray of the multi-energy dose nucleus in the scanned object.
  • the X-ray dose distribution of the multi-energy dose nucleus in the scanned object is related to the X-ray emitted by the X-ray source, that is, the X-ray of the multi-energy dose nucleus is related to the scanned object. It can be calculated according to the characteristics of the multi-energy dose nucleus and the scanned object.
  • the X-ray dose distribution of the multi-energy dose nuclear in the scanned object for further description of the X-ray dose distribution in the scanned object for obtaining the multi-energy dose nucleus, please refer to Figure 4 of this application and its related descriptions.
  • Step 350 Obtain an X-ray image of the scanned object according to the first initial conversion matrix and the dose distribution.
  • the first initial conversion matrix is multiplied by the X-ray dose distribution of the multi-energy dose nucleus in the scanning object, and the X-ray image of the scanning object is calculated.
  • Fig. 6a for a schematic diagram of the first initial conversion matrix
  • Fig. 6b for the fitting effect of the first initial conversion matrix on the X-ray dose distribution of the multi-energy dose nucleus in the scanning object.
  • Step 360 Obtain an actual measurement image, and find the X-ray image closest to the actual measurement image, so as to determine the optimal energy spectrum among the multiple initial energy spectra and the optimal conversion matrix from the first initial conversion matrix.
  • an X-ray imaging device can be used to scan and image a phantom that simulates the scanning part of the scanned object to obtain a measured image, or a scanned image that meets the conditions can be found in the patient's medical record as the measured image.
  • the mold body can be a polymethyl methacrylate (PMMA) mold body, a uniform water mold, etc., and the shape of the mold body can be set according to actual needs.
  • PMMA polymethyl methacrylate
  • the X-ray imaging device is a mammography machine
  • a cylindrical phantom, a rectangular parallelepiped phantom, a hemispherical phantom, etc. can be used.
  • the performance parameters of each X-ray imaging device can be correspondingly configured with multiple initial energy spectra and multiple first initial conversion matrices.
  • the initial energy spectra and the first initial conversion matrices can be obtained based on experience, and combined according to these initial energy spectra.
  • the initial conversion function obtains multiple X-ray images, find the one that is closest to the measured image among these X-ray images, and use the initial energy spectrum corresponding to the X-ray image as the optimal energy spectrum and the first X-ray image corresponding to the X-ray image.
  • the initial conversion matrix is used as the optimal conversion matrix.
  • the optimal conversion function represents the conversion relationship for the detector to convert the dose distribution into the X-ray image
  • the optimal conversion matrix is the coefficient in the optimal conversion function.
  • the dose distribution may be converted into a gray scale in the X-ray image, so that different parts of the scanned part of the scanned object are presented with different image gray values for distinction.
  • the optimal conversion function may be the process of converting the dose into grayscale in the modeled detector, in conjunction with the optimal energy spectrum, and using an optimization algorithm for optimization.
  • the calculated image is closest to the measured image
  • the energy spectrum of the time In some embodiments, when the calculated image is closest to the measured image, the energy spectrum and the transfer function at this time are "optimal".
  • the phantom includes 0, 1, 4, and 7cm phantoms.
  • the X-ray image (that is, the calculated image) of the 0cm phantom (ie, the calculated image) and the relative error of the pixel value of the corresponding position of the measured image are compared, and the relative error of each pixel value is compared
  • the sum is used as the pixel difference of the 0cm phantom, and based on the sum of the relative errors of each pixel value, it is judged whether the error between the X-ray image and the measured image is less than or equal to the threshold; for example, the 1, 4, and 7cm phantom and the measured value are obtained respectively Pixel difference value of the image, and judge whether the error between the X-ray image and the measured image is less than or equal to the threshold value according to the sum of the 4 pixel difference values.
  • the correction may also be made based on a machine learning algorithm or the like, so that
  • step 370 the X-ray source is modeled according to the optimal energy spectrum, and the detector is modeled according to the optimal conversion matrix to obtain a model of the X-ray imaging device.
  • the method of splicing single-energy dose nuclei into multi-energy dose nuclei can be used to reduce the Monte Carlo algorithm simulation part in the modeling process, so as to further improve the modeling efficiency; use the optimal conversion matrix to establish the detector model, Simplify the model to facilitate the analysis and calculation of X-ray imaging equipment; and configure an optimal conversion matrix, optimal energy spectrum and flux matrix for each voltage and each filtered combination, so as to improve the accuracy of the fitting;
  • the X-ray imaging equipment model established according to the X-ray imaging equipment modeling method can obtain X-ray images of scanning objects with multiple thicknesses. Consistency means that when the current modeling scheme is correct, multiple adjustment parameters can be considered.
  • Fig. 4 is an exemplary flow chart for obtaining the dose distribution of the X-ray of the multi-energy dose nucleus in the scanned object according to some embodiments of the present application.
  • Step 410 Configure a flux matrix according to the performance parameters of the X-ray imaging device.
  • the performance parameters of the X-ray imaging device may include the voltage gear between the cathode and the anode of the bulb and the material of the additional filter.
  • the corresponding flux matrix is also configured for each voltage range and filtering combination.
  • Step 420 Obtain the initial value of the photon flux distribution, and obtain the X-ray photon flux distribution of the multi-energy dose nucleus according to the initial value of the photon flux distribution.
  • the initial value of the photon flux distribution is the X-ray photon flux distribution of each single energy dose nucleus.
  • the photon flux distribution of the X-ray of the single-energy dose nucleus simulated by the Monte Carlo algorithm is used as the initial value of the photon flux distribution
  • the polynomial is used to fit the X-ray photon flux distribution function of the multi-energy dose nucleus, and the polynomials are respectively substituted
  • Several sets of photon flux distribution initial values can be calculated to obtain the coefficients of each monomial in the polynomial, so that the photon flux distribution function can be obtained.
  • a polynomial is used to fit the photon flux distribution of the X-ray of the multi-energy dose nucleus.
  • the polynomial is:
  • p0 is a constant term
  • p1 is the factor of the quadratic term of the variable x
  • p3, p4 and p5 are the factors of the primary, quadratic, and cubic terms of the variable y, respectively.
  • x-p2 The 2 square term takes into account the symmetrical structure of the mammary gland, so the photon flux distribution should also be symmetrical about the central axis
  • p2 is the coordinate of the central axis in the y direction of the photon flux distribution function image.
  • the function image of the X-ray photon flux distribution of the multi-energy dose nucleus is shown in Figure 5. .
  • the term p5*y*y*y in the polynomial can be deleted, so that the polynomial can be simplified.
  • Step 430 According to the attenuation law, the flux matrix, and the photon flux distribution of the X-ray of the multi-energy dose nucleus, obtain the attenuated photon flux distribution after penetrating the scanning object.
  • the attenuation law may be the linear attenuation law adopted when X-rays penetrate the scanning object in the traditional modeling method.
  • the flux matrix is multiplied by the X-ray photon flux distribution of the multi-energy dose nuclear, and then the linear attenuation law is used to calculate the attenuated photon flux distribution after penetrating the scanning object.
  • Step 440 Convolve the attenuated photon flux distribution and the multi-energy dose nucleus to obtain a dose distribution.
  • the attenuated photon flux distribution and the multi-energy dose nucleus are convolved to obtain the X-ray emitted by the X-ray source, that is, the dose distribution of the X-ray of the multi-energy dose nucleus in the scanning object.
  • Fig. 7 is an exemplary flowchart of a modeling method of an X-ray imaging device according to some embodiments of the present application.
  • both step 210 and step 220 may include step 710 to step 760.
  • Step 710 Obtain an initial energy spectrum and an initial conversion function.
  • the initial energy spectrum is configured according to the performance parameters of the X-ray imaging equipment.
  • X-ray source includes bulb and additional filter.
  • the performance parameters include the voltage range between the cathode and anode of the tube and the material of the additional filter. According to the voltage range between the cathode and anode of the tube and the material of the additional filter, the X-ray emitted by the X-ray source can be configured.
  • Initial energy spectrum is configured according to the performance parameters of the X-ray imaging equipment.
  • the initial conversion function represents a conversion relationship in which the detector converts the attenuated X-rays penetrating the scanning object into the X-rays of the scanning object.
  • the process of obtaining the initial transfer function can be similar to the principle of splicing multi-energy dose nuclei with single-energy dose nuclei.
  • Figure 8 of this application and related descriptions please refer to Figure 8 of this application and related descriptions.
  • Step 720 Obtain a single energy dose core.
  • the Monte Carlo algorithm can be used to simulate multiple different single-energy dose nuclei, which is more efficient than directly simulating the entire X-ray source.
  • Monte Carlo software such as EGSnrc and Geant4 can be used for simulation, or the Monte Carlo algorithm developed by oneself can be used for simulation.
  • the X-ray of a single energy dose nuclear is a single energy X-ray.
  • step 730 based on the initial energy spectrum, a plurality of different single-energy dose nuclei are spliced into a multi-energy dose nucleus.
  • the initial energy spectrum is the energy spectrum of the X-rays emitted by the X-ray source, and the X-rays emitted by the X-ray source are multi-energy X-rays, that is, the X-rays of the multi-energy dose nuclear. Therefore, the initial energy spectrum is also the multi-energy dose nuclear The energy spectrum of the X-ray. Using Monte Carlo algorithm to simulate the energy spectrum of single energy X-rays obtained by simulating multiple different single energy dose nuclei as the initial value, based on the initial energy spectrum, multiple different single energy dose nuclei are combined to obtain a multi-energy dose nucleus.
  • Step 740 Obtain the photon flux distribution of the X-ray of the multi-energy dose nucleus after penetrating the scanning object and attenuating.
  • Step 750 Obtain an X-ray image of the scanned object according to the initial transfer function and the attenuated photon flux distribution.
  • a method similar to that in step 420 may be used to calculate the photon flux distribution of the X-ray of the multi-energy dose nucleus before attenuation, and use the attenuation law to calculate the photon flux distribution of the X-ray of the multi-energy dose nucleus after attenuation.
  • the optimal conversion function and the initial conversion function both represent the gray-scale conversion relationship of the X-ray that penetrates the multi-energy dose nucleus after the attenuation of the scanning object is converted into the X-ray image by the detector. According to the initial transfer function and the attenuated photon flux distribution, the X-ray image of the scanned object is calculated.
  • Step 760 Obtain the measured image, and find the X-ray image closest to the measured image, so as to determine the optimal energy spectrum among the multiple initial energy spectra and the optimal transformation function from the multiple initial conversion functions.
  • an X-ray imaging device can be used to scan and image a phantom that simulates the scanning part of the scanned object to obtain a measured image, or a scanned image that meets the conditions can be found in the patient's medical record as the measured image.
  • the mold body can be a polymethyl methacrylate (PMMA) mold body, a uniform water mold, etc., and the shape of the mold body can be set according to actual needs.
  • PMMA polymethyl methacrylate
  • the X-ray imaging device is a mammography machine
  • a cylindrical phantom, a rectangular parallelepiped phantom, a hemispherical phantom, etc. can be used.
  • the performance parameters of each X-ray imaging device can be configured with multiple initial energy spectra.
  • multiple first initial conversion matrices are obtained, and multiple X-rays are obtained according to these initial energy spectra and initial conversion functions.
  • For ray images find the one that is closest to the measured image among these X-ray images, and use the initial energy spectrum corresponding to the X-ray image as the optimal energy spectrum and the initial conversion function corresponding to the X-ray image as the optimal conversion function.
  • the similarity between the measured image and the X-ray image can be calculated, and the X-ray image with the highest similarity is used as the X-ray image closest to the measured image.
  • the similarity between the measured image and the X-ray image may be determined based on the pixel value of the corresponding position between the measured image and the X-ray image. In some embodiments, if the similarity between the X-ray image exceeding the preset ratio (for example, 80%, 85%, etc.) and the measured image is lower than the preset threshold (for example, 50%, etc.), it can also be adjusted Attenuation law (ie, attenuation ratio).
  • step 770 the X-ray source is modeled according to the optimal energy spectrum, and the detector is modeled according to the optimal conversion function to obtain a model of the X-ray imaging device.
  • Fig. 8 is an exemplary flow chart of obtaining an initial conversion function according to some embodiments of the present application. Obtaining the initial conversion function may include step 810 to step 830.
  • Step 810 Obtain the initial value of the conversion function of the detector for each single-energy photon according to the blueprint of the detector.
  • the initial value of the conversion function of each single-energy photon that penetrates the scanning object and is converted into an X-ray image by the detector after reaching the detector is obtained.
  • the initial value of the conversion function represents the conversion relationship between each single-energy photon and the X-ray image after penetrating the scanned object.
  • Step 820 Obtain the attenuated X-ray energy spectrum after penetrating the scanning object.
  • the attenuated X-ray energy spectrum is the energy spectrum of a part of the attenuated X-ray that the X-ray of the multi-energy dose nucleus passes through the scanning object and is absorbed by the scanning object.
  • Obtaining the attenuated X-ray energy spectrum after penetrating the scanning object can be obtained according to the energy spectrum of the X-ray emitted by the X-ray source, that is, the X-ray energy spectrum of the multi-energy dose nuclear and the attenuation law of the X-ray passing through the scanning object, for example, the initial energy spectrum can be used
  • the attenuated X-ray energy spectrum is calculated according to the attenuation law.
  • Step 830 based on the attenuated X-ray energy spectrum, splice multiple single-energy photons into X-rays of the multi-energy dose nucleus, and obtain the initial transfer function according to the initial value of the transfer function.
  • the attenuated X-ray energy spectrum that is, the energy spectrum of the attenuated multi-energy dose nucleus
  • multiple different single energy photons are combined to obtain the attenuated multi-energy dose nuclear attenuated multi-energy X-ray, according to
  • the initial value of the transfer function of these different single-energy photons is the initial transfer function.
  • Fig. 9 is an exemplary flowchart of a modeling method of an X-ray imaging device according to some embodiments of the present application.
  • step 210 may include step 910
  • step 220 may include step 920 to step 940.
  • Step 910 Use the Monte Carlo algorithm to simulate the X-ray source to obtain the optimal energy spectrum.
  • the Monte Carlo algorithm is used to simulate the X-ray source to obtain the optimal energy spectrum.
  • the X-ray source of the X-ray imaging device can also be used for experiments to obtain the optimal energy spectrum.
  • Step 920 Obtain a second initial conversion matrix of the scanning object of each thickness.
  • the second initial conversion matrix can directly convert the X-rays emitted by the X-ray source into the X-ray image of the scanning object.
  • the second initial conversion matrix of the scanned object of each thickness can be set based on experience. For example, when the X-ray imaging device is a mammography machine, the thickness of the PMMA phantom is divided into eight thickness levels from 0 cm to 7 cm, and a corresponding second initial conversion matrix is configured for each thickness of the PMMA phantom. Where 0cm represents the aerial image.
  • step 930 the X-ray source is modeled according to the optimal energy spectrum, and the detector is modeled according to the second initial conversion matrix to obtain an X-ray image of the scanned object.
  • the optimal energy spectrum since the optimal energy spectrum has been determined, modeling the X-ray source according to the optimal energy spectrum can obtain a more accurate X-ray model. It is only necessary to determine the optimal conversion function required to establish the detector model.
  • the optimal conversion function represents the conversion relationship of the detector to convert the X-rays emitted by the X-ray source into the X-ray image of the scanning object
  • the optimal conversion matrix is the coefficient in the optimal conversion function, so only the optimal conversion function needs to be determined.
  • the optimal conversion matrix is sufficient.
  • the X-ray source model established by the optimal energy spectrum and the detector model established by the second conversion matrix can obtain the X-ray image of the scanned object.
  • Step 940 Obtain the measured image, and find the X-ray image closest to the measured image, so as to determine the optimal transformation matrix corresponding to each scanning object of different thickness in the second initial transformation matrix.
  • an X-ray imaging device can be used to scan and image a phantom that simulates the scanning part of the scanned object to obtain a measured image, or a scanned image that meets the conditions can be found in the patient's medical record as the measured image.
  • the phantom can be PMMA phantom, uniform water mold, etc.
  • the shape of the phantom can be set according to actual needs. For example, when the X-ray imaging device is a mammography machine, a cylindrical phantom, a rectangular parallelepiped phantom, a hemispherical phantom, etc. can be used.
  • Each thickness of the scanning object can be configured with multiple second initial conversion matrices, and the X-ray source model established by the optimal energy spectrum and the detector model established by these second initial conversion matrices can respectively obtain multiple X-ray images. , Find the one closest to the measured image among these X-ray images, and use the second initial conversion matrix corresponding to the X-ray image as the optimal conversion matrix.
  • the second initial conversion matrix is corrected, the correction can be made based on a machine learning algorithm, etc., so that the obtained X-ray image is getting closer and closer to the measured image.
  • step 950 the X-ray source is modeled according to the optimal energy spectrum, and the detector is modeled according to the optimal conversion matrix to obtain a model of the X-ray imaging device.
  • the steps in the foregoing embodiments of the modeling method for X-ray imaging equipment can be combined arbitrarily and reasonably, so as to form more implementations of the modeling method for X-ray imaging equipment.
  • the initial energy spectrum in the modeling method of the X-ray imaging device shown in FIG. 2 is directly replaced with the optimal energy spectrum obtained in the modeling method of the X-ray imaging device shown in FIG. 9. This is just an example here. For example, the rest of the various combinations will not be repeated.
  • This application also provides a method for modeling X-ray images, including:
  • the present application also provides a modeling device for X-ray imaging equipment.
  • the X-ray imaging equipment includes an X-ray source for emitting X-rays and a detector for converting X-rays penetrating a scanning object into X-ray images.
  • the modeling device 1000 of the X-ray imaging device includes an energy spectrum acquisition module 1010, a conversion function acquisition module 1020, and a modeling module 1030.
  • the modeling device 1000 of the X-ray imaging device may be implemented by the modeling device 100 (such as the processing device 140) of the X-ray imaging device shown in FIG. 1.
  • the energy spectrum acquisition module 1010 is used to acquire the optimal energy spectrum of the X-rays emitted by the X-ray source.
  • the conversion function obtaining module 1020 is used to obtain the optimal conversion function of the detector to X-rays.
  • the modeling module 1030 is used for modeling the X-ray source according to the optimal energy spectrum, and modeling the detector according to the optimal conversion function to obtain a model of the X-ray imaging device.
  • the energy spectrum acquisition module 1010, the conversion function acquisition module 1020, and the modeling module 1030 in this embodiment can implement corresponding steps in each embodiment of the foregoing X-ray imaging device modeling method, which will not be repeated here.
  • the present application also provides a computer-readable storage medium having a computer program stored on the computer-readable storage medium, and when the computer program is executed by a processor, the processor can execute the steps of the radiation control repair method in any one of the above-mentioned embodiments.
  • the aforementioned storage medium may be a magnetic disk, an optical disc, a read-only memory (Read-Only Memory, ROM), or a random access memory (Random Access Memory, RAM), etc.
  • one or more methods or modules shown in FIGS. 2-10 can be combined with one or more methods or modules shown in FIGS. 11-17 to better improve the construction of X-ray imaging equipment. Mode efficiency.
  • the detector in the X-ray imaging device can generate a measured image.
  • the pixel value of the measured image is related to the energy level and energy of the photons received by each detector crystal, but it is not a completely linear relationship. For example, if a detector crystal receives 10 20KeV photons or 20 10KeV photons, although the total energy of the received photons is 200KeV, the pixel values of the measured images generated by the two are different.
  • the photons received by the detector will first penetrate the wire grid in the X-ray imaging equipment. Therefore, when modeling the X-ray imaging equipment, in addition to modeling the entire X-ray imaging equipment, it is also necessary Model the detectors and grids in X-ray imaging equipment.
  • Fig. 11 is an exemplary flowchart of a modeling method of an X-ray imaging device according to some embodiments of the present application.
  • the modeling method 1100 of an X-ray imaging device may be suitable for the case of modeling an X-ray imaging device, and is particularly suitable for a case of modeling a detector and a wire grid in the X-ray imaging device.
  • the method may be executed by the modeling apparatus of the X-ray imaging equipment provided in the embodiments of the present application, and the apparatus may be implemented by software and/or hardware, and the apparatus may be integrated on various user terminals or servers.
  • the modeling method 1100 of the X-ray imaging device may be executed by the modeling device 100 (such as the processing device 140) of the X-ray imaging device.
  • the modeling method 1100 of X-ray imaging equipment may be stored in a storage device (such as the storage device 150) in the form of a program or instruction.
  • a storage device such as the storage device 150
  • the modeling device 100 such as the processing device 140
  • the modeling method 1100 of the X-ray imaging device can be implemented.
  • the operation schematic diagram of the modeling method 1100 of the X-ray imaging apparatus presented below is illustrative. In some embodiments, one or more undescribed additional operations and/or one or more undiscussed operations may be utilized to complete the process. In addition, the order of operations of the process 1100 shown in FIG. 11 and described below is not limitative.
  • Step 1110 Acquire the simulated flux distribution and energy spectrum of the X-ray imaging device without the grid, and the measured images of multiple phantoms with different thicknesses with grids that have been tested.
  • the flux distribution is the simulation result of the flux distribution of X-rays generated by the X-ray imaging equipment.
  • the X-rays have not been passed through a certain phantom and grid, that is to say, the flux distribution is simulated without a filter.
  • This simulation process can be implemented based on the Monka software, or based on other algorithm modeling, which is not specifically limited here.
  • the Monte Carlo software can be any open source software that can be used to calculate X-ray doses, such as EGSnrc, Geant4, etc., or a self-developed Monte Carlo algorithm, which is not specifically limited here.
  • the meaning and simulation process of the energy spectrum are similar to the flux distribution, so I will not repeat them here.
  • the measured image may be an image of each phantom with a grid actually generated by the detector.
  • the thickness of each phantom is usually different from each other, and the thickness of each phantom is known.
  • the body can be a polymethylmethacrylate (PMMA) phantom or a phantom made of other materials, which is not specifically limited here; the number of measured images for each phantom can be one or more, which is not done here Specific restrictions.
  • the measured image is the image generated by the detector based on X-rays passing through a certain phantom and a grid.
  • the measured image corresponding to the 7cm phantom is based on the detector's The image generated by X-rays penetrating the 7cm phantom and the grid. It should be noted that if a phantom is a 0cm phantom, it means that the measured image is the image generated by the X-ray imaging device after irradiating the air.
  • Step 1120 Simulate the multi-energy dose nucleus of each phantom according to the energy spectrum, and calculate the calculated image of each phantom according to the calculation factors.
  • the calculation factors include flux distribution, each multi-energy dose nucleus and the to-be-adjusted Conversion relationship.
  • the multi-energy dose nucleus of each phantom can be simulated separately according to the obtained energy spectrum, and the convolution result of the flux distribution and the multi-energy dose nucleus can be considered as an image, which can be understood as the attenuation of a certain phantom After the X-ray dose. Since each phantom or each thickness has a corresponding multi-energy dose nucleus, each phantom also has a corresponding convolution result. Regarding the convolution result and the measured image corresponding to a certain phantom, there is usually a difference between the two. This difference can be compensated by a conversion relationship.
  • the number of the conversion relationship can be one or more, for example, each The motifs can correspond to the same conversion relationship, or respectively correspond to different conversion relationships, which are not specifically limited here.
  • the calculated image of each phantom can be simulated according to the flux distribution, the multi-energy dose nuclei and the conversion relationship.
  • the flux distribution and the multi-energy dose nucleus of the phantom can be The convolution result is multiplied by the conversion relationship to obtain the calculated image of the phantom, and the flux distribution, the multi-energy dose nucleus and the conversion relationship can be called calculation factors.
  • the multi-energy dose core can be a multi-energy dose core with a grid device (ie, with a grid) (ie, a multi-energy dose core with a grid), or a multi-energy with a grid-less device (ie, without a grid)
  • the dose core ie, the gridless multi-energy dose core
  • the specific type of the multi-energy dose nucleus will affect the conversion relationship. For example, if the multi-energy dose nucleus has considered the influence of the filter wire grid in the simulation, the convolution result is the convolution of the grid device If the multi-energy dose core does not consider the filter grid in the simulation, the convolution result is the convolution of the gridless device.
  • the calculated image based on the conversion relationship of the grid is the calculated image of the grid device, therefore, there is comparability between the calculated image of the grid device and the measured image of the grid device.
  • the energy spectrum is the energy spectrum directly generated by the simulated X-ray imaging equipment. The energy spectrum will still change when it is irradiated to the detector. Therefore, whether it is a gridded multi-energy dose nucleus or a gridless multi-energy dose nucleus, The corresponding conversion relationship has taken into account the influence of the detector.
  • Step 1130 Compare the differences between each calculated image and each measured image respectively, and determine whether the conversion relationship needs to be adjusted according to the comparison result.
  • the differences between each calculated image and each measured image are respectively compared, and it is determined whether the conversion relationship needs to be adjusted according to the comparison result.
  • the difference between the calculated image and the measured image of each phantom is small enough, and/or, when the sum of the difference between the calculated image and the measured image of each phantom is small enough, it means that at this time
  • the conversion relationship and the multi-energy dose nucleus are correct, they do not need to be adjusted, and the modeling is over.
  • the pixel difference value of the corresponding position between the calculated image and the measured image of the same phantom is calculated, and the comparison result is determined according to the pixel difference value corresponding to each phantom.
  • the phantom includes 0, 1, 4, and 7cm phantoms, compare the relative error of the pixel value of the corresponding position of the calculated image and the measured image of the 0cm phantom, and use the sum of the relative errors of each pixel value as the 0cm phantom Pixel difference; similarly, get the pixel difference of 1, 4, and 7cm phantoms respectively, and judge whether the conversion relationship needs to be adjusted according to the sum of these 4 pixel differences.
  • the adjusted conversion relationship can be obtained. Therefore, in subsequent applications, when the attenuation degree of X-rays (such as simulated X-rays) irradiated on the object to be simulated is obtained, the thickness of the object to be simulated can be obtained according to the degree of attenuation and the adjusted conversion relationship; Based on the multi-energy dose nucleus corresponding to the thickness of the object to be simulated, the flux distribution and the adjusted conversion relationship, the simulated image of the object to be simulated can be calculated.
  • the attenuation degree of X-rays such as simulated X-rays
  • the technical solution of the embodiment of the present application obtains the simulated flux distribution and energy spectrum of the X-ray imaging device without the grid, and the measured images of multiple phantoms with different thicknesses with grids that have been tested. , Can simulate the multi-energy dose nucleus of each phantom according to the energy spectrum, and then calculate the calculated image of each phantom according to the flux distribution, each multi-energy dose nucleus and the conversion relationship, thereby ensuring the simulated calculated image Therefore, by comparing the differences between each calculated image and each measured image separately, it can be judged whether the conversion relationship needs to be adjusted according to the comparison result, so as to obtain the conversion relationship used in conjunction with the multi-energy dose core .
  • the above-mentioned technical solution reduces the difference between the calculated image and the measured image by acquiring the conversion relationship used in conjunction with the multi-energy dose core, and realizes the effect of comprehensive modeling of the grid and the detector, thereby realizing X-ray imaging The effect of high-precision modeling of equipment.
  • judging whether the conversion relationship needs to be adjusted according to the comparison result can include: if the conversion relationship needs to be adjusted according to the comparison result, the conversion relationship can be adjusted according to the comparison result, and according to the adjusted conversion relationship Update the conversion relationship; repeat the steps of separately calculating the calculated image of each phantom according to the calculation factors, until the comparison result meets the preset modeling conditions.
  • the calculation factors will be relatively updated, and the calculated images of each phantom can be recalculated based on the updated calculation factors, and the calculated images and the measured images are compared again.
  • the preset modeling condition may be a certain pixel difference threshold and so on.
  • the initial conversion relationship involved in this cyclic process can be set arbitrarily or based on existing experience, and there must be differences between the calculated image and the actual image obtained based on the initial conversion relationship. It involves the cyclical process of adjusting the conversion relationship based on differences.
  • FIG. 12a and FIG. 12b this is a schematic diagram of the conversion relationship and the modeling result when the comparison result meets the preset modeling condition
  • FIG. 12a is shown according to some embodiments of the present application
  • Figure 12b is a schematic diagram of the comparison between the calculated image and the measured image of the 1, 2, 3... 8, 9 cm phantom (each image is indicated by a line), the abscissa Is the pixel index (1-72, 72 pixel index), and the ordinate is the pixel value.
  • the calculated image and the measured image of each phantom basically overlap each other.
  • the calculated image obtained by the simulation method is very similar to the measured image, and the modeling accuracy of X-ray imaging equipment is relatively high.
  • the above technical solution is a modeling solution for each phantom of a certain energy spectrum. After the energy spectrum modeling is completed, the above modeling method can be executed again to realize each mode of another energy spectrum. The modeling of the body is completed until the modeling of each energy spectrum involved in the X-ray imaging device is completed, at which time the modeling of the X-ray imaging device is completed. Secondly, when there is a difference between the calculated image and the measured image, the above technical solution can reduce this difference by adjusting the conversion relationship, and the adjustment process is relatively simple.
  • the existing technical solutions for implementing modeling based on the Monka software need to analyze which simulation process has an error, such as an error in the simulation of the detector, an error in the simulation of the grid, and both the simulation of the detector and the grid. If errors occur, and then adjust the modeling script based on the analysis results. This adjustment process is very cumbersome and the modeling efficiency is extremely low. The existing solutions can hardly guarantee the modeling accuracy while ensuring the completion of the modeling.
  • the foregoing X-ray imaging device modeling method may further include: adjusting each parameter in the preset flux distribution fitting function according to the comparison result, and according to the adjusted preset flux distribution fitting function The fitting results update the flux distribution.
  • the Monka software can only simulate the main components of the X-ray imaging equipment, it cannot simulate various screws and other scattered parts. Therefore, the Monka software and the actual There are differences in X-ray imaging equipment, which makes it difficult for the flux distribution simulated by the Monka software to be exactly the same as the actual flux distribution. Therefore, similar to the initial conversion relationship, the obtained simulated flux distribution can also be used as the initial flux distribution.
  • the flux distribution can be adjusted according to the difference Make adjustments to make the difference between the calculated image obtained based on the adjusted flux distribution and the measured image smaller and smaller.
  • this adjustment process can be realized by adjusting each parameter in the preset flux distribution fitting function, which can be used to fit the flux distribution according to each parameter, thus, by adjusting each parameter
  • the parameters can realize the adjustment of flux distribution.
  • the reason for this setting is that the coordinated adjustment of the conversion relationship and the flux distribution can improve the modeling accuracy of the X-ray imaging device.
  • This square term is to consider that the structure of the mammary gland is symmetrical, so the photon flux distribution should also be about the center Axisymmetric, p2 is the coordinate of the image center axis (anode axis/cathode axis) in the y direction.
  • P0, p1, p2, p3, p4, and p5 in the preset flux distribution fitting function are parameters that have no meaning; in Figure 4, the curved surface on the left is one of the flux distributions.
  • the orthogonal direction is the orthogonal direction
  • the image plane is the image plane
  • the selected range is the selected range.
  • the preset flux distribution fitting function can be simplified. For example, the item p5*y*y*y can be deleted.
  • the conversion relationship may include a conversion matrix and/or a multi-energy conversion function.
  • the multi-energy conversion function is based on each single-energy photon simulated by the detector in the X-ray imaging device (such as the detector's drawing) It is obtained by splicing (or combining) the single-energy conversion functions irradiated on the detector.
  • the conversion relationship can be a conversion matrix (as shown in FIG. 3a), a multi-energy conversion function, or other presentation forms, which are not specifically limited here.
  • each photon is a single-energy photon
  • multiple single-energy photons can be spliced based on different attenuation ratios to form the energy spectrum of a certain energy level.
  • the single-energy conversion function of each single-energy photon irradiated on the detector can be simulated according to the drawing of the detector in the X-ray imaging device, and then the single-energy conversion functions are stitched together according to the weight of each single-energy photon
  • this multi-energy transfer function can also be understood as a transfer function spliced by the energy spectrum attenuated by the filter grid, and because X-rays penetrate the grid, only one detector can be imaged. Therefore, the multi-energy transfer function simulates the imaging effect of the detector on the X-rays penetrating the grid.
  • the comprehensive modeling of the detector and the grid based on the multi-energy transfer function is a similar process, which will not be repeated here.
  • Fig. 14 is an exemplary flowchart of a modeling method of an X-ray imaging device according to some embodiments of the present application. This embodiment is optimized on the basis of the above-mentioned technical solutions.
  • the multi-energy dose nucleus is a gridless multi-energy dose nucleus
  • the conversion relationship includes a first conversion relationship corresponding to each phantom.
  • the The method may further include: obtaining a target conversion relationship according to each first conversion relationship, where the target conversion relationship is a conversion relationship corresponding to each phantom.
  • the method of this embodiment may specifically include the following steps:
  • Step 1410 Acquire the simulated flux distribution and energy spectrum of the X-ray imaging device without a grid, and measured images of multiple phantoms with different thicknesses with grids that have been tested.
  • Step 1420 Simulate the gridless multi-energy dose nucleus of each phantom according to the energy spectrum, and calculate the calculated image of each phantom according to the calculation factors.
  • the calculation factors include flux distribution and each gridless multi-energy dose nucleus. And the first conversion relationship corresponding to each phantom to be adjusted.
  • the gateless multi-energy dose core is the multi-energy dose core of the gateless device, which does not consider the influence of the grid.
  • the convolution result of the flux distribution and the gateless multi-energy dose core is the gateless The convolution result of the device, and the measured image is the measured image of the gated device. There must be a difference between the two. This difference can be reflected through the first conversion relationship, so as to be based on the convolution result of the gateless device and the considered
  • the first conversion relationship affected by the wire grid obtains the calculated image of the grid device, and at the same time, the first conversion relationship also takes the influence of the detector into consideration.
  • the first conversion relationship is a conversion relationship that takes into account the influence of the grid, and the grid attenuates X-rays in different phantoms, one can be configured for each phantom.
  • the first conversion relationship is to respectively model the comprehensive effects of the grid and the detector under the corresponding phantom based on the multiple first conversion relationships.
  • the initial first conversion relationship can be configured for each phantom after the energy spectrum is simulated based on the Monka software, it can be configured arbitrarily, or it can be configured based on existing experience. , There is no specific limitation here.
  • Step 1430 Compare the differences between each calculated image and each measured image. If it is determined that the conversion relationship needs to be adjusted according to the comparison result, adjust the first conversion relationship according to the comparison result, and update the first conversion according to the adjusted first conversion relationship Relationship: Repeat the steps of calculating the calculated image of each phantom according to the calculation factors, until the comparison result meets the preset modeling conditions.
  • the pixel values of the corresponding positions between the simulated calculated image and the measured image have an attenuation ratio, such as 0.1 times, 10 times, 100 times, etc., and Each phantom or each thickness usually corresponds to a different attenuation ratio.
  • the attenuation ratios of each thickness can be directly combined into the first conversion relationship, and there is no need to calculate them separately.
  • At least one conversion relationship can be adjusted according to the comparison result. If it is determined according to the comparison result that, except for a certain phantom, the difference between the calculated images and the measured images of the remaining phantoms is small , You can adjust only the first conversion relationship corresponding to the phantom; for another example, if the comparison result is the comprehensive modeling result of each phantom, you need to adjust each first conversion relationship; etc., not here Make specific restrictions.
  • Step 1440 Obtain a target conversion relationship according to each first conversion relationship, where the target conversion relationship is a conversion relationship corresponding to each motif.
  • a target conversion relationship corresponding to each phantom can be obtained according to each first conversion relationship.
  • the reason for this setting is: in the application link of the modeling result, the object to be simulated The thickness of is not known. If the first conversion relations exist at the same time, take the thickness of the object to be simulated as 3cm and 5cm as an example. Because the thickness is not known in advance, it is impossible to determine which thickness corresponds to the first conversion relationship to be calculated. 3cm and 5cm. Therefore, at this time, a target conversion relationship that can integrate various thicknesses is needed. Based on the target conversion relationship, the thickness of the object to be simulated is determined.
  • the attenuation degree corresponding to which thickness can be determined, and thus the thickness of the object to be simulated can be determined; further, the corresponding gridless multi-energy dose nucleus can be called based on the thickness, and based on the gridless multi-energy dose nucleus.
  • the energy dose nucleus, flux distribution and target conversion relationship are calculated to calculate the calculated image of the object to be simulated.
  • each first conversion relationship can be directly merged, and the combined result can be averaged to obtain the target conversion relationship; for another example, you can Fit the pixels at the corresponding positions in each first conversion relationship to obtain the target conversion relationship, such as fitting a thickness-related conversion function to the first pixel in each first conversion relationship, and simulating the second pixel Sum up a conversion function related to thickness...and so on. Therefore, the target conversion relationship is composed of N conversion functions, and N is the total number of pixels in the first conversion relationship.
  • the target conversion relationship can also be obtained in other ways, which will not be repeated here.
  • the thickness of the untested phantom is usually known.
  • the untested phantom can be a phantom with a thickness of 2, 3, 5, or 6cm.
  • the second conversion relationship of each untested phantom can be determined according to each first conversion relationship.
  • the second conversion relationship can be obtained by interpolation fitting.
  • the conversion relationship, and further, the target conversion relationship can be obtained through each first conversion relationship and each second conversion relationship.
  • the gridless multi-energy dose nucleus of each untested phantom can also be fitted by interpolation of the gridless multi-energy dose nucleus of each phantom.
  • the technical solution of the embodiment of the present application realizes the high precision of the X-ray imaging equipment by cooperating with the gridless multi-energy dose nucleus and the first conversion relationship corresponding to each phantom which takes into account the influence of the grid and the detector at the same time.
  • Fig. 15 is an exemplary flowchart of a modeling method of an X-ray imaging device according to some embodiments of the present application. This embodiment is optimized on the basis of the above-mentioned technical solutions.
  • the multi-energy dose core is a gridded multi-energy dose core
  • the conversion relationship is a third conversion relationship corresponding to each phantom
  • the calculation factor also includes the attenuation ratio corresponding to each phantom;
  • the conversion relationship is adjusted according to the comparison result, and the conversion relationship is updated according to the adjusted conversion relationship.
  • it may include: adjusting each attenuation ratio according to the comparison result, updating each attenuation ratio according to the adjusted attenuation ratio; adjusting the third conversion relationship according to each attenuation ratio, The third conversion relationship is updated according to the adjusted third conversion relationship; after the comparison result meets the preset modeling condition, it may further include: fitting each attenuation ratio to obtain a fitting result between the attenuation ratio and the thickness.
  • the method of this embodiment may specifically include the following steps:
  • Step 1510 Acquire the simulated flux distribution and energy spectrum of the X-ray imaging device without a grid, and measured images of multiple phantoms with different thicknesses with a grid that have been tested.
  • Step 1520 Simulate the gridded multi-energy dose nucleus of each phantom according to the energy spectrum, and calculate the calculated image of each phantom according to the calculation factors.
  • the calculation factors include flux distribution and each gridded multi-energy dose nucleus. , The attenuation ratio corresponding to each phantom and the third conversion relationship corresponding to each phantom to be adjusted.
  • the multi-energy dose nucleus with grid is the multi-energy dose nucleus of the grid device, which has considered the influence of the grid.
  • the result of the flux distribution and the convolution of the grid multi-energy dose nucleus is the grid
  • the convolution result of the device, and the measured image is the measured image of the device with a grid.
  • the third conversion relationship mainly considers the detector's Influence.
  • the third conversion relationship is a conversion relationship that does not consider the influence of the grid, it can be assumed that the grid does not exist, and a third conversion relationship can fuse the modeling results of the detector in each phantom , That is, a third conversion relationship can merge each phantom or each thickness.
  • the initial third conversion relationship may be configured arbitrarily, or configured based on existing experience, which is not specifically limited here.
  • each phantom has a The difference in the attenuation of X-rays is relatively large and corresponds to different attenuation ratios.
  • the calculation factors also include each The attenuation ratio corresponding to the phantom.
  • the convolution result of the flux distribution and the gridded multi-energy dose nucleus of the phantom can be multiplied by the corresponding attenuation ratio of the phantom, and then multiplied by the third conversion relationship to obtain the phantom.
  • the calculated image of the body It should be noted that the effect of the grid is implied in the attenuation ratio. This is because different phantoms have different levels of attenuation of X-rays, and the resulting image pixels are also different, and they penetrate a certain phantom. X-rays also need to penetrate the grid to be received by the detector.
  • the attenuation degree of the X-rays penetrating the grid is different.
  • the attenuation ratio of the pixel value of the corresponding position between the calculated image and the measured image is also different. Therefore, the effect of the grid is implied in the attenuation ratio.
  • Step 1530 Compare the differences between each calculated image and each measured image. If the conversion relationship needs to be adjusted according to the comparison result, adjust each attenuation ratio according to the comparison result, update each attenuation ratio according to the adjusted attenuation ratio, and Adjust the third conversion relationship according to each attenuation ratio, update the third conversion relationship according to the adjusted third conversion relationship; repeat the steps of calculating the calculated image of each phantom according to the calculation factors, until the comparison result meets the preset modeling conditions .
  • the attenuation ratio and the third conversion relationship need to be adjusted. This is because the attenuation ratios corresponding to each phantom or each thickness are different. If the modeling result of a certain phantom is not ideal, you need to adjust the phantom.
  • the attenuation ratio is adjusted, and the third conversion relationship is a conversion relationship that combines the attenuation ratios. Therefore, a change in the attenuation ratio will bring about a change in the third conversion relationship.
  • an optional solution for adjusting the attenuation ratio and the third conversion relationship according to the comparison result is to set the initial attenuation ratio of each phantom according to the existing experience of the attenuation degree of X-ray irradiation on each phantom.
  • the third conversion relationship can be arbitrarily set, such as a certain unit matrix, multiplying the flux distribution and the convolution result of the multi-energy dose core with a grid by the attenuation ratio of a certain phantom, and then multiplying it by the third conversion relationship, you can get A calculated image of the phantom; further, the sum of the relative errors of the corresponding pixels between the calculated image of each phantom and the measured image is used as the objective function.
  • the value of the objective function is larger, and the attenuation ratio can be adjusted according to the function value.
  • adjust the third conversion relationship based on the adjusted attenuation ratio and the least square method then, re-simulate the calculated image, and recalculate the objective function based on the re-simulated calculated image, and judge whether the value of the objective function is the smallest, and iteratively , Until the function value is the smallest, this shows that the attenuation ratio at this time is correct, and the third conversion relationship is also correct at this time, and the modeling ends.
  • Step 1540 Fit each attenuation ratio to obtain a fitting result between the attenuation ratio and the thickness.
  • each attenuation ratio can be fitted to obtain a fitting result between the attenuation ratio and the thickness.
  • the fitting result can be a fitting function.
  • the reason for this setting It is: in the application link of the modeling results, the thickness of the object to be simulated is not known. If the attenuation ratios exist at the same time, take the thickness of the object to be simulated as 3cm and 5cm as an example, because the thickness is not known in advance and the call cannot be determined. Which thickness corresponds to the attenuation ratio of 3cm and 5cm can be calculated. Therefore, at this time, a fitting result that can fuse each thickness is required.
  • the thickness of the object to be simulated is determined, and then the The attenuation ratio corresponding to the thickness and the grated multi-energy dose nucleus are used to obtain the calculated image of the object to be simulated.
  • the execution order of the attenuation ratio fitting can be executed after the correct attenuation ratio is obtained, or after the initial attenuation ratio is obtained, for example, a fitting between the attenuation ratio and the thickness is obtained according to the initial attenuation ratio
  • the process of adjusting the attenuation ratio is the process of adjusting the fitting function.
  • the technical solution of the embodiment of the present application uses a gridded multi-energy dose nucleus, which implies the influence of the grid or the attenuation ratio caused by the grid, and the third conversion that implies the detector and corresponds to each phantom.
  • the relationship cooperates with each other to realize the effect of high-precision modeling of X-ray imaging equipment.
  • FIG. 16 is an exemplary block diagram of a modeling device of an X-ray imaging device according to some embodiments of the present application.
  • the modeling device 1600 of an X-ray imaging device can be used to execute the X-ray imaging device provided by any of the foregoing embodiments. Modeling method.
  • This device and the X-ray imaging device modeling method of the foregoing embodiments belong to the same application concept.
  • the modeling device 1600 of the X-ray imaging device may include: a data acquisition module 1610, an image calculation module 1620, and an adjustment judgment module 1630.
  • the modeling device 1600 of the X-ray imaging device may be implemented by the modeling device 100 (such as the processing device 140) of the X-ray imaging device shown in FIG. 1.
  • the data acquisition module 1610 is used to acquire the simulated flux distribution and energy spectrum of the X-ray imaging device without the grid, and the tested images of multiple phantoms with different thicknesses with grids. ;
  • the image calculation module 1620 is used to simulate the multi-energy dose nucleus of each phantom according to the energy spectrum, and calculate the calculated image of each phantom according to the calculation factors.
  • the calculation factors include flux distribution and each multi-energy dose nucleus. And the conversion relationship to be adjusted;
  • the adjustment judgment module 1630 is used to compare the differences between each calculated image and each measured image, and determine whether the conversion relationship needs to be adjusted according to the comparison result.
  • the adjustment judgment module 1630 may specifically include:
  • the conversion relationship adjustment unit is configured to adjust the conversion relationship according to the comparison result if it is judged that the conversion relationship needs to be adjusted according to the comparison result, and update the conversion relationship according to the adjusted conversion relationship;
  • the repetitive execution unit is used to repeatedly execute the step of separately calculating the calculated image of each phantom according to the calculation factors, until the comparison result meets the preset modeling condition.
  • the multi-energy dose nucleus is a gridless multi-energy dose nucleus
  • the conversion relationship includes a first conversion relationship corresponding to each phantom.
  • the device may further include:
  • the target conversion relationship obtaining module is used to obtain the target conversion relationship according to each first conversion relationship, where the target conversion relationship is a conversion relationship corresponding to each phantom.
  • the target conversion relationship gets the module, which can be specifically used for:
  • each first conversion relationship respectively determine the second conversion relationship of each untested phantom that has not been tested
  • the target conversion relationship is obtained.
  • the multi-energy dose core is a gridded multi-energy dose core
  • the conversion relationship is a third conversion relationship corresponding to each phantom
  • the calculation factor also includes the proportional attenuation ratio corresponding to each phantom
  • the conversion relationship adjustment unit may specifically include: a proportional attenuation ratio adjustment subunit, which is used to adjust each proportional attenuation ratio according to the comparison result, and update each proportional attenuation ratio according to the adjusted proportional attenuation ratio;
  • the third conversion relationship adjustment subunit is configured to adjust the third conversion relationship according to each proportional attenuation ratio, and update the third conversion relationship according to the adjusted third conversion relationship;
  • the device may further include: a proportional attenuation ratio fitting module for fitting each proportional attenuation ratio to obtain a proportional attenuation ratio fitting result.
  • the device may further include:
  • the flux distribution adjustment unit is used to adjust each parameter in the preset flux distribution fitting function according to the comparison result, and update the flux distribution according to the adjusted fitting result of the preset flux distribution fitting function.
  • the conversion relationship adjustment unit may include:
  • the comparison subunit is used to calculate the pixel difference of the corresponding position between the calculated image and the measured image corresponding to the same phantom, and determine the comparison result according to the pixel difference corresponding to each phantom.
  • the conversion relationship may include a conversion matrix and/or a multi-energy conversion function.
  • the multi-energy conversion function is the single-energy conversion of each single-energy photon irradiated on the detector according to the blueprint of the detector in the X-ray imaging device. The function is spliced together.
  • the above-mentioned modeling device of X-ray imaging equipment may further include:
  • the thickness obtaining module is used to obtain the attenuation degree of X-rays (such as simulated X-rays) irradiated on the object to be simulated, and obtain the thickness of the object to be simulated according to the attenuation degree and the adjusted conversion relationship;
  • the simulation image calculation module is used to calculate the simulation image of the object to be simulated based on the multi-energy dose nucleus corresponding to the thickness of the object to be simulated, the flux distribution and the adjusted conversion relationship.
  • the X-ray imaging device modeling device obtained by the embodiment shown in FIG. 16 of the present application obtains the simulated flux distribution and energy spectrum of the X-ray imaging device without the grid through the data acquisition module, and the tested band Measured images of multiple phantoms with different thicknesses of the grid; the image calculation module can simulate the multi-energy dose nucleus of each phantom according to the energy spectrum, and then according to the flux distribution, each multi-energy dose nucleus and the conversion relationship. Calculate the calculated images of each phantom, thereby ensuring the image accuracy and calculation efficiency of the simulated calculated images; the adjustment judgment module can judge the conversion according to the comparison results by comparing the differences between the calculated images and the measured images.
  • the above-mentioned device can reduce the difference between the calculated image and the measured image by acquiring the conversion relationship used with the multi-energy dose core, and realize the effect of comprehensive modeling of the grid and the detector, thereby realizing the X-ray imaging equipment The effect of high-precision modeling.
  • the X-ray imaging device modeling apparatus provided in the embodiment of the present application can execute the X-ray imaging device modeling method provided in any embodiment of the present application, and has functional modules and beneficial effects corresponding to the execution method.
  • FIG. 17 is a schematic diagram of an exemplary structure of a device according to some embodiments of the present application.
  • the device 1700 includes a memory 1710, a processor 1720, an input device 1730, and an output device 1740.
  • the number of processors 1720 in the device may be one or more.
  • one processor 1720 is taken as an example; the memory 1710, the processor 1720, the input device 1730, and the output device 1740 in the device may be connected by a bus or other means.
  • Figure 17 takes the connection via the bus 1750 as an example.
  • the memory 1710 can be used to store software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the X-ray imaging device modeling method in the embodiment of the present application (for example, X-ray imaging equipment).
  • the processor 1720 executes various functional applications and data processing of the device by running the software programs, instructions, and modules stored in the memory 1710, that is, realizes the above-mentioned modeling method of the X-ray imaging device.
  • the memory 1710 may mainly include a program storage area and a data storage area.
  • the program storage area may store an operating system and an application program required by at least one function; the data storage area may store data created according to the use of the device, and the like.
  • the memory 1710 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices.
  • the memory 1710 may further include a memory remotely provided with respect to the processor 1720, and these remote memories may be connected to the device through a network. Examples of the aforementioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.
  • the input device 1730 can be used to receive input numeric or character information, and to generate key signal input related to user settings and function control of the device.
  • the output device 1740 may include a display device such as a display screen.
  • the present application provides a storage medium containing computer-executable instructions, when the computer-executable instructions are executed by a computer processor, a method for modeling an X-ray imaging device is performed, the method including:
  • the calculation factors include the flux distribution, each multi-energy dose nucleus and the conversion relationship to be adjusted;
  • a storage medium containing computer-executable instructions provided by an embodiment of the present application is not limited to the method operations described above, and can also execute the X-ray imaging equipment provided by any embodiment of the present application. Related operations in the modeling method.
  • ROM Read-Only Memory
  • RAM Random Access Memory
  • FLASH Flash memory
  • hard disk or optical disk etc., including several instructions to make a computer device (which can be a personal computer) , A server, or a network device, etc.) execute the method described in each embodiment of the present application.
  • this application uses specific words to describe the embodiments of this application.
  • “one embodiment”, “an embodiment”, and/or “some embodiments” mean a certain feature, structure, or characteristic related to at least one embodiment of the present application. Therefore, it should be emphasized and noted that “one embodiment” or “one embodiment” or “an alternative embodiment” mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. .
  • certain features, structures, or characteristics in one or more embodiments of the present application can be appropriately combined.
  • a computer storage medium may contain a propagated data signal containing a computer program code, for example on a baseband or as part of a carrier wave.
  • the propagated signal may have multiple manifestations, including electromagnetic forms, optical forms, etc., or suitable combinations.
  • the computer storage medium may be any computer readable medium other than the computer readable storage medium, and the medium may be connected to an instruction execution system, device, or device to realize communication, propagation, or transmission of the program for use.
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Abstract

本申请实施例公开了一种X射线成像设备的建模方法和装置。其中，一种X射线成像设备的建模方法，X射线成像设备包括用于发出X射线的X射线源以及用于将穿透扫描对象的X射线转换为X射线图像的探测器，建模方法包括：获取X射线源发出的X射线的最优能谱：获取探测器的最优转换函数：以及根据最优能谱对X射线源进行建模，并根据最优转换函数对探测器进行建模，以得到X射线成像设备的模型。

Description

一种X射线成像设备的建模方法和装置
交叉引用
本申请要求2020年06月23日提交的中国专利申请号202010580387.8和2020年06月23日提交的中国专利申请号202010582483.6的优先权，其内容全部通过引用并入本文。
技术领域
本申请涉及医学影像技术领域，特别是涉及一种X射线成像设备的建模方法和装置。
背景技术
目前，在需要计算和分析X射线成像设备产生的X射线剂量分布以及图像时需要对X射线成像设备进行建模。传统的做法是使用蒙卡软件模拟X射线成像设备。但是，使用蒙卡软件模拟X射线成像设备的效率很低。
例如，在X射线成像设备为乳腺机时，乳腺机有范围为20KV～40KV之间的21档电压选项和两种滤过(铑滤过和银滤过)，所以理论上乳腺机可能会产生42种能谱。若想要计算和分析乳腺机产生的剂量分布以及产生的图像，需要建模乳腺机。传统的做法是使用蒙卡软件模拟乳腺机整个设备，在固定电压和滤过前提下，如果模拟出的图像与实测图像一致，则可认为当前对乳腺机的模拟方案是正确的。而实际上，由于乳腺机的电压较低，根据实验，16台四核电脑采用蒙卡软件模拟乳腺机并行两天三夜生成的X射线图像精度也只是勉强可用，效率较低。
因此，需要提供一种X射线成像设备的建模方法和装置，用于提高X射线成像设备的建模效率。
发明内容
本申请实施例之一提供一种X射线成像设备的建模方法，所述X射线成像设备包括用于发出X射线的X射线源以及用于将穿透扫描对象的X射线转换为X射线图像的探测器，其特征在于，所述建模方法包括：获取所述X射线源发出的X射线的最优能谱；获取所述探测器的最优转换函数；以及根据所述最优能谱对所述X射线源进行建模，并根据所述最优转换函数对所述探测器进行建模，以得到所述X射线成像设备的模型。
在一些实施例中，所述获取所述X射线源发出的X射线的最优能谱和获取所述探测器对所述X射线的最优转换函数，包括：获取初始能谱和第一初始转换矩阵；获取单能剂量核；基于所述初始能谱，将多个不同的单能剂量核拼接成多能剂量核；获取所述多能剂量核 的X射线在所述扫描对象内的剂量分布；根据所述第一初始转换矩阵和所述剂量分布，得到所述扫描对象的X射线图像；以及获取实测图像，并找到与所述实测图像最接近的所述X射线图像，以在多个所述初始能谱中确定所述最优能谱和在多个所述第一初始转换矩阵中确定最优转换矩阵；其中，所述最优转换函数表示所述探测器将所述剂量分布转换为所述X射线图像的转换关系，所述最优转换矩阵为所述最优转换函数中的系数。
在一些实施例中，所述获取初始能谱和第一初始转换矩阵，包括：根据所述X射线成像设备的性能参数，配置所述初始能谱和所述第一初始转换矩阵。
在一些实施例中，所述获取所述多能剂量核的X射线在所述扫描对象内的剂量分布，包括：根据所述X射线成像设备的性能参数，配置通量矩阵；获取光子通量分布初值，并根据所述光子通量分布初值，得到所述多能剂量核的X射线的光子通量分布；所述光子通量分布初值为各所述单能剂量核的X射线的光子通量分布；根据衰减规律、所述通量矩阵以及所述多能剂量核的X射线的光子通量分布，得到穿透所述扫描对象衰减后的光子通量分布；以及对所述衰减后的光子通量分布和所述多能剂量核进行卷积，以得到所述剂量分布。
在一些实施例中，所述获取所述X射线源发出的X射线的最优能谱和获取所述探测器对所述X射线的最优转换函数，包括：获取初始能谱和初始转换函数；获取单能剂量核；基于所述初始能谱，将多个不同的单能剂量核拼接成多能剂量核；获取所述多能剂量核的X射线在穿透所述扫描对象衰减后的光子通量分布；根据所述初始转换函数和所述衰减后的光子通量分布，得到所述扫描对象的X射线图像；获取实测图像，并找到与所述实测图像最接近的所述X射线图像，以在多个所述初始能谱中确定所述最优能谱和在多个所述初始转换函数中确定所述最优转换函数；其中，所述最优转换函数表示所述探测器将穿透所述扫描对象衰减后的多能剂量核的X射线转换为X射线图像的转换关系。
在一些实施例中，所述获取初始转换函数，包括：根据所述探测器的图纸得到所述探测器对每个单能光子的转换函数初值；获取穿透所述扫描对象的衰减X射线能谱；以及基于所述衰减X射线能谱，将多个所述单能光子拼接成所述多能剂量核衰减后的X射线，并根据所述转换函数初值，得到所述初始转换函数。
在一些实施例中，所述获取所述探测器对所述X射线的最优转换函数，包括：分别获取每个厚度的所述扫描对象的第二初始转换矩阵；根据所述最优能谱对所述X射线源进行建模，并根据所述第二初始转换矩阵对所述探测器进行建模，以得到所述扫描对象的X射线图像；获取实测图像，并找到与所述实测图像最接近的所述X射线图像，以在所述第二初始转换矩阵中确定各不同厚度的所述扫描对象对应的最优转换矩阵；其中，所述最优转换函数表 示所述探测器将所述X射线源发出的X射线转换为所述扫描对象的X射线图像的转换关系，所述最优转换矩阵为所述最优转换函数中的系数。
在一些实施例中，所述获取所述X射线源发出的X射线的最优能谱，包括：利用蒙特卡罗算法模拟所述X射线源，以获取所述最优能谱；或者，利用所述X射线成像设备的所述X射线源进行试验，以获取所述最优能谱。
在一些实施例中，所述方法还包括：利用所述X射线成像设备的模型，得到所述扫描对象的X射线图像。
本申请实施例之一提供一种X射线成像设备的建模装置，所述X射线成像设备包括用于发出X射线的X射线源以及用于将穿透扫描对象的X射线转换为X射线图像的探测器，其特征在于，所述建模装置包括：能谱获取模块，用于获取所述X射线源发出的X射线的最优能谱；转换函数获取模块，用于获取所述探测器的最优转换函数；建模模块，用于根据所述最优能谱对所述X射线源进行建模，并根据所述最优转换函数对所述探测器进行建模，以得到所述X射线成像设备的模型。
在一些实施例中，所述转换函数获取模块进一步用于：获取初始能谱和第一初始转换矩阵；获取单能剂量核；基于所述初始能谱，将多个不同的单能剂量核拼接成多能剂量核；获取所述多能剂量核的X射线在所述扫描对象内的剂量分布；根据所述第一初始转换矩阵和所述剂量分布，得到所述扫描对象的X射线图像；以及获取实测图像，并找到与所述实测图像最接近的所述X射线图像，以在多个所述初始能谱中确定所述最优能谱和在多个所述第一初始转换矩阵中确定最优转换矩阵；其中，所述最优转换函数表示所述探测器将所述剂量分布转换为所述X射线图像的转换关系，所述最优转换矩阵为所述最优转换函数中的系数。
在一些实施例中，所述转换函数获取模块进一步用于：获取初始能谱和初始转换函数；获取单能剂量核；基于所述初始能谱，将多个不同的单能剂量核拼接成多能剂量核；获取所述多能剂量核的X射线在穿透所述扫描对象衰减后的光子通量分布；根据所述初始转换函数和所述衰减后的光子通量分布，得到所述扫描对象的X射线图像；获取实测图像，并找到与所述实测图像最接近的所述X射线图像，以在多个所述初始能谱中确定所述最优能谱和在多个所述初始转换函数中确定所述最优转换函数；其中，所述最优转换函数表示所述探测器将穿透所述扫描对象衰减后的多能剂量核的X射线转换为X射线图像的转换关系。
在一些实施例中，所述转换函数获取模块进一步用于：分别获取每个厚度的所述扫描对象的第二初始转换矩阵；根据所述最优能谱对所述X射线源进行建模，并根据所述第二初始转换矩阵对所述探测器进行建模，以得到所述扫描对象的X射线图像；获取实测图像，并 找到与所述实测图像最接近的所述X射线图像，以在所述第二初始转换矩阵中确定各不同厚度的所述扫描对象对应的最优转换矩阵；其中，所述最优转换函数表示所述探测器将所述X射线源发出的X射线转换为所述扫描对象的X射线图像的转换关系，所述最优转换矩阵为所述最优转换函数中的系数。
本申请实施例之一提供一种X射线成像设备的建模装置，所述装置包括至少一个处理器以及至少一个存储器；所述至少一个存储器用于存储计算机指令；所述至少一个处理器用于执行所述计算机指令中的至少部分指令以实现如本申请实施例之一提供的X射线成像设备的建模方法。
本申请实施例之一提供一种计算机可读存储介质，所述存储介质存储计算机指令，当计算机读取存储介质中的计算机指令后，计算机执行如本申请实施例之一提供的X射线成像设备的建模方法。
本申请实施例之一提供一种X射线成像设备的建模方法，包括：获取X射线成像设备的已模拟的无滤线栅的通量分布和能谱、以及已测试的带滤线栅的具有不同厚度的多个模体的实测图像；根据所述能谱分别模拟出各所述模体的多能剂量核，并根据计算因素分别计算出各所述模体的计算图像，其中，所述计算因素包括所述通量分布、各所述多能剂量核以及待调整的转换关系；分别对比各所述计算图像和各所述实测图像间的差异性，并根据对比结果判断所述转换关系是否需要进行调整。
在一些实施例中，所述根据对比结果判断所述转换关系是否需要进行调整，包括：若根据对比结果判断所述转换关系需要进行调整，则根据所述对比结果调整所述转换关系，并根据已调整的所述转换关系更新所述转换关系；重复执行所述根据计算因素分别计算出各所述模体的计算图像的步骤，直至所述对比结果满足预设建模条件。
在一些实施例中，所述多能剂量核为无栅多能剂量核，所述转换关系包括与各所述模体分别对应的第一转换关系；在所述直至所述对比结果满足预设建模条件之后，还包括：根据各所述第一转换关系得到目标转换关系，其中，所述目标转换关系是与各所述模体均对应的一个转换关系。
在一些实施例中，所述根据各所述第一转换关系得到目标转换关系，包括：根据各所述第一转换关系，分别确定未测试的各未测模体的第二转换关系；根据各所述第一转换关系和各所述第二转换关系，得到目标转换关系。
在一些实施例中，所述多能剂量核为带栅多能剂量核，所述转换关系为与各所述模体均对应的第三转换关系，所述计算因素还包括各所述模体对应的衰减比例；所述根据所述对 比结果调整所述转换关系，并根据已调整的所述转换关系更新所述转换关系，包括：根据对比结果调整各所述衰减比例，根据已调整的各所述衰减比例更新各所述衰减比例；根据各所述衰减比例调整所述第三转换关系，根据已调整的所述第三转换关系更新所述第三转换关系；在所述直至所述对比结果满足预设建模条件之后，还包括：对各所述衰减比例进行拟合，得到衰减比例和厚度间的拟合结果。
在一些实施例中，所述方法还包括：根据所述对比结果调整预设通量分布拟合函数中的各参数，根据已调整的所述预设通量分布拟合函数的拟合结果更新所述通量分布。
在一些实施例中，所述分别对比各所述计算图像和各所述实测图像间的差异性，包括：计算同一所述模体对应的所述计算图像和所述实测图像间对应位置的像素差值，并根据各所述模体对应的所述像素差值确定对比结果。
在一些实施例中，所述转换关系包括转换矩阵和/或多能转换函数，所述多能转换函数是根据所述X射线成像设备中探测器模拟出的每个单能光子照射在所述探测器上的单能转换函数组合得到的。
在一些实施例中，所述方法还包括：获取模拟X射线照射在待模拟物体上的衰减程度，根据所述衰减程度和已调整完成的所述转换关系，得到所述待模拟物体的厚度；基于所述待模拟物体的厚度对应的所述多能剂量核、所述通量分布和所述已调整完成的所述转换关系，计算出所述待模拟物体的模拟图像。
本申请实施例之一提供一种X射线成像设备的建模装置，包括：数据获取模块，用于获取X射线成像设备的已模拟的无滤线栅的通量分布和能谱、以及已测试的带滤线栅的具有不同厚度的多个模体的实测图像；图像计算模块，用于根据所述能谱分别模拟出各所述模体的多能剂量核，并根据计算因素分别计算出各所述模体的计算图像，其中，所述计算因素包括所述通量分布、各所述多能剂量核以及待调整的转换关系；调整判断模块，用于分别对比各所述计算图像和各所述实测图像间的差异性，并根据对比结果判断所述转换关系是否需要进行调整。
在一些实施例中，所述调整判断模块进一步用于：若根据对比结果判断所述转换关系需要进行调整，则根据所述对比结果调整所述转换关系，并根据已调整的所述转换关系更新所述转换关系；重复执行所述根据计算因素分别计算出各所述模体的计算图像的步骤，直至所述对比结果满足预设建模条件。
在一些实施例中，所述调整判断模块进一步用于：计算同一所述模体对应的所述计算图像和所述实测图像间对应位置的像素差值，并根据各所述模体对应的所述像素差值确定对 比结果。
在一些实施例中，所述转换关系包括转换矩阵和/或多能转换函数，所述多能转换函数是根据所述X射线成像设备中探测器的图纸模拟出的每个单能光子照射在所述探测器上的单能转换函数拼接得到的。
在一些实施例中，所述装置还包括：模拟图像获取模块，用于获取模拟X射线照射在待模拟物体上的衰减程度，根据所述衰减程度和已调整完成的所述转换关系，得到所述待模拟物体的厚度；并基于所述待模拟物体的厚度对应的所述多能剂量核、所述通量分布和所述已调整完成的所述转换关系，计算出所述待模拟物体的模拟图像。
本申请实施例之一提供一种X射线成像设备的建模装置，所述装置包括至少一个处理器以及至少一个存储器；所述至少一个存储器用于存储计算机指令；所述至少一个处理器用于执行所述计算机指令中的至少部分指令以实现如本申请实施例之一提供的一种X射线成像设备的建模方法。
本申请实施例之一提供一种计算机可读存储介质，其特征在于，所述存储介质存储计算机指令，当计算机读取存储介质中的计算机指令后，计算机执行如本申请实施例之一提供的一种X射线成像设备的建模方法。
附图说明
本申请将以示例性实施例的方式进一步说明，这些示例性实施例将通过附图进行详细描述。这些实施例并非限制性的，在这些实施例中，相同的编号表示相同的结构，其中：
图1是根据本申请一些实施例所示的示例性X射线成像设备的建模装置的应用场景示意图；
图2是根据本申请一些实施例所示的X射线成像设备的建模方法的示例性流程图；
图3是根据本申请一些实施例所示的X射线成像设备的建模方法的示例性流程图；
图4是根据本申请一些实施例所示的获取多能剂量核的X射线在扫描对象内的剂量分布的示例性流程图；
图5是根据本申请一些实施例所示的多能剂量核的X射线的光子通量分布的函数示意图；
图6a是根据本申请一些实施例所示的第一初始转换矩阵的示意图；
图6b是根据本申请一些实施例所示的第一初始转换矩阵对多能剂量核的X射线在扫描对象内的剂量分布的拟合效果示意图；
图7是根据本申请一些实施例所示的X射线成像设备的建模方法的示例性流程图；
图8是根据本申请一些实施例所示的获取初始转换函数的示例性流程图；
图9是根据本申请一些实施例所示的X射线成像设备的建模方法的示例性的流程图；
图10是根据本申请一些实施例所示的X射线成像设备的建模装置的示例性框图；
图11是根据本申请一些实施例所示的X射线成像设备的建模方法的示例性流程图；
图12a是根据本申请一些实施例所示的X射线成像设备的建模方法的转换关系示意图；
图12b是根据本申请一些实施例所示的X射线成像设备的建模方法的对比示意图；
图13是根据本申请一些实施例所示的X射线成像设备的建模方法中通量示意图；
图14是根据本申请一些实施例所示的X射线成像设备的建模方法的示例性流程图；
图15是根据本申请一些实施例所示的X射线成像设备的建模方法的示例性流程图；
图16是根据本申请一些实施例所示的X射线成像设备的建模装置的示例性框图；
图17是根据本申请一些实施例所示的一种设备的示例性框图。
具体实施方式
为了更清楚地说明本申请的实施例的技术方案，下面将对实施例描述中所需要使用的附图作简单的介绍。显而易见地，下面描述中的附图仅仅是本申请的一些示例或实施例，对于本领域的普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图将本申请应用于其他类似情景。应当理解，给出这些示例性的实施例仅仅是为了使相关领域的技术人员能够更好地理解进而实现本申请，而并非以任何方式限制本申请的范围。除非从语言环境中显而易见或另做说明，图中相同标号代表相同结构或操作。
应当理解，本文使用的“系统”、“装置”、“单元”和/或“模块”是用于区分不同级别的不同组件、元件、部件、部分或装配的一种方法。然而，如果其他词语可实现相同的目的，则可通过其他表达来替换所述词语。
如本申请和权利要求书中所示，除非上下文明确提示例外情形，“一”、“一个”、“一种”和/或“该”等词并非特指单数，也可包括复数。一般说来，术语“包括”与“包含”仅提示包括已明确标识的步骤和元素，而这些步骤和元素不构成一个排它性的罗列，方法或者设备也可能包含其它的步骤或元素。
虽然本申请对根据本申请的实施例的系统中的某些模块或单元做出了各种引用，然而，任何数量的不同模块或单元可以被使用并运行在客户端和/或服务器上。所述模块仅是说明性的，并且所述系统和方法的不同方面可以使用不同模块。
本申请中使用了流程图用来说明根据本申请的实施例的系统所执行的操作。应当理解 的是，前面或后面操作不一定按照顺序来精确地执行。相反，可以按照倒序或同时处理各个步骤。同时，也可以将其他操作添加到这些过程中，或从这些过程移除某一步或数步操作。
图1是根据本申请一些实施例所示的示例性X射线成像设备的建模装置的应用场景示意图。
如图1所示，X射线成像设备的建模装置100可以包括X射线成像设备110、网络120、终端130、处理设备140以及存储设备150。
X射线成像设备110可以用于X射线成像的设备。在一些实施例中，X射线成像设备110可以包括数字X线摄影(Digital Radiography，DR)设备、电子计算机断层扫描(Computed Tomography，CT)设备、锥形束投照电子计算机断层扫描(Cone Beam Computer Tomography，CBCT)设备、乳腺机等。在一些实施例中，X射线成像设备110可以包括X射线源和探测器。在成像过程中，扫描对象位于X射线源和探测器之间，X射线源向扫描对象发出X射线，探测器将穿透扫描对象的X射线转换为X射线图像。例如，X射线成像设备在对乳房成像时，由于X射线在穿透正常乳腺组织、肿瘤、钙化点等的衰减程度不同，穿透扫描对象的X射线到达探测器时携带的信息也不同，因此在探测器转换得到的X射线图像上也会呈现出差别，从而便于分辨乳房中病灶的位置、形状和大小。
在一些实施例中，X射线成像设备110可以是乳腺机。乳腺机可以具有21档电压选项(例如，20-40KV)以及两种滤过(例如，铑滤过和银滤过)，可以产生42种能谱。若想计算和分析乳腺机产生的剂量分布以及图像，需要在各能谱下对各模体进行建模。如果基于蒙卡(即，蒙特卡罗)软件实现对X射线成像设备110进行建模，在固定电压和滤过的情况下，若蒙卡软件生成的各模体的计算图像与相应的实测图像均一致，则认为当前的蒙卡建模方案正确。但是，因乳腺机电压较低，16台四核电脑并行运行2天2夜只能生成某模体的计算图像，且生成的计算图像的图像精度也只是勉强可用，无法实现乳腺机的高精度的建模。在一些实施例中，X射线成像设备的建模装置100可以用于对乳腺机进行建模，以提高对乳腺机进行建模的效率及精度。
网络120可以包括能够促进X射线成像设备的建模装置100的信息和/或数据交换的任何合适的网络，还可以是医院网络HIS(Hospital Information System)或PACS(Picture archiving and communication systems)或其他医院网络的一部分或者尽管与HIS或PACS或其他医院网络独立但连接于其上。在一些实施例中，X射线成像设备的建模装置100的一个或多个组件(例如，X射线成像设备110、终端130、处理设备140、存储设备150等)可以通过网络120与X射线成像设备的建模装置100的一个或多个组件之间交换信息和/或数据。例 如，处理设备140可以通过网络120从X射线成像设备110获取实测图像。网络120可以包括公共网络(如互联网)、私人网络(例如，局域网(LAN)、广域网(WAN))等)、有线网络(如以太网)、无线网络(例如，802.11网络、无线Wi-Fi网络等)、蜂窝网络(例如，长期演进(LTE)网络)、帧中继网络、虚拟专用网络(VPN)、卫星网络、电话网络、路由器、集线器、服务器计算机等其中一种或几种组合。例如，网络120可以包括有线网络、光纤网络、电信网络、局域网、无线局域网(WLAN)、城域网(MAN)，公用交换电话网(PSTN)、蓝牙 TM网络，ZigBee TM网络、近场通信(NFC)网络等其中一种或几种的组合。在一些实施例中，网络120可以包括一个或多个网络接入点。例如，网络120可以包括有线和/或无线网络接入点，例如基站和/或因特网交换点，通过所述接入点，X射线成像设备的建模装置100的一个或多个组件可以连接网络120以交换数据和/或信息。
终端130可以包括移动设备131、平板电脑132、笔记本电脑133等或其任意组合。在一些实施例中，终端130可以通过网络与X射线成像设备的建模装置100中的其他组件交互。例如，终端130可以向X射线成像设备110发送一种或多种控制指令以控制X射线成像设备110按照指令进行扫描。又例如，终端130可以接收处理设备140的处理结果。例如，终端130可以接收X射线成像设备的模型。还例如，终端130可以接收基于待模拟物体的厚度对应的多能剂量核、通量分布和已调整完成的转换关系，计算出的待模拟物体的模拟图像。在一些实施例中，移动设备131可以包括智能家居装置、可穿戴设备、移动装置、虚拟现实装置、增强现实装置等或其任意组合。在一些实施例中，智能家居装置可以包括智能照明装置、智能电器控制装置、智能监控装置、智能电视、智能摄像机、对讲机等或其任意组合。在一些实施例中，可穿戴设备可以包括手链、鞋袜、眼镜、头盔、手表、衣服、背包、智能附件等或其任意组合。在一些实施例中，移动装置可包括移动电话、个人数字助理(PDA)、游戏装置、导航装置、POS装置、笔记本电脑、平板电脑、台式机等或其任意组合。在一些实施例中，该虚拟现实装置和/或增强现实装置可包括虚拟现实头盔、虚拟现实眼镜、虚拟现实补丁、增强现实头盔、增强现实眼镜、增强现实补丁等或其任意组合。例如，该虚拟现实装置和/或增强现实装置可包括Google Glass TM、Oculus Rift TM、HoloLens TM或Gear VR TM等。在一些实施例中，终端130可以是处理设备140的一部分。
在一些实施例中，处理设备140可以处理从X射线成像设备110、终端130和/或存储设备150获得的数据和/或信息。例如，处理设备140可以从X射线成像设备110获取X射线成像设备110的性能参数。又例如，处理设备140可以从存储设备150获取初始能谱和初始转换函数。又例如，处理设备140可以从X射线成像设备110获取已测试的带滤线栅的具 有不同厚度的多个模体的实测图像。又例如，处理设备140可以从存储设备150获取X射线成像设备110的已模拟的无滤线栅的通量分布和能谱。在一些实施例中，处理设备140可以包括单个服务器或服务器组。服务器组可以是集中式的，也可以是分布式的。在一些实施例中，处理设备140可以是本地的或远程的。例如，处理设备140可以通过网络120从X射线成像设备110、终端130和/或存储设备150访问信息和/或数据。又例如，处理设备140可以直接连接X射线成像设备110、终端130和/或存储设备150以访问信息和/或数据。在一些实施例中，处理设备140可以在云平台上实现。例如，云平台可以包括私有云、公共云、混合云、社区云、分布式云、跨云、多云等其中一种或几种的组合。
存储设备150可以存储数据(例如，初始能谱、第一初始转换矩阵、X射线成像设备的性能参数、X射线成像设备的已模拟的无滤线栅的通量分布和能谱、已测试的带滤线栅的具有不同厚度的多个模体的实测图像等)、指令和/或任何其他信息。在一些实施例中，存储设备150可以存储从X射线成像设备110、终端130和/或处理设备140处获得的数据，例如，存储设备150可以存储从X射线成像设备110获得的扫描对象的实测图像。在一些实施例中，存储设备150可以存储处理设备140执行或使用的数据和/或指令，以执行本申请中描述的示例性方法。例如，存储设备150可以存储根据对比结果调整后的转换关系。又例如，存储设备150还可以存储从多个初始能谱中确定的最优能谱、从多个初始转换函数中确定的最优转换函数。在一些实施例中，存储设备150可包括大容量存储器、可移除存储器、易失性读写存储器、只读存储器(ROM)等其中一种或几种的组合。大容量存储可以包括磁盘、光盘、固态硬盘、移动存储等。可移除存储器可以包括闪存驱动器、软盘、光盘、存储卡、ZIP磁盘、磁带等。易失性读写存储器可以包括随机存取存储器(RAM)。RAM可以包括动态随机存储器(DRAM)、双数据率同步动态随机存取存储器(DDR-SDRAM)、静态随机存取存储器(SRAM)、可控硅随机存取存储器(T-RAM)、零电容随机存取存储器(Z-RAM)等。ROM可以包括掩模只读存储器(MROM)、可编程的只读存储器(PROM)、可擦除可编程只读存储器(EPROM)，电可擦除可编程只读存储器(EEPROM)、光盘只读存储器(CD-ROM)、数字多功能光盘等。在一些实施例中，存储设备150可以通过本申请中描述的云平台实现。例如，云平台可以包括私有云、公共云、混合云、社区云、分布式云、跨云、多云等其中一种或几种的组合。
在一些实施例中，存储设备150可以连接网络120，以与X射线成像设备的建模装置100中的一个或多个组件(例如，处理设备140、终端130等)之间实现通信。X射线成像设备的建模装置100中的一个或多个组件可以通过网络120读取存储设备150中的数据或指令。 在一些实施例中，存储设备150可以是处理设备140的一部分，也可以是独立的并与处理设备140直接或间接相连。
应该注意的是，上述描述仅出于说明性目的而提供，并不旨在限制本申请的范围。对于本领域普通技术人员而言，在本申请内容的指导下，可做出多种变化和修改。可以以各种方式组合本申请描述的示例性的实施例的特征、结构、方法和其他特征，以获得另外的和/或替代的示例性的实施例。例如，存储设备150可以是包括云计算平台的数据存储设备，例如公共云、私有云、社区和混合云等。然而，这些变化与修改不会背离本申请的范围。
本申请提供一种X射线成像设备的建模方法，以建立X射线成像设备的模型。例如，X射线成像设备包括直接数字平板X射线成像系统(DR，Digital Radiography)、电子计算机断层扫描(CT，Computed Tomography)、锥形束CT(CBCT，Cone beam CT)等。根据建立的X射线成像设备的模型可以得到X射线源产生的X射线的光子通量分布、X射线在扫描对象内的剂量分布、扫描对象的X射线图像等，从而对X射线成像设备进行分析，以对其进行优化，有利于减少对扫描对象辐射、提升X射线图像的质量等。
X射线成像设备可以包括X射线源和探测器。在成像过程中，扫描对象位于X射线源和探测器之间，X射线源向扫描对象发出X射线，探测器将穿透扫描对象的X射线转换为X射线图像。例如，X射线成像设备在对乳房成像时，由于X射线在穿透正常乳腺组织、肿瘤、钙化点等的衰减程度不同，穿透扫描对象的X射线到达探测器时携带的信息也不同，因此在探测器转换得到的X射线图像上也会呈现出差别，从而便于分辨乳房中病灶的位置、形状和大小。
图2是根据本申请一些实施例所示的X射线成像设备的建模方法的示例性的流程图。在一些实施例中，X射线成像设备的建模方法200可以由X射线成像设备的建模装置100(如处理设备140)执行。例如，X射线成像设备的建模方法200可以以程序或指令的形式存储在存储装置(如存储设备150)中，当X射线成像设备的建模装置100(如处理设备140)执行该程序或指令时，可以实现X射线成像设备的建模方法200。下面呈现的X射线成像设备的建模方法200的操作示意图是说明性的。在一些实施例中，可以利用一个或以上未描述的附加操作和/或未讨论的一个或以上操作来完成该过程。另外，图2中示出的和下面描述的流程200的操作的顺序不旨在是限制性的。
步骤210，获取X射线源发出的X射线的最优能谱。
最优能谱可以与X射线成像设备的性能参数有关，根据最优能谱建立的X射线源模型能够很好的反映X射线成像设备的特性，从而有利于对X射线成像设备的分析。在一些实 施例中，可以基于蒙特卡罗算法模拟X射线源或者基于X射线成像设备试验直接获取X射线源发出的X射线的最优能谱。在一些实施例中，也可以通过计算间接获取X射线源发出的X射线的最优能谱。在一些实施例中，最优能谱可以为理论上存在的X射线成像设备真正的能谱。在一些实施例中，最优能谱可以通过数学方法计算获得。
步骤220，获取探测器对X射线的最优转换函数。
具体的，获取探测器的最优转换函数。最优转换函数可以表示探测器将X射线源发出的X射线直接转换为扫描对象的X射线图像的转换关系，此时，转换函数可以与扫描对象相关，具体与扫描对象的扫描部位的厚度、密度等相关，因此，根据最优转换函数建立的探测器模型能够很好的体现扫描对象的特性，从而有利于对扫描对象的分析；最优转换函数也可以表示探测器将穿透扫描对象的X射线转换为扫描对象的X射线图像的转换关系，或者X射线源发出的X射线在扫描对象内的剂量分布转换为扫描对象的X射线图像的转换关系，此时，转换函数可以与X射线成像设备的性能参数相关，根据最优转换函数对探测器进行建模，探测器模型能够很好的反映X射线成像设备的特性，从而有利于对X射线成像设备的分析。关于获取探测器对X射线的最优转换函数的进一步说明可以参见本申请图3及其相关描述。
步骤230，根据最优能谱对X射线源进行建模，并根据最优转换函数对探测器进行建模，以得到X射线成像设备的模型。
具体的，根据最优能谱对X射线源进行建模后可以得到X射线源模型，根据最优转换函数对探测器进行建模后可以得到探测器模型，从而能够得到完整的X射线成像设备的模型。
上述X射线成像设备的建模方法200，根据获取的X射线源发出的X射线的最优能谱对X射线源进行建模，并根据获取的探测器对X射线的最优转换函数对探测器进行建模，从而得到X射线成像设备的模型，根据该X射线成像设备的模型可以获得更加接近实测图像的X射线图像(在某些实施例中，该X射线图像可以是计算图像)，从而有利于对X射线成像设备进行分析。
此外，传统的蒙卡软件模拟X射线成像设备的效率很低，并且由于探测器生成图像是与每个探测器晶体接收的光子的能级和能量有关的，并非完全的线性关系，比如一个探测器晶体接收到10个20KeV的光子和接收到20个10KeV的光子，虽然接收的总能量都是200KeV，但最后产生的图像灰度是有些不同的，因此，传统的蒙卡软件方法在模拟X射线成像设备时需要分别模拟X射线源和探测器，在根据模拟的X射线成像设备得到的X射线图像与实测图像不一致时，可能是模拟的X射线源和模拟的探测器中任意一个出错或者两个都 出错，需要找到出错原因并且需要重新模拟X射线成像设备以得到新的X射线图像，使得进一步降低建模效率。相对于传统的蒙卡软件模拟方法，上述建模方法采用最优转换函数表示对X射线或X射线在扫描对象内的剂量分布与X射线图像的转换关系，以替代传统的蒙特软件模拟方法中的对探测器的模拟部分，减少了利用蒙卡软件模拟的部分，使得提高建模效率。
在另一些实施例中，X射线成像设备的探测器接收到的光子会先穿透X射线成像设备中的滤线栅，因此，在对X射线成像设备进行建模时，除需要对整个X射线成像设备进行建模外，还可以对X射线成像设备中的探测器和滤线栅进行建模，对X射线成像设备中的探测器和滤线栅进行建模的进一步说明可以参见本申请图11及其相关描述。
图3是根据本申请一些实施例所示的X射线成像设备的建模方法的示例性流程图。如图3所示，X射线成像设备的建模方法具体包括以下步骤。其中图3实施例中步骤210和步骤220可以均包括步骤310至步骤360。
步骤310，获取初始能谱和第一初始转换矩阵。
在一些实施例中，可以根据X射线成像设备的性能参数，配置初始能谱和第一初始转换矩阵。X射线源可以包括球管和附加滤片。在一些实施例中，性能参数包括球管的阴极和阳极之间的电压档位及附加滤片的材料。在一些实施例中，可以根据球管的阴极和阳极之间的电压档位及附加滤片材料配置X射线源发出的X射线的初始能谱和探测器的第一初始转换矩阵。
在X射线源中，阳极通常由高原子序数的金属靶材料(如钼、钨等)制成，阴极具有由钨等材料制成的灯丝，通过加热阴极的灯丝释放电子，并在阴极和阳极之间施加高压电场，以对阴极释放的电子加速，加速后的电子轰击阳极的金属靶面，从而产生X射线。球管阴极与阳极之间的电压档位指阴极与阳极之间高压电场的电压大小。例如，对于乳腺机，球管的阴极与阳极之间的电压档位大致分为20KV～40KV之间的21档电压，不同的电压档位可以产生不同的X射线。
位于球管和扫描对象之间的附加滤片用于滤除球管发出的X射线中的一部分不必要的X射线，不同材料的附加滤片能够滤除不同X射线，从而减少患者对不必要X射线的辐射吸收剂量。例如，对于乳腺机，可以采用铑滤过、银滤过，不同的附加滤片材料可以得到不同的X射线。
在一些实施例中，在X射线成像设备为乳腺机时，X射线成像系统的性能参数可以是这21个电压档位和两种滤过的各种组合，并为每种组合分别配置对应的初始能谱和对应的 第一初始转换矩阵。在其他实施例中，由于一些特殊形状的附加滤片还可以产生特定光谱的X射线，在某种程度上就可以与扫描物体的扫描部位的吸收光谱相匹配，从而选择性的增加扫描对象的扫描部位中各部分的对比强度，因此，也可以根据球管的阴极与阳极之间电压档位和附加滤片的形状配置对应的初始能谱和对应的第一初始转换矩阵。
步骤320，获取单能剂量核。
具体的，可以利用蒙特卡罗算法模拟多个不同的单能剂量核，相对于直接模拟整个X射线源，模拟效率更高。例如，可以采用EGSnrc、Geant4等蒙卡软件进行模拟，也可以基于自己研发的蒙特卡罗算法进行模拟。单能剂量核的X射线为单能X射线。
步骤330，基于初始能谱，将多个不同的单能剂量核拼接成多能剂量核。
具体的，初始能谱为X射线源发出的X射线的能谱，而X射线源发出的X射线为多能X射线，即多能剂量核的X射线，因此初始能谱也是多能剂量核的X射线的能谱。以蒙特卡罗算法模拟多个不同的单能剂量核得到的单能X射线的能谱作为初值，基于初始能谱，对多个不同的单能剂量核进行组合得到多能剂量核。
步骤340，获取多能剂量核的X射线在扫描对象内的剂量分布。
具体的，多能剂量核的X射线在扫描对象内的剂量分布与X射线源发出的X射线即多能剂量核的X射线与扫描对象相关，根据多能剂量核和扫描对象的特性可以计算多能剂量核的X射线在扫描对象内的剂量分布。关于获取多能剂量核的X射线在扫描对象内的剂量分布的进一步说明可以参见本申请图4及其相关描述。
步骤350，根据第一初始转换矩阵和剂量分布，得到扫描对象的X射线图像。
具体的，将第一初始转换矩阵与多能剂量核的X射线在扫描对象内的剂量分布相乘，计算得到扫描对象的X射线图像。第一初始转换矩阵示意图参见图6a，第一初始转换矩阵对多能剂量核的X射线在扫描对象内的剂量分布的拟合效果参见图6b。
步骤360，获取实测图像，并找到与实测图像最接近的X射线图像，以在多个初始能谱中确定最优能谱和在第一初始转换矩阵中确定最优转换矩阵。
具体的，可以利用X射线成像设备对模拟扫描对象的扫描部位的模体进行扫描成像，以获取实测图像，也可以在患者的病历中找到符合条件的扫描图像作为实测图像。模体可以采用聚甲基丙烯酸甲酯(PMMA)模体、均匀的水模等，模体形状根据实际需求设置即可。例如，在X射线成像设备为乳腺机时，可以采用圆柱体模体、长方体模体、半球形模体等。
每个X射线成像设备的性能参数均可以对应配置有多个初始能谱和多个第一初始转换矩阵，初始能谱和第一初始转换矩阵可以根据经验得到，并分别根据这些初始能谱合初始 转换函数得到多个X射线图像，在这些X射线图像中找到与实测图像最接近的一个，以该X射线图像对应的初始能谱作为最优能谱和以该X射线图像对应的第一初始转换矩阵作为最优转换矩阵。本实施例中，最优转换函数表示探测器将剂量分布转换为X射线图像的转换关系，最优转换矩阵为最优转换函数中的系数。具体的，可以是将剂量分布转换为X射线图像中的灰度，从而将扫描对象的扫描部位内的不同部分以不同的图像灰度值呈现以进行区别。在一些实施例中，最优转换函数可以是在建模探测器将剂量转化为灰度的过程，与最优能谱配合，使用优化算法进行优化，当计算图像与实测图像之间最接近的时候的能谱。在一些实施例中，当计算图像与实测图像之间最接近的时候，此时的能谱和转换函数即为“最优”。
在另一些实施例中，还可以只为同一种X射线成像设备的系统参数设置一个初始能谱和一个第一初始转换矩阵，并利用该初始能谱和该第一初始转换矩阵得到X射线图像。将得到的X射线图像与实测图像进行对比，若它们之间的误差超出阈值，对初始能谱和第一初始转换矩阵进行修正后再建模并得到新的X射线图像，直到X射线图像与实测图像之间的误差小于等于阈值。在一些实施例中，可以基于一个或多个模体的X射线图像(即计算图像)和实测图像间对应位置的像素差值，判断是否要对初始能谱和第一初始转换矩阵进行修正。示例地，模体包括0、1、4、7cm模体，对比0cm模体的X射线图像(即计算图像)和实测图像的相应位置的像素值的相对误差，并将各像素值的相对误差之和作为0cm模体的像素差值，基于各像素值的相对误差之和判断X射线图像与实测图像之间的误差是否小于等于阈值；示例地，分别得到1、4、7cm模体和实测图像的像素差值，并根据这4个像素差值之和判断X射线图像与实测图像之间的误差是否小于等于阈值。在一些实施例中，在对初始能谱和第一初始转换矩阵进行修正时还可以基于机器学习算法等进行修正，使得到的X射线图像越来越接近实测图像。
步骤370，根据最优能谱对X射线源进行建模，并根据最优转换矩阵对探测器进行建模，以得到X射线成像设备的模型。
在一些实施例中，可以利用单能剂量核拼接成多能剂量核的方式减少建模过程中的蒙特卡罗算法模拟部分，使得进一步提高建模效率；利用最优转换矩阵建立探测器模型，简化模型，方便针对X射线成像设备的分析和计算；并且，为每档电压和每个滤过的组合分别配置一个最优转换矩阵、最优能谱和通量矩阵，使得提高拟合精度；此外，根据X射线成像设备的建模方法建立的X射线成像设备模型可以得到多个厚度的扫描对象的X射线图像，在需要通过判断多个厚度的扫描对象的X射线图像与实测图像集同时一致才表示当前建模方案正确时，可以兼顾多个调节参数。
图4是根据本申请一些实施例所示的获取多能剂量核的X射线在扫描对象内的剂量分布的示例性流程图。
步骤410，根据X射线成像设备的性能参数，配置通量矩阵。
具体的，X射线的成像设备的性能参数可以包括球管的阴极与阳极之间的电压档位和附加滤片的材料。本实施例中，同样为每种电压档位和滤过组合配置对应的通量矩阵。
步骤420，获取光子通量分布初值，并根据光子通量分布初值，得到多能剂量核的X射线的光子通量分布。
具体的，光子通量分布初值为各单能剂量核的X射线的光子通量分布。以蒙特卡罗算法模拟的单能剂量核的X射线的光子通量分布作为光子通量分布初值，利用多项式拟合多能剂量核的X射线的光子通量分布函数，在多项式中分别代入几组光子通量分布初值，可以计算得到多项式中各单项式的系数，从而可以得到光子通量分布函数。例如，在X射线成像设备为乳腺机时，利用多项式拟合多能剂量核的X射线的光子通量分布。多项式为：
f(x,y)＝p0+p1*(x-p2) 2+p3*y+p4*y*y+p5*y*y*y。
其中，p0为常数项，p1为变量x的二次项的因数，p3、p4以及p5分别为变量y的一次项、二次项以及三次项的因数。(x-p2) 2平方项是考虑到乳腺机是对称结构，所以光子通量分布也应该是关于中心轴对称的，p2为光子通量分布函数图像y方向中心轴坐标。利用光子通量分布初值可以解出p0至p5的值，从而得到多能剂量核的X射线的光子通量分布函数，多能剂量核的X射线的光子通量分布的函数图像参见图5。在一些实施例中，多项式中的p5*y*y*y这一项可以删去，从而可以对多项式进行简化。
步骤430，根据衰减规律、通量矩阵以及多能剂量核的X射线的光子通量分布，得到穿透扫描对象衰减后的光子通量分布。
具体的，衰减规律可以是传统的建模方法中X射线穿透扫描对象时采用的线性衰减规律。将通量矩阵与多能剂量核的X射线的光子通量分布相乘，再利用线性衰减规律计算穿透扫描对象衰减后的光子通量分布。
步骤440，对衰减后的光子通量分布和多能剂量核进行卷积，以得到剂量分布。
具体的，对衰减后的光子通量分布和多能剂量核进行卷积，以得到X射线源发出的X射线即多能剂量核的X射线在扫描对象内的剂量分布。
图7是根据本申请一些实施例所示的X射线成像设备的建模方法的示例性流程图。在一些实施例中，步骤210和步骤220均可以包括步骤710至步骤760。
步骤710，获取初始能谱和初始转换函数。
具体的，根据X射线成像设备的性能参数，配置初始能谱。X射线源包括球管和附加滤片。性能参数包括球管的阴极和阳极之间的电压档位及附加滤片的材料，根据球管的阴极和阳极之间的电压档位及附加滤片材料可以配置X射线源发出的X射线的初始能谱。
在一些实施例中，初始转换函数表示探测器将穿透扫描对象衰减后的X射线转换为扫描对象的X射线的转换关系。获取初始转换函数的过程可以与单能剂量核拼接多能剂量核的原理类似。关于获取初始转换函数的进一步说明可以参见本申请图8及其相关描述。
步骤720，获取单能剂量核。
具体的，可以利用蒙特卡罗算法模拟多个不同的单能剂量核，相对于直接模拟整个X射线源，模拟效率更高。例如，可以采用EGSnrc、Geant4等蒙卡软件进行模拟，也可以基于自己研发的蒙特卡罗算法进行模拟。单能剂量核的X射线为单能X射线。
步骤730，基于初始能谱，将多个不同的单能剂量核拼接成多能剂量核。
具体的，初始能谱为X射线源发出的X射线的能谱，而X射线源发出的X射线为多能X射线，即多能剂量核的X射线，因此初始能谱也是多能剂量核的X射线的能谱。以蒙特卡罗算法模拟多个不同的单能剂量核得到的单能X射线的能谱作为初值，基于初始能谱，对多个不同的单能剂量核进行组合得到多能剂量核。
步骤740，获取多能剂量核的X射线在穿透扫描对象衰减后的光子通量分布。
步骤750，根据初始转换函数和衰减后的光子通量分布，得到扫描对象的X射线图像。
具体的，可以采用与步骤420中类似的方法计算多能剂量核的X射线在衰减前的光子通量分布，并利用衰减规律计算多能剂量核的X射线在衰减后的光子通量分布。本实施例中，最优转换函数和初始转换函数均表示探测器对穿透扫描对象衰减后的多能剂量核的X射线转换为X射线图像的灰度转换关系。根据初始转换函数和衰减后的光子通量分布，计算扫描对象的X射线图像。
步骤760，获取实测图像，并找到与实测图像最接近的X射线图像，以在多个初始能谱中确定最优能谱和在多个初始转换函数中确定最优转换函数。
具体的，可以利用X射线成像设备对模拟扫描对象的扫描部位的模体进行扫描成像，以获取实测图像，也可以在患者的病历中找到符合条件的扫描图像作为实测图像。模体可以采用聚甲基丙烯酸甲酯(PMMA)模体、均匀的水模等，模体形状根据实际需求设置即可。例如，在X射线成像设备为乳腺机时，可以采用圆柱体模体、长方体模体、半球形模体等。
每个X射线成像设备的性能参数均可以对应配置有多个初始能谱，根据步骤710至 步骤730获取多个第一初始转换矩阵，并分别根据这些初始能谱和初始转换函数得到多个X射线图像，在这些X射线图像中找到与实测图像最接近的一个，以该X射线图像对应的初始能谱作为最优能谱和以该X射线图像对应的初始转换函数作为最优转换函数。
在其他实施例中，还可以只为同一种X射线成像设备的系统参数设置一个初始能谱，根据步骤710至步骤730确定一个初始转换函数，并利用该初始能谱和该初始转换函数得到X射线图像。将得到的X射线图像与实测图像进行对比，若它们之间的误差超出阈值，对初始能谱和初始转换函数进行修正后再建模并得到新的X射线图像，直到X射线图像与实测图像之间的误差小于等于阈值。在对初始能谱和初始转换函数进行修正时可以基于机器学习算法等进行修正，使得到的X射线图像越来越接近实测图像。
在一些实施例中，可以通过计算实测图像与X射线图像的相似度，并将相似度最高的X射线图像作为与实测图像最接近的X射线图像。在一些实施例中，实测图像与X射线图像的相似度可以基于实测图像与X射线图像间对应位置的像素值确定。在一些实施例中，若超过预设比例(例如，80％、85％等)的X射线图像与实测图像之间的相似度均低于预设阈值(例如，50％等)，还可以调整衰减规律(即，衰减比例)。
步骤770，根据最优能谱对X射线源进行建模，并根据最优转换函数对探测器进行建模，以得到X射线成像设备的模型。
图8是根据本申请一些实施例所示的获取初始转换函数的示例性流程图。获取初始转换函数可以包括步骤810至步骤830。
步骤810，根据探测器的图纸得到探测器对每个单能光子的转换函数初值。
具体的，根据探测器的图纸得到穿透扫描对象的每个单能光子到达探测器后被探测器转换为X射线图像的转换函数初值。转换函数初值即表示穿透扫描对象后的每个单能光子与X射线图像的转换关系。
步骤820，获取穿透扫描对象的衰减后的衰减X射线能谱。
具体的，衰减X射线能谱为多能剂量核的X射线经过扫描对象，被扫描对象吸收一部分衰减后X射线的能谱。获取穿透扫描对象衰减后的衰减X射线能谱可以根据X射线源发出的X射线即多能剂量核的X射线的能谱和X射线经过扫描对象的衰减规律得到，例如可以采用初始能谱作为X射线源发出的X射线的能谱，根据衰减规律计算衰减X射线能谱。
步骤830，基于衰减X射线能谱，将多个单能光子进行拼接成多能剂量核的X射线，并根据转换函数初值，得到初始转换函数。
具体的，基于衰减X射线能谱即衰减后的多能剂量核的X射线的能谱，对多个不同 的单能光子进行组合以得到多能剂量核衰减后的衰减多能X射线，根据这些不同的单能光子的转换函数初值，得到初始转换函数。
图9是根据本申请一些实施例所示的X射线成像设备的建模方法的示例性流程图。其中，步骤210可以包括步骤910，步骤220可以包括步骤920至步骤940。
步骤910，利用蒙特卡罗算法模拟X射线源，以获取最优能谱。
具体的，利用蒙特卡罗算法模拟X射线源，以获取最优能谱。在其他实施例中，也可以利用X射线成像设备的X射线源进行试验，以获取最优能谱。
步骤920，分别获取每个厚度的扫描对象的第二初始转换矩阵。
具体的，第二初始转换矩阵可以直接将X射线源发出的X射线直接转换为扫描对象的X射线图像。具体可以根据经验设置每个厚度的扫描对象的第二初始转换矩阵。例如，在X射线成像设备为乳腺机时，将PMMA模体的厚度分为0cm～7cm的八个厚度等级，并为每个厚度的PMMA模体分别配置对应的第二初始转换矩阵。其中0cm代表空拍图像。
步骤930，根据最优能谱对X射线源进行建模，并根据第二初始转换矩阵对探测器进行建模，以得到扫描对象的X射线图像。
具体的，由于最优能谱已经确定，因此根据最优能谱对X射线源进行建模能够得到比较准确的X射线模型，只需要确定建立探测器模型所需要的最优转换函数即可，本实施例中由于最优转换函数表示探测器将X射线源发出的X射线转换为扫描对象的X射线图像的转换关系，最优转换矩阵为最优转换函数中的系数，因此只需要确定最优转换矩阵即可。利用最优能谱建立的X射线源模型和第二转化矩阵建立的探测器模型可以得到扫描对象的X射线图像。
步骤940，获取实测图像，并找到与实测图像最接近的X射线图像，以在第二初始转换矩阵中确定各不同厚度的扫描对象对应的最优转换矩阵。
具体的，可以利用X射线成像设备对模拟扫描对象的扫描部位的模体进行扫描成像，以获取实测图像，也可以在患者的病历中找到符合条件的扫描图像作为实测图像。模体可以采用PMMA模体、均匀的水模等，模体形状根据实际需求设置即可。例如，在X射线成像设备为乳腺机时，可以采用圆柱体模体、长方体模体、半球形模体等。
每个厚度的扫描对象均可以对应配置有多个第二初始转换矩阵，并分别最优能谱建立的X射线源模型和这些第二初始转换矩阵建立的探测器模型分别得到多个X射线图像，在这些X射线图像中找到与实测图像最接近的一个，以该X射线图像对应的第二初始转换矩阵作为最优转换矩阵。
在其他实施例中，还可以为同一个厚度的扫描对象只设置一个第二初始转换矩阵，并利用该X射线源模型和该第二初始转换矩阵建立的探测器模型得到X射线图像。将得到的X射线图像与实测图像进行对比，若它们之间的误差超出阈值，对第二初始转换矩阵进行修正后再建模并得到新的X射线图像，直到X射线图像与实测图像之间的误差小于等于阈值。在对第二初始转换矩阵进行修正时可以基于机器学习算法等进行修正，使得到的X射线图像越来越接近实测图像。
步骤950，根据最优能谱对X射线源进行建模，并根据最优转换矩阵对探测器进行建模，以得到X射线成像设备的模型。
需要说明的是，上述各X射线成像设备的建模方法实施例中的各步骤可以进行任意合理的组合，从而形成更多X射线成像设备的建模方法的实施方式。例如将图2所示的X射线成像设备的建模方法中的初始能谱直接用图9所示的X射线成像设备的建模方法中获取的最优能谱替代，此处只是一个示例性的举例，其余各种组合方式不再赘述。
本申请还提供一种X射线图像的建模方法，包括：
利用如上任一实施例中的X射线成像设备的建模方法，得到X射线成像设备的模型；以及
利用X射线成像设备的模型，得到扫描对象的X射线图像。
本申请还提供一种X射线成像设备的建模装置，X射线成像设备包括用于发出X射线的X射线源以及用于将穿透扫描对象的X射线转换为X射线图像的探测器。如图10所示，X射线成像设备的建模装置1000包括能谱获取模块1010、转换函数获取模块1020以及建模模块1030。在一些实施例中，X射线成像设备的建模装置1000可以由图1所示的X射线成像设备的建模装置100(如处理设备140)实现。
能谱获取模块1010用于获取X射线源发出的X射线的最优能谱。转换函数获取模块1020用于获取探测器对X射线的最优转换函数。建模模块1030用于根据最优能谱对X射线源进行建模，并根据最优转换函数对所述探测器进行建模，以得到X射线成像设备的模型。本实施例中的能谱获取模块1010、转换函数获取模块1020以及建模模块1030可以对应实现上述X射线成像设备的建模方法各实施例中的对应步骤，此处不再赘述。
本申请还提供一种计算机可读存储介质，该计算机可读存储介质上存储有计算机程序，该计算机程序被处理器执行时可以使得处理器执行上述任意一个实施例中辐射控制修复方法的步骤。
上述对于计算机可读存储介质的限定可以参见上文中对于方法的具体限定，在此不再 赘述。
需要说明的是，本领域普通技术人员可以理解实现上述方法中的全部或部分流程，是可以通过计算机程序来指令相关的硬件来完成，该程序可存储于一计算机可读取存储介质中；上述的程序在执行时，可包括如上述各方法的实施例的流程。其中，上述存储介质可为磁碟、光盘、只读存储记忆体(Read-Only Memory，简称ROM)或随机存储记忆体(Random Access Memory，简称RAM)等。
在一些实施例中，图2-10所示的一个或多个方法或模块可以和图11-17所示的一个或多个方法或模块相结合，以更好的提升X射线成像设备的建模效率。
在一些实施例中，基于X射线成像设备中的探测器可生成实测图像，该实测图像的像素值与每个探测器晶体接收到的光子的能级和能量有关，但并非是完全的线性关系，如某个探测器晶体接收到10个20KeV的光子或是20个10KeV的光子，虽然其接收到的光子的总能量都是200KeV，但二者生成的实测图像的像素值存在差异。而且，探测器接收到的光子会先穿透X射线成像设备中的滤线栅，因此，在对X射线成像设备进行建模时，除需要对整个X射线成像设备进行建模外，还需要对X射线成像设备中的探测器和滤线栅进行建模。
图11是根据本申请一些实施例所示的X射线成像设备的建模方法的示例性的流程图。X射线成像设备的建模方法1100可以适用于对X射线成像设备进行建模的情况，尤其适用于对X射线成像设备中的探测器和滤线栅进行建模的情况。该方法可以由本申请实施例提供的X射线成像设备的建模装置来执行，该装置可以由软件和/或硬件的方式实现，该装置可以集成在各种用户终端或服务器上。在一些实施例中，X射线成像设备的建模方法1100可以由X射线成像设备的建模装置100(如处理设备140)执行。例如，X射线成像设备的建模方法1100可以以程序或指令的形式存储在存储装置(如存储设备150)中，当X射线成像设备的建模装置100(如处理设备140)执行该程序或指令时，可以实现X射线成像设备的建模方法1100。下面呈现的X射线成像设备的建模方法1100的操作示意图是说明性的。在一些实施例中，可以利用一个或以上未描述的附加操作和/或未讨论的一个或以上操作来完成该过程。另外，图11中示出的和下面描述的流程1100的操作的顺序不是限制性的。
步骤1110，获取X射线成像设备的已模拟的无滤线栅的通量分布和能谱、以及已测试的带滤线栅的具有不同厚度的多个模体的实测图像。
其中，通量分布是X射线成像设备产生的X射线的通量分布的模拟结果，该X射线未经由某模体和滤线栅，也就是说，该通量分布是模拟出的无滤线栅的通量分布，或是说模拟出的无栅设备的通量分布。这一模拟过程可以是基于蒙卡软件实现的，也可以是基于其余 算法建模实现的，在此未做具体限定。该蒙卡软件可以是任意可用于计算X射线剂量的开源软件比如EGSnrc、Geant4等等，也可以是自主研发的蒙特卡罗算法，在此未做具体限定。相应的，能谱的含义和模拟过程与通量分布类似，在此不再赘述。
在一些实施例中，实测图像可以是探测器实际生成的带滤线栅的各模体的图像，各模体的厚度通常互不相同，且每个模体的厚度是已知的，该模体可以是有机玻璃(Polymethylmethacrylate，PMMA)模体，也可以是其余材质的模体，在此未做具体限定；每个模体的实测图像的数量可以是1个或多个，在此未做具体限定。换言之，该实测图像是探测器基于经由某模体和滤线栅的X射线生成的图像，例如，若某模体是7cm模体，则该7cm模体对应的实测图像是探测器基于已先后穿透7cm模体和滤线栅的X射线生成的图像。需要说明的是，若某模体是0cm模体，这意味着实测图像是X射线成像设备对空气照射后生成的图像。
步骤1120，根据能谱分别模拟出各模体的多能剂量核，并根据计算因素分别计算出各模体的计算图像，其中，计算因素包括通量分布、各多能剂量核以及待调整的转换关系。
其中，根据已获取的能谱可分别模拟出各模体的多能剂量核，而通量分布和多能剂量核的卷积结果可以认为是一幅图像，其可以理解为经过某模体衰减后的X射线的剂量。由于每个模体或是说每个厚度均存在与其对应的多能剂量核，因此，每个模体亦存在与其对应的卷积结果。针对某一模体对应的卷积结果和实测图像，二者间通常是存在差异性的，这一差异性可以通过转换关系弥补，该转换关系的数量可以是一个或是多个，比如，各模体可以与同一个转换关系相对应，也可以分别与不同的转换关系相对应，在此未做具体限定。这样一来，根据通量分布、各多能剂量核以及转换关系可模拟出各模体的计算图像，示例性的，针对某模体，将通量分布和该模体的多能剂量核的卷积结果，再乘以转换关系，即可得到该模体的计算图像，而通量分布、多能剂量核和转换关系可称为计算因素。
多能剂量核可以是带栅设备(即，带滤线栅)的多能剂量核(即，带栅多能剂量核)，也可以是无栅设备(即，无滤线栅)的多能剂量核(即，无栅多能剂量核)，在此未做具体限定。在此基础上，一方面，多能剂量核的具体类型会对转换关系产生影响，比如，若多能剂量核在模拟时已考虑过滤线栅的影响，即卷积结果是带栅设备的卷积结果，则基于未考虑滤线栅的转换关系得到的计算图像亦是带栅设备的计算图像；若多能剂量核在模拟时未考虑过滤线栅，即卷积结果是无栅设备的卷积结果，则基于已考虑滤线栅的转换关系得到的计算图像方是带栅设备的计算图像，由此，带栅设备的计算图像和带栅设备的实测图像间才存在可比性。另一方面，能谱是模拟出的X射线成像设备直接产生的能谱，该能谱照射到探测器时 仍会发生变化，因此无论是带栅多能剂量核还是无栅多能剂量核，与其对应的转换关系均已考虑到探测器的影响。
步骤1130，分别对比各计算图像和各实测图像间的差异性，并根据对比结果判断转换关系是否需要进行调整。
其中，分别对比各计算图像和各实测图像间的差异性，并根据对比结果判断该转换关系是否需要进行调整。示例性的，当每个模体的计算图像和实测图像间的差异性都足够小时，和/或，当各模体的计算图像和实测图像间的差异性之和足够小时，这说明此时的转换关系和多能剂量核均是正确的，它们无需再进行调整，建模结束。一旦任一模体的计算图像和实测图像间的差异性较大，和/或，各模体的计算图像和实测图像间的差异性之和较大，这说明此时的转换关系仍是需要调整的，可根据某优化算法对转换关系进行调整。
示例性的，计算同一模体的计算图像和实测图像间对应位置的像素差值，并根据各模体对应的像素差值确定对比结果。比如，模体包括0、1、4、7cm模体，对比0cm模体的计算图像和实测图像的相应位置的像素值的相对误差，并将各像素值的相对误差之和作为0cm模体的像素差值；类似的，分别得到1、4、7cm模体的像素差值，并根据这4个像素差值之和判断是否需要调整转换关系。
在此基础上，可选的，当X射线成像设备建模结束后，可以获取到已调整完成的转换关系。由此，在后续应用中，当获取到X射线(如模拟X射线)照射在待模拟物体上的衰减程度时，可以根据衰减程度和已调整完成的转换关系，得到待模拟物体的厚度；进而，基于待模拟物体的厚度对应的多能剂量核、通量分布和已调整完成的转换关系，即可计算出待模拟物体的模拟图像。
本申请实施例的技术方案，通过获取X射线成像设备的已模拟的无滤线栅的通量分布和能谱、以及已测试的带滤线栅的具有不同厚度的多个模体的实测图像，可根据能谱分别模拟出各模体的多能剂量核，进而根据通量分布、各多能剂量核以及转换关系分别计算出各模体的计算图像，由此保证了模拟出的计算图像的图像精度和计算效率；由此，通过分别对比各计算图像和各实测图像间的差异性，可根据对比结果判断该转换关系是否需要进行调整，以得到与多能剂量核配合使用的转换关系。上述技术方案，通过获取到与多能剂量核配合使用的转换关系以缩小计算图像和实测图像间的差异性，实现了滤线栅和探测器的综合建模的效果，进而实现了X射线成像设备的高精度建模的效果。
在此基础上，可选的，根据对比结果判断转换关系是否需要进行调整，可包括：若根据对比结果判断转换关系需要进行调整，则可以根据对比结果调整转换关系，并根据已调整 的转换关系更新转换关系；重复执行根据计算因素分别计算出各模体的计算图像的步骤，直至对比结果满足预设建模条件。
其中，在根据调整结果更新转换关系后，计算因素亦会相对更新，则根据已更新的计算因素可重新计算出各模体的计算图像，并再次将各计算图像和各实测图像进行对比，根据对比结果是否满足预设建模条件，判断是再次调整转换关系，还是建模结束，该预设建模条件可以是某个像素差值阈值等等。当对比结果满足预设建模条件时，这说明基于此时的转换关系得到的计算图像与实测图像十分相似，建模结果较好。这一循环过程中涉及到的初始的转换关系可以是任意设置的，也可以是根据已有经验设置的，而基于该初始的转换关系得到的计算图像与实际图像间必然存在差异性，这也就涉及到根据差异性调整转换关系的循环过程。
示例性的，如图12a和图12b所示，这是当对比结果满足预设建模条件时，转换关系和建模结果的示意图，其中，图12a是根据本申请的一些实施例所示的一种X射线成像设备的建模方法的转换关系示意图；图12b是1、2、3……8、9cm模体的计算图像和实测图像的对比示意图(每幅图像通过线条示意)，横坐标是像素索引(1-72，72个像素索引)，纵坐标是像素值，由此可知，每个模体的计算图像和实测图像基本重叠在一起，这说明基于本申请实施例所述的建模方法得到的计算图像与实测图像十分相似，X射线成像设备的建模精度较高。
需要说明的是，首先，上述技术方案是针对某一能谱的各模体的建模方案，在该能谱建模结束后，可再次执行上述建模方法以实现另一能谱的各模体的建模，直至X射线成像设备涉及到的各能谱建模完毕，此时，该X射线成像设备建模完毕。其次，当计算图像和实测图像间存在差异性时，上述技术方案通过调整转换关系即可缩小这一差异性，调整过程较为简单。相应的，现有的基于蒙卡软件实现建模的技术方案，其需要先分析是哪个模拟过程出现错误，如探测器模拟出现错误、滤线栅模拟出现错误、探测器和滤线栅模拟均出现错误等等，再根据分析结果调整建模脚本，这一调整过程十分繁琐，建模效率极为低下，现有方案在保证建模完成度的同时，很难保证建模精度。
在上述技术方案的基础上，上述X射线成像设备的建模方法还可以包括：根据对比结果调整预设通量分布拟合函数中的各参数，根据已调整的预设通量分布拟合函数的拟合结果更新通量分布。其中，以基于蒙卡软件模拟通量分布为例，因蒙卡软件只能模拟出X射线成像设备中的主要部件，其无法模拟出各种螺丝等零散部件，因此，蒙卡软件和实际的X射线成像设备是存在差异性的，这就使得基于蒙卡软件模拟出的通量分布与实际的通量分布很 难完全相同。因此，类似于初始的转换关系，已获取的模拟出的通量分布也可以作为初始的通量分布，在获取到计算图像和实测图像间的差异性后，可根据该差异性对通量分布进行调整，以使基于已调整的通量分布得到的计算图像与实测图像间的差异性越来越小。而且，这一调整过程可通过调整预设通量分布拟合函数中的各参数实现，该预设通量分布拟合函数可用于根据各参数拟合出通量分布，由此，通过调整各参数即可实现通量分布的调整。这样设置的原因在于，转换关系和通量分布的配合调整，可提高X射线成像设备的建模精度。
示例性的，以乳腺机为例，其通量分布的大致形状如图13所示，预设通量分布拟合函数可以是f(x,y)＝P0+p1*(x-p2) 2+p3*y+p4*y*y+p5*y*y*y，(x-p2) 2这个平方项是考虑到乳腺机的结构是对称的，则光子的通量分布也应该是关于中心轴对称的，p2是y方向上图像中心轴(anode axis阳极轴/cathode axis阴极轴)的坐标。需要说明的是，预设通量分布拟合函数中的P0、p1、p2、p3、p4和p5是没有任何含义的参数；在图4中，左侧的弯曲面即为通量分布的一个示意图，orthogonal direction是正交方向，image plane是像平面，selected range是选择范围。在一些实施例中，预设通量分布拟合函数可以简化。例如，p5*y*y*y这一项可以删去。
一种可选的技术方案，转换关系可以包括转换矩阵和/或多能转换函数，多能转换函数是根据X射线成像设备中探测器(如探测器的图纸)模拟出的每个单能光子照射在探测器上的单能转换函数拼接(或组合)得到的。其中，转换关系可以是转换矩阵(如图3a所示)，可以是多能转换函数，也可以是其余的呈现形式，在此未做具体限定。以基于多能转换函数建模探测器为例，假设各光子是单能光子，多个单能光子基于不同衰减比例进行拼接即可构成某一能级的能谱。由此，根据X射线成像设备中探测器的图纸可模拟出的每个单能光子照射在探测器上的单能转换函数，进而，根据各单能光子的权重将各单能转换函数拼接起来以构成一个多能转换函数，这一多能转换函数也可理解为经过滤线栅衰减的能谱拼接出来的转换函数，且因X射线穿透滤线栅后只差一个探测器即可成像，由此，多能转换函数模拟了探测器对穿透滤线栅的X射线的成像效果。相应的，基于多能转换函数综合建模探测器和滤线栅是类似过程，在此不再赘述。
图14是根据本申请一些实施例所示的X射线成像设备的建模方法的示例性流程图。本实施例以上述各技术方案为基础进行优化。在本实施例中，可选的，多能剂量核为无栅多能剂量核，转换关系包括与各模体分别对应的第一转换关系，在直至对比结果满足预设建模条件之后，该方法还可包括：根据各第一转换关系得到目标转换关系，目标转换关系是与各模体均对应的一个转换关系。其中，与上述各实施例相同或相应的术语的解释在此不再赘述。
参见图14，本实施例的方法具体可以包括如下步骤：
步骤1410，获取X射线成像设备的已模拟的无滤线栅的通量分布和能谱、以及已测试的带滤线栅的具有不同厚度的多个模体的实测图像。
步骤1420，根据能谱分别模拟出各模体的无栅多能剂量核，并根据计算因素分别计算出各模体的计算图像，其中，计算因素包括通量分布、各无栅多能剂量核以及待调整的与各模体分别对应的第一转换关系。
其中，无栅多能剂量核是无栅设备的多能剂量核，其未考虑滤线栅的影响，正如上文所述，通量分布和无栅多能剂量核的卷积结果是无栅设备的卷积结果，而实测图像是带栅设备的实测图像，二者间必然存在差异性，这一差异性可通过第一转换关系体现出来，以便基于无栅设备的卷积结果和已考虑滤线栅影响的第一转换关系得到带栅设备的计算图像，与此同时，该第一转换关系也考虑到了探测器的影响。
需要说明的是，因第一转换关系是考虑到滤线栅影响的转换关系，且滤线栅在不同的模体下对X射线的衰减程度不同，因此，可为每个模体分别配置一个第一转换关系，基于多个第一转换关系分别建模滤线栅和探测器在相应的模体下的综合效果。可选的，在循环执行过程中，初始的第一转换关系可以是基于蒙卡软件模拟出能谱后为每个模体配置的，可以是任意配置的，也可以是根据已有经验配置的，在此未做具体限定。
步骤1430，分别对比各计算图像和各实测图像间的差异性，若根据对比结果判断转换关系需要进行调整，则根据对比结果调整第一转换关系，根据已调整的第一转换关系更新第一转换关系；重复执行根据计算因素分别计算出各模体的计算图像的步骤，直至对比结果满足预设建模条件。
其中，通常情况下，模拟出的计算图像与测试出的实测图像间对应位置的像素值是很难真正一致的，它们多存在一个衰减比例，比如0.1倍、10倍、100倍等等，且各模体或是说各厚度通常对应不同的衰减比例。但是，在本申请实施例中，因模体和第一转换关系具有一一对应关系，因此，各厚度的衰减比例可以直接合并到第一转换关系中，无需单独拎出来计算。
在根据对比结果调整第一转换关系时，可以根据对比结果对至少一个转换关系进行调整，如若根据对比结果确定除某一模体外，其余模体的计算图像和实测图像间的差异性均较小，则可只对该模体对应的第一转换关系进行调整；再比如，若对比结果是各模体的综合建模结果，则需要对各个第一转换关系进行调整；等等，在此未做具体限定。
步骤1440，根据各第一转换关系得到目标转换关系，其中，目标转换关系是与各模体 均对应的一个转换关系。
其中，在得到各个模体的建模结果后，可根据各第一转换关系得到与各模体均对应的一个目标转换关系，这样设置的原因在于：在建模结果的应用环节，待模拟物体的厚度是无法得知的，如果各第一转换关系同时存在，以待模拟物体的厚度是3cm和5cm为例，因为预先不知道厚度而无法确定调用哪个厚度对应的第一转换关系才能计算出3cm和5cm，因此，这时需要一个可以融合各厚度的目标转换关系，基于该目标转换关系确定待模拟物体的厚度，比如，因X射线照射在不同厚度的待模拟物体上的衰减程度不同，则根据目标转换关系可确定出这是哪个厚度对应的衰减程度，由此可确定出待模拟物体的厚度；进一步，可基于该厚度调用相应的无栅多能剂量核，并基于该无栅多能剂量核、通量分布和目标转换关系计算出该待模拟物体的计算图像。
在此基础上，根据各第一转换关系得到目标转换关系的实现方式有多种，比如，可以将各第一转换关系直接合并，并对合并结果取均值以得到目标转换关系；再比如，可以将各第一转换关系中对应位置的像素进行拟合以得到目标转换关系，如对各个第一转换关系中的第1个像素拟合出一个与厚度有关的转换函数、对第2个像素拟合出一个与厚度有关的转换函数……以此类推，由此，目标转换关系由N个转换函数构成，N是第一转换关系中的像素总数。当然，还可以通过其他方式得到目标转换关系，在此不再赘述。
可选的，针对那些未测试的各未测模体，该未测模体的厚度通常是已知的，比如若已测试的模体是厚度为0、1、4、7cm的模体，则未测模体可以是厚度为2、3、5、6cm的模体，根据各第一转换关系可分别确定出各未测模体的第二转换关系，如通过插值拟合的方式得到第二转换关系，进而，通过各第一转换关系和各第二转换关系可得到目标转换关系。同样的，也可根据各模体的无栅多能剂量核插值拟合出各未测模体的无栅多能剂量核，这些数据在建模结果的应用环节不可或缺。
本申请实施例的技术方案，通过无栅多能剂量核以及同时考虑了滤线栅和探测器影响的与各模体分别对应的第一转换关系相互配合，实现了X射线成像设备的高精度建模的效果。
图15是根据本申请一些实施例所示的X射线成像设备的建模方法的示例性流程图。本实施例以上述各技术方案为基础进行优化。在本实施例中，可选的，多能剂量核为带栅多能剂量核，转换关系为与各模体均对应的第三转换关系，计算因素还包括各模体对应的衰减比例；根据对比结果调整转换关系，根据已调整的转换关系更新转换关系，具体可以包括：根据对比结果调整各衰减比例，根据已调整的各衰减比例更新各衰减比例；根据各衰减比例调整第三转换关系，根据已调整的第三转换关系更新第三转换关系；在直至对比结果满足预 设建模条件之后，还可以包括：对各衰减比例进行拟合，得到衰减比例和厚度间的拟合结果。其中，与上述各实施例相同或相应的术语的解释在此不再赘述。
参见图15，本实施例的方法具体可以包括如下步骤：
步骤1510，获取X射线成像设备的已模拟的无滤线栅的通量分布和能谱、以及已测试的带滤线栅的具有不同厚度的多个模体的实测图像。
步骤1520，根据能谱分别模拟出各模体的带栅多能剂量核，并根据计算因素分别计算出各模体的计算图像，其中，计算因素包括通量分布、各带栅多能剂量核、各模体对应的衰减比例及待调整的与各模体均对应的第三转换关系。
其中，带栅多能剂量核是带栅设备的多能剂量核，其已考虑滤线栅的影响，正如上文所述，通量分布和带栅多能剂量核的卷积结果是带栅设备的卷积结果，而实测图像是带栅设备的实测图像，二者间必然存在差异性，这一差异性可通过第三转换关系体现出来，该第三转换关系主要是考虑了探测器的影响。需要说明的是，因第三转换关系是未考虑滤线栅影响的转换关系，由此可假设滤线栅不存在，一个第三转换关系即可融合探测器在各模体下的建模结果，即一个第三转换关系可融合各模体或是说各厚度。可选的，初始的第三转换关系可以是任意配置的，也可以是根据已有经验配置的，在此未做具体限定。
通常情况下，模拟出的计算图像与测试出的实测图像间对应位置的像素值很难真正一致，它们多存在一个衰减比例，比如0.1倍、10倍、100倍等等，且各模体因对X射线的衰减程度的差异较大而对应不同的衰减比例。在本申请实施例中，因各模体均对应同一个第三转换关系，因此，各衰减比例无法直接合并到第三转换关系中，其需要单独拎出来计算，此时的计算因素还包括各模体对应的衰减比例。由此，针对某模体，可将通量分布和该模体的带栅多能剂量核的卷积结果，乘以该模体对应的衰减比例，再乘以第三转换关系，得到该模体的计算图像。需要说明的是，该衰减比例中暗含了滤线栅的效果，这是因为，不同的模体对X射线的衰减程度不同，由此生成的图像像素也是不同的，且穿透某模体的X射线还需要穿透滤线栅方能被探测器接收到，因穿透不同模体的X射线的能谱已发生变化，则该X射线再穿透滤线栅的衰减程度更是不同的，由此得到的计算图像和实测图像间对应位置的像素值衰减比例亦是不同的，因此，衰减比例中暗含了滤线栅的效果。
步骤1530，分别对比各计算图像和各实测图像间的差异性，若根据对比结果判断转换关系需要进行调整，则根据对比结果调整各衰减比例，根据已调整的各衰减比例更新各衰减比例，并根据各衰减比例调整第三转换关系，根据已调整的第三转换关系更新第三转换关系；重复执行根据计算因素分别计算出各模体的计算图像的步骤，直至对比结果满足预设建 模条件。
其中，衰减比例和第三转换关系都是需要调整的，这是因为，各模体或是说各厚度对应的衰减比例不同，若某模体的建模结果不理想，则需要对该模体的衰减比例进行调整，而第三转换关系是融合各衰减比例的转换关系，因此，衰减比例的变化会带来第三转换关系的变化。
示例性的，根据对比结果调整衰减比例和第三转换关系的一个可选方案是，根据X射线照射在各模体上的衰减程度的已有经验设置各模体的初始的衰减比例，初始的第三转换关系可以是任意设置如某个单位矩阵，将通量分布与带栅多能剂量核的卷积结果，乘以某个模体的衰减比例，再乘以第三转换关系，可以得个该模体的计算图像；进而，将各模体的计算图像和实测图像间对应像素的相对误差之和作为目标函数，此时的目标函数的函数值较大，可根据函数值调整衰减比例，并基于各已调整衰减比例和最小二乘法调整第三转换关系；进而，重新模拟计算图像，并根据重新模拟的计算图像重新计算目标函数，判断该目标函数的函数值是否已最小，循环往复，直至函数值最小，这说明此时的衰减比例是正确的，此时的第三转换关系亦是正确的，建模结束。
步骤1540，对各衰减比例进行拟合，得到衰减比例和厚度间的拟合结果。
其中，在得到各模体的正确的衰减比例后，可对各衰减比例进行拟合，得到一个衰减比例和厚度间的拟合结果，该拟合结果可以是一个拟合函数，这样设置的原因在于：在建模结果的应用环节，待模拟物体的厚度是无法得知的，如果各个衰减比例同时存在，以待模拟物体的厚度是3cm和5cm为例，因为预先不知道厚度而无法确定调用哪个厚度对应的衰减比例才能计算出3cm和5cm，因此，这时需要一个可以融合各厚度的拟合结果，基于该拟合结果和X射线的衰减程度确定待模拟物体的厚度，进而调用与该厚度对应的衰减比例和带栅多能剂量核以得到该待模拟物体的计算图像。
需要说明的是，衰减比例拟合的执行顺序可以在得到正确的衰减比例后执行，也可以在得到初始的衰减比例后执行，比如，根据初始的衰减比例得到一个衰减比例和厚度间的拟合函数，此时，调整衰减比例的过程即为调整拟合函数的过程。
本申请实施例的技术方案，通过带栅多能剂量核、暗含了滤线栅的影响或者是滤线栅造成的衰减比例、以及暗含了探测器的且与各模体均对应的第三转换关系相互配合，实现了X射线成像设备的高精度建模的效果。
图16是根据本申请一些实施例所示的X射线成像设备的建模装置的示例性框图，X射线成像设备的建模装置1600可以用于执行上述任意实施例所提供的X射线成像设备的建 模方法。该装置与上述各实施例的X射线成像设备的建模方法属于同一个申请构思，在X射线成像设备的建模装置的实施例中未详尽描述的细节内容，可以参考上述X射线成像设备的建模方法的实施例。参见图16，X射线成像设备的建模装置1600可以包括：数据获取模块1610、图像计算模块1620和调整判断模块1630。在一些实施例中，X射线成像设备的建模装置1600可以由图1所示的X射线成像设备的建模装置100(如处理设备140)实现。
其中，数据获取模块1610，用于获取X射线成像设备的已模拟的无滤线栅的通量分布和能谱、及已测试的带滤线栅的具有不同厚度的多个模体的实测图像；
图像计算模块1620，用于根据能谱分别模拟出各模体的多能剂量核，并根据计算因素分别计算出各模体的计算图像，其中，计算因素包括通量分布、各多能剂量核以及待调整的转换关系；
调整判断模块1630，用于分别对比各计算图像和各实测图像间的差异性，并根据对比结果判断转换关系是否需要进行调整。
可选的，调整判断模块1630，具体可以包括：
转换关系调整单元，用于若根据对比结果判断转换关系需要进行调整，则根据对比结果调整转换关系，并根据已调整的转换关系更新转换关系；
重复执行单元，用于重复执行根据计算因素分别计算出各模体的计算图像的步骤，直至对比结果满足预设建模条件。
可选的，多能剂量核为无栅多能剂量核，转换关系包括与各模体分别对应的第一转换关系；在上述装置的基础上，该装置还可包括：
目标转换关系得到模块，用于根据各第一转换关系得到目标转换关系，其中，目标转换关系是与各模体均对应的一个转换关系。
可选的，目标转换关系得到模块，具体可以用于：
根据各第一转换关系，分别确定未测试的各未测模体的第二转换关系；
根据各第一转换关系和各第二转换关系，得到目标转换关系。
可选的，多能剂量核为带栅多能剂量核，转换关系为与各模体均对应的第三转换关系，计算因素还包括各模体对应的比例衰减比例；
转换关系调整单元，具体可以包括：比例衰减比例调整子单元，用于根据对比结果调整各比例衰减比例，根据已调整的各比例衰减比例更新各比例衰减比例；
第三转换关系调整子单元，用于根据各比例衰减比例调整第三转换关系，根据已调整的第三转换关系更新第三转换关系；
在此基础上，该装置还可包括：比例衰减比例拟合模块，用于对各比例衰减比例进行拟合，得到比例衰减比例拟合结果。
可选的，在上述装置的基础上，该装置还可包括：
通量分布调整单元，用于根据对比结果调整预设通量分布拟合函数中的各参数，根据已调整的预设通量分布拟合函数的拟合结果更新通量分布。
可选的，转换关系调整单元，可以包括：
对比子单元，用于计算同一模体对应的计算图像和实测图像间对应位置的像素差值，并根据各模体对应的像素差值确定对比结果。
可选的，转换关系可以包括转换矩阵和/或多能转换函数，多能转换函数是根据X射线成像设备中探测器的图纸模拟出的每个单能光子照射在探测器上的单能转换函数拼接得到的。
可选的，上述X射线成像设备的建模装置，还可以包括：
厚度得到模块，用于获取X射线(如模拟X射线)照射在待模拟物体上的衰减程度，根据衰减程度和已调整完成的转换关系，得到待模拟物体的厚度；
模拟图像计算模块，用于基于待模拟物体的厚度对应的多能剂量核、通量分布和已调整完成的转换关系，计算出待模拟物体的模拟图像。
本申请图16所示的实施例提供的X射线成像设备的建模装置，通过数据获取模块获取X射线成像设备的已模拟的无滤线栅的通量分布和能谱、以及已测试的带滤线栅的具有不同厚度的多个模体的实测图像；图像计算模块可根据能谱分别模拟出各模体的多能剂量核，进而根据通量分布、各多能剂量核以及转换关系分别计算出各模体的计算图像，由此保证了模拟出的计算图像的图像精度和计算效率；调整判断模块通过分别对比各计算图像和各实测图像间的差异性，可根据对比结果判断该转换关系是否需要进行调整，以得到与多能剂量核配合使用的转换关系。上述装置，通过获取到与多能剂量核配合使用的转换关系以缩小计算图像和实测图像间的差异性，实现了滤线栅和探测器的综合建模的效果，进而实现了X射线成像设备的高精度建模的效果。
本申请实施例所提供的X射线成像设备的建模装置可执行本申请任意实施例所提供的X射线成像设备的建模方法，具备执行方法相应的功能模块和有益效果。
值得注意的是，上述X射线成像设备的建模装置的实施例中，所包括的各个单元和模块只是按照功能逻辑进行划分的，但并不局限于上述的划分，只要能够实现相应的功能即可；另外，各功能单元的具体名称也只是为了便于相互区分，并不用于限制本申请的保护范 围。
图17是根据本申请一些实施例所示的一种设备的示例性结构示意图，如图17所示，该设备1700包括存储器1710、处理器1720、输入装置1730和输出装置1740。设备中的处理器1720的数量可以是一个或多个，图17中以一个处理器1720为例；设备中的存储器1710、处理器1720、输入装置1730和输出装置1740可以通过总线或其它方式连接，图17中以通过总线1750连接为例。
存储器1710作为一种计算机可读存储介质，可用于存储软件程序、计算机可执行程序以及模块，如本申请实施例中的X射线成像设备的建模方法对应的程序指令/模块(例如，X射线成像设备的建模装置中的数据获取模块1610、图像计算模块1620和调整判断模块1630)。处理器1720通过运行存储在存储器1710中的软件程序、指令以及模块，从而执行设备的各种功能应用以及数据处理，即实现上述的X射线成像设备的建模方法。
存储器1710可主要包括存储程序区和存储数据区，其中，存储程序区可存储操作系统、至少一个功能所需的应用程序；存储数据区可存储根据设备的使用所创建的数据等。此外，存储器1710可以包括高速随机存取存储器，还可以包括非易失性存储器，例如至少一个磁盘存储器件、闪存器件、或其他非易失性固态存储器件。在一些实例中，存储器1710可进一步包括相对于处理器1720远程设置的存储器，这些远程存储器可以通过网络连接至设备。上述网络的实例包括但不限于互联网、企业内部网、局域网、移动通信网及其组合。
输入装置1730可用于接收输入的数字或字符信息，以及产生与装置的用户设置以及功能控制有关的键信号输入。输出装置1740可包括显示屏等显示设备。
本申请提供一种包含计算机可执行指令的存储介质，所述计算机可执行指令在由计算机处理器执行时用于执行一种X射线成像设备的建模方法，该方法包括：
获取X射线成像设备的已模拟的无滤线栅的通量分布和能谱、以及已测试的带滤线栅的具有不同厚度的多个模体的实测图像；
根据能谱分别模拟出各模体的多能剂量核，根据计算因素分别计算出各模体的计算图像，计算因素包括通量分布、各多能剂量核以及待调整的转换关系；
分别对比各计算图像和各实测图像间的差异性，并根据对比结果判断转换关系是否需要进行调整。
当然，本申请实施例所提供的一种包含计算机可执行指令的存储介质，其计算机可执行指令不限于如上所述的方法操作，还可以执行本申请任意实施例所提供的X射线成像设备的建模方法中的相关操作。
通过以上关于实施方式的描述，所属领域的技术人员可以清楚地了解到，本申请可借助软件及必需的通用硬件来实现，当然也可以通过硬件实现，但很多情况下前者是更佳的实施方式。依据这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品可以存储在计算机可读存储介质中，如计算机的软盘、只读存储器(Read-Only Memory,ROM)、随机存取存储器(Random Access Memory,RAM)、闪存(FLASH)、硬盘或光盘等，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本申请各个实施例所述的方法。
注意，上述仅为本申请的较佳实施例及所运用技术原理。本领域技术人员会理解，本申请不限于这里所述的特定实施例，对本领域技术人员来说能够进行各种明显的变化、重新调整和替代而不会脱离本申请的保护范围。因此，虽然通过以上实施例对本申请进行了较为详细的说明，但是本申请不仅仅限于以上实施例，在不脱离本申请构思的情况下，还可以包括更多其他等效实施例，而本申请的范围由所附的权利要求范围决定。
同时，本申请使用了特定词语来描述本申请的实施例。如“一个实施例”、“一实施例”、和/或“一些实施例”意指与本申请至少一个实施例相关的某一特征、结构或特点。因此，应强调并注意的是，本说明书中在不同位置两次或多次提及的“一实施例”或“一个实施例”或“一个替代性实施例”并不一定是指同一实施例。此外，本申请的一个或多个实施例中的某些特征、结构或特点可以进行适当的组合。
此外，本领域技术人员可以理解，本申请的各方面可以通过若干具有可专利性的种类或情况进行说明和描述，包括任何新的和有用的工序、机器、产品或物质的组合，或对他们的任何新的和有用的改进。相应地，本申请的各个方面可以完全由硬件执行、可以完全由软件(包括固件、常驻软件、微码等)执行、也可以由硬件和软件组合执行。以上硬件或软件均可被称为“数据块”、“模块”、“引擎”、“单元”、“组件”或“系统”。此外，本申请的各方面可能表现为位于一个或多个计算机可读介质中的计算机产品，该产品包括计算机可读程序编码。
计算机存储介质可能包含一个内含有计算机程序编码的传播数据信号，例如在基带上或作为载波的一部分。该传播信号可能有多种表现形式，包括电磁形式、光形式等，或合适的组合形式。计算机存储介质可以是除计算机可读存储介质之外的任何计算机可读介质，该介质可以通过连接至一个指令执行系统、装置或设备以实现通讯、传播或传输供使用的程序。位于计算机存储介质上的程序编码可以通过任何合适的介质进行传播，包括无线电、电缆、光纤电缆、RF、或类似介质，或任何上述介质的组合。
本申请各部分操作所需的计算机程序编码可以用任意一种或多种程序语言编写，包括 面向对象编程语言如Java、Scala、Smalltalk、Eiffel、JADE、Emerald、C++、C#、VB.NET、Python等，常规程序化编程语言如C语言、Visual Basic、Fortran 2003、Perl、COBOL 2002、PHP、ABAP，动态编程语言如Python、Ruby和Groovy，或其他编程语言等。该程序编码可以完全在用户计算机上运行、或作为独立的软件包在用户计算机上运行、或部分在用户计算机上运行部分在远程计算机运行、或完全在远程计算机或服务器上运行。在后种情况下，远程计算机可以通过任何网络形式与用户计算机连接，比如局域网(LAN)或广域网(WAN)，或连接至外部计算机(例如通过因特网)，或在云计算环境中，或作为服务使用如软件即服务(SaaS)。
此外，除非权利要求中明确说明，本申请所述处理元素和序列的顺序、数字字母的使用、或其他名称的使用，并非用于限定本申请流程和方法的顺序。尽管上述披露中通过各种示例讨论了一些目前认为有用的申请实施例，但应当理解的是，该类细节仅起到说明的目的，附加的权利要求并不仅限于披露的实施例，相反，权利要求旨在覆盖所有符合本申请实施例实质和范围的修正和等价组合。例如，虽然以上所描述的系统组件可以通过硬件设备实现，但是也可以只通过软件的解决方案得以实现，如在现有的服务器或移动设备上安装所描述的系统。
同理，应当注意的是，为了简化本申请披露的表述，从而帮助对一个或多个申请实施例的理解，前文对本申请实施例的描述中，有时会将多种特征归并至一个实施例、附图或对其的描述中。但是，这种披露方法并不意味着本申请对象所需要的特征比权利要求中提及的特征多。实际上，实施例的特征要少于上述披露的单个实施例的全部特征。
最后，应当理解的是，本申请中所述实施例仅用以说明本申请实施例的原则。其他的变形也可能属于本申请的范围。因此，作为示例而非限制，本申请实施例的替代配置可视为与本申请的教导一致。相应地，本申请的实施例不仅限于本申请明确介绍和描述的实施例。

Claims (31)

  1. 一种X射线成像设备的建模方法，所述X射线成像设备包括用于发出X射线的X射线源以及用于将穿透扫描对象的X射线转换为X射线图像的探测器，其特征在于，所述建模方法包括：
    获取所述X射线源发出的X射线的最优能谱；
    获取所述探测器的最优转换函数；以及
    根据所述最优能谱对所述X射线源进行建模，并根据所述最优转换函数对所述探测器进行建模，以得到所述X射线成像设备的模型。
  2. 根据权利要求1所述的方法，其特征在于，所述获取所述X射线源发出的X射线的最优能谱和获取所述探测器对所述X射线的最优转换函数，包括：
    获取初始能谱和第一初始转换矩阵；
    获取单能剂量核；
    基于所述初始能谱，将多个不同的单能剂量核拼接成多能剂量核；
    获取所述多能剂量核的X射线在所述扫描对象内的剂量分布；
    根据所述第一初始转换矩阵和所述剂量分布，得到所述扫描对象的X射线图像；以及
    获取实测图像，并找到与所述实测图像最接近的所述X射线图像，以在多个所述初始能谱中确定所述最优能谱和在多个所述第一初始转换矩阵中确定最优转换矩阵；
    其中，所述最优转换函数表示所述探测器将所述剂量分布转换为所述X射线图像的转换关系，所述最优转换矩阵为所述最优转换函数中的系数。
  3. 根据权利要求2所述的方法，其特征在于，所述获取初始能谱和第一初始转换矩阵，包括：
    根据所述X射线成像设备的性能参数，配置所述初始能谱和所述第一初始转换矩阵。
  4. 根据权利要求2或3所述的方法，其特征在于，所述获取所述多能剂量核的X射线在所述扫描对象内的剂量分布，包括：
    根据所述X射线成像设备的性能参数，配置通量矩阵；
    获取光子通量分布初值，并根据所述光子通量分布初值，得到所述多能剂量核的X射线的光子通量分布；所述光子通量分布初值为各所述单能剂量核的X射线的光子通量分布；
    根据衰减规律、所述通量矩阵以及所述多能剂量核的X射线的光子通量分布，得到穿透所述扫描对象衰减后的光子通量分布；以及
    对所述衰减后的光子通量分布和所述多能剂量核进行卷积，以得到所述剂量分布。
  5. 根据权利要求1所述的方法，其特征在于，所述获取所述X射线源发出的X射线的最优能谱和获取所述探测器对所述X射线的最优转换函数，包括：
    获取初始能谱和初始转换函数；
    获取单能剂量核；
    基于所述初始能谱，将多个不同的单能剂量核拼接成多能剂量核；
    获取所述多能剂量核的X射线在穿透所述扫描对象衰减后的光子通量分布；
    根据所述初始转换函数和所述衰减后的光子通量分布，得到所述扫描对象的X射线图像；
    获取实测图像，并找到与所述实测图像最接近的所述X射线图像，以在多个所述初始能谱中确定所述最优能谱和在多个所述初始转换函数中确定所述最优转换函数；
    其中，所述最优转换函数表示所述探测器将穿透所述扫描对象衰减后的多能剂量核的X射线转换为X射线图像的转换关系。
  6. 根据权利要求5所述的方法，其特征在于，所述获取初始转换函数，包括：
    根据所述探测器的图纸得到所述探测器对每个单能光子的转换函数初值；
    获取穿透所述扫描对象的衰减X射线能谱；以及
    基于所述衰减X射线能谱，将多个所述单能光子拼接成所述多能剂量核衰减后的X射线，并根据所述转换函数初值，得到所述初始转换函数。
  7. 根据权利要求1所述的方法，其特征在于，所述获取所述探测器对所述X射线的最优转换函数，包括：
    分别获取每个厚度的所述扫描对象的第二初始转换矩阵；
    根据所述最优能谱对所述X射线源进行建模，并根据所述第二初始转换矩阵对所述探测器进行建模，以得到所述扫描对象的X射线图像；
    获取实测图像，并找到与所述实测图像最接近的所述X射线图像，以在所述第二初始转换矩阵中确定各不同厚度的所述扫描对象对应的最优转换矩阵；
    其中，所述最优转换函数表示所述探测器将所述X射线源发出的X射线转换为所述扫描对象的X射线图像的转换关系，所述最优转换矩阵为所述最优转换函数中的系数。
  8. 根据权利要求1或7所述的方法，其特征在于，所述获取所述X射线源发出的X射 线的最优能谱，包括：
    利用蒙特卡罗算法模拟所述X射线源，以获取所述最优能谱；
    或者，利用所述X射线成像设备的所述X射线源进行试验，以获取所述最优能谱。
  9. 根据权利要求1或7所述的方法，其特征在于，还包括：
    利用所述X射线成像设备的模型，得到所述扫描对象的X射线图像。
  10. 一种X射线成像设备的建模装置，所述X射线成像设备包括用于发出X射线的X射线源以及用于将穿透扫描对象的X射线转换为X射线图像的探测器，其特征在于，所述建模装置包括：
    能谱获取模块，用于获取所述X射线源发出的X射线的最优能谱；
    转换函数获取模块，用于获取所述探测器的最优转换函数；
    建模模块，用于根据所述最优能谱对所述X射线源进行建模，并根据所述最优转换函数对所述探测器进行建模，以得到所述X射线成像设备的模型。
  11. 根据权利要求10所述的装置，其特征在于，所述转换函数获取模块进一步用于：
    获取初始能谱和第一初始转换矩阵；
    获取单能剂量核；
    基于所述初始能谱，将多个不同的单能剂量核拼接成多能剂量核；
    获取所述多能剂量核的X射线在所述扫描对象内的剂量分布；
    根据所述第一初始转换矩阵和所述剂量分布，得到所述扫描对象的X射线图像；以及
    获取实测图像，并找到与所述实测图像最接近的所述X射线图像，以在多个所述初始能谱中确定所述最优能谱和在多个所述第一初始转换矩阵中确定最优转换矩阵；
    其中，所述最优转换函数表示所述探测器将所述剂量分布转换为所述X射线图像的转换关系，所述最优转换矩阵为所述最优转换函数中的系数。
  12. 根据权利要求10所述的装置，其特征在于，所述转换函数获取模块进一步用于：
    获取初始能谱和初始转换函数；
    获取单能剂量核；
    基于所述初始能谱，将多个不同的单能剂量核拼接成多能剂量核；
    获取所述多能剂量核的X射线在穿透所述扫描对象衰减后的光子通量分布；
    根据所述初始转换函数和所述衰减后的光子通量分布，得到所述扫描对象的X射线图像；
    获取实测图像，并找到与所述实测图像最接近的所述X射线图像，以在多个所述初始能谱中确定所述最优能谱和在多个所述初始转换函数中确定所述最优转换函数；
    其中，所述最优转换函数表示所述探测器将穿透所述扫描对象衰减后的多能剂量核的X射线转换为X射线图像的转换关系。
  13. 根据权利要求10所述的装置，其特征在于，所述转换函数获取模块进一步用于：
    分别获取每个厚度的所述扫描对象的第二初始转换矩阵；
    根据所述最优能谱对所述X射线源进行建模，并根据所述第二初始转换矩阵对所述探测器进行建模，以得到所述扫描对象的X射线图像；
    获取实测图像，并找到与所述实测图像最接近的所述X射线图像，以在所述第二初始转换矩阵中确定各不同厚度的所述扫描对象对应的最优转换矩阵；
    其中，所述最优转换函数表示所述探测器将所述X射线源发出的X射线转换为所述扫描对象的X射线图像的转换关系，所述最优转换矩阵为所述最优转换函数中的系数。
  14. 一种X射线成像设备的建模装置，其特征在于，所述装置包括至少一个处理器以及至少一个存储器；
    所述至少一个存储器用于存储计算机指令；
    所述至少一个处理器用于执行所述计算机指令中的至少部分指令以实现如权利要求1～9任意一项所述的一种X射线成像设备的建模方法。
  15. 一种计算机可读存储介质，其特征在于，所述存储介质存储计算机指令，当计算机读取存储介质中的计算机指令后，计算机执行如权利要求1～9任一项所述的一种X射线成像设备的建模方法。
  16. 一种X射线成像设备的建模方法，其特征在于，包括：
    获取X射线成像设备的已模拟的无滤线栅的通量分布和能谱、以及已测试的带滤线栅的具有不同厚度的多个模体的实测图像；
    根据所述能谱分别模拟出各所述模体的多能剂量核，并根据计算因素分别计算出各所述模体的计算图像，其中，所述计算因素包括所述通量分布、各所述多能剂量核以及待调整的转换关系；
    分别对比各所述计算图像和各所述实测图像间的差异性，并根据对比结果判断所述转换关系是否需要进行调整。
  17. 根据权利要求16所述的方法，其特征在于，所述根据对比结果判断所述转换关系是否需要进行调整，包括：
    若根据对比结果判断所述转换关系需要进行调整，则根据所述对比结果调整所述转换关系，并根据已调整的所述转换关系更新所述转换关系；
    重复执行所述根据计算因素分别计算出各所述模体的计算图像的步骤，直至所述对比结果满足预设建模条件。
  18. 根据权利要求17所述的方法，其特征在于，所述多能剂量核为无栅多能剂量核，所述转换关系包括与各所述模体分别对应的第一转换关系；在所述直至所述对比结果满足预设建模条件之后，还包括：
    根据各所述第一转换关系得到目标转换关系，其中，所述目标转换关系是与各所述模体均对应的一个转换关系。
  19. 根据权利要求18所述的方法，其特征在于，所述根据各所述第一转换关系得到目标转换关系，包括：
    根据各所述第一转换关系，分别确定未测试的各未测模体的第二转换关系；
    根据各所述第一转换关系和各所述第二转换关系，得到目标转换关系。
  20. 根据权利要求17所述的方法，其特征在于，所述多能剂量核为带栅多能剂量核，所述转换关系为与各所述模体均对应的第三转换关系，所述计算因素还包括各所述模体对应的衰减比例；所述根据所述对比结果调整所述转换关系，并根据已调整的所述转换关系更新所述转换关系，包括：
    根据对比结果调整各所述衰减比例，根据已调整的各所述衰减比例更新各所述衰减比例；
    根据各所述衰减比例调整所述第三转换关系，根据已调整的所述第三转换关系更新所述第三转换关系；
    在所述直至所述对比结果满足预设建模条件之后，还包括：对各所述衰减比例进行拟合，得到衰减比例和厚度间的拟合结果。
  21. 根据权利要求17所述的方法，其特征在于，还包括：
    根据所述对比结果调整预设通量分布拟合函数中的各参数，根据已调整的所述预设通量分布拟合函数的拟合结果更新所述通量分布。
  22. 根据权利要求16所述的方法，其特征在于，所述分别对比各所述计算图像和各所述实测图像间的差异性，包括：
    计算同一所述模体对应的所述计算图像和所述实测图像间对应位置的像素差值，并根据各所述模体对应的所述像素差值确定对比结果。
  23. 根据权利要求16所述的方法，其特征在于，所述转换关系包括转换矩阵和/或多能转换函数，所述多能转换函数是根据所述X射线成像设备中探测器模拟出的每个单能光子照射在所述探测器上的单能转换函数组合得到的。
  24. 根据权利要求16所述的方法，其特征在于，还包括：
    获取模拟X射线照射在待模拟物体上的衰减程度，根据所述衰减程度和已调整完成的所述转换关系，得到所述待模拟物体的厚度；
    基于所述待模拟物体的厚度对应的所述多能剂量核、所述通量分布和所述已调整完成的所述转换关系，计算出所述待模拟物体的模拟图像。
  25. 一种X射线成像设备的建模装置，其特征在于，包括：
    数据获取模块，用于获取X射线成像设备的已模拟的无滤线栅的通量分布和能谱、以及已测试的带滤线栅的具有不同厚度的多个模体的实测图像；
    图像计算模块，用于根据所述能谱分别模拟出各所述模体的多能剂量核，并根据计算因素分别计算出各所述模体的计算图像，其中，所述计算因素包括所述通量分布、各所述多能剂量核以及待调整的转换关系；
    调整判断模块，用于分别对比各所述计算图像和各所述实测图像间的差异性，并根据对比结果判断所述转换关系是否需要进行调整。
  26. 根据权利要求25所述的装置，其特征在于，所述调整判断模块进一步用于：
    若根据对比结果判断所述转换关系需要进行调整，则根据所述对比结果调整所述转换关系，并根据已调整的所述转换关系更新所述转换关系；
    重复执行所述根据计算因素分别计算出各所述模体的计算图像的步骤，直至所述对比结果满足预设建模条件。
  27. 根据权利要求25所述的装置，其特征在于，所述调整判断模块进一步用于：
    计算同一所述模体对应的所述计算图像和所述实测图像间对应位置的像素差值，并根据各所述模体对应的所述像素差值确定对比结果。
  28. 根据权利要求25所述的装置，其特征在于，所述转换关系包括转换矩阵和/或多能转换函数，所述多能转换函数是根据所述X射线成像设备中探测器的图纸模拟出的每个单能光子照射在所述探测器上的单能转换函数拼接得到的。
  29. 根据权利要求25所述的装置，其特征在于，还包括：
    模拟图像获取模块，用于获取模拟X射线照射在待模拟物体上的衰减程度，根据所述衰减程度和已调整完成的所述转换关系，得到所述待模拟物体的厚度；并基于所述待模拟物体的厚度对应的所述多能剂量核、所述通量分布和所述已调整完成的所述转换关系，计算出所述待模拟物体的模拟图像。
  30. 一种X射线成像设备的建模装置，其特征在于，所述装置包括至少一个处理器以及至少一个存储器；
    所述至少一个存储器用于存储计算机指令；
    所述至少一个处理器用于执行所述计算机指令中的至少部分指令以实现如权利要求16～24任意一项所述的一种X射线成像设备的建模方法。
  31. 一种计算机可读存储介质，其特征在于，所述存储介质存储计算机指令，当计算机读取存储介质中的计算机指令后，计算机执行如权利要求16～24任一项所述的一种X射线成像设备的建模方法。
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