WO2017148332A1 - 一种灌注分析方法与设备 - Google Patents

一种灌注分析方法与设备 Download PDF

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Publication number
WO2017148332A1
WO2017148332A1 PCT/CN2017/074703 CN2017074703W WO2017148332A1 WO 2017148332 A1 WO2017148332 A1 WO 2017148332A1 CN 2017074703 W CN2017074703 W CN 2017074703W WO 2017148332 A1 WO2017148332 A1 WO 2017148332A1
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perfusion
model
parameter
time concentration
concentration curve
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PCT/CN2017/074703
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English (en)
French (fr)
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龙帆
马杰延
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上海联影医疗科技有限公司
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Publication of WO2017148332A1 publication Critical patent/WO2017148332A1/zh
Priority to US16/116,817 priority Critical patent/US11004200B2/en
Priority to US17/316,692 priority patent/US11631178B2/en

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G06T7/0014—Biomedical image inspection using an image reference approach
    • G06T7/0016—Biomedical image inspection using an image reference approach involving temporal comparison
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10076—4D tomography; Time-sequential 3D tomography
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10088—Magnetic resonance imaging [MRI]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10104—Positron emission tomography [PET]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30096—Tumor; Lesion
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30101—Blood vessel; Artery; Vein; Vascular
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30101—Blood vessel; Artery; Vein; Vascular
    • G06T2207/30104—Vascular flow; Blood flow; Perfusion

Definitions

  • the present application relates to the field of medical imaging, and in particular, to a method and device for perfusion analysis.
  • Perfusion can refer to the important function of blood flow through the capillary network to deliver oxygen and metabolites to the organ cells of the organ. Through the perfusion measurement of the organ, the hemodynamic state of the microcirculation can be reflected, and then its function can be understood.
  • Perfusion imaging can be used to measure organ blood perfusion parameters with contrast agents by imaging techniques, and to understand organ microcirculation and functional status through perfusion parameters. In perfusion imaging, the process of determining perfusion parameters may be referred to as perfusion analysis.
  • Existing perfusion analysis methods have problems in that the processing speed is not fast enough and are susceptible to noise. Therefore, it is necessary to provide a perfusion analysis method and equipment, which can improve the existing perfusion analysis method and improve the speed and accuracy of obtaining perfusion parameters. Sex.
  • a perfusion analysis method may include: acquiring a scanned image, wherein the scanned image includes one or more images corresponding to one or more time points; acquiring a time concentration discrete point according to the scanned image; acquiring the discrete points according to the time concentration An initial time concentration curve, wherein the initial time concentration curve may represent a change in contrast agent concentration in an organ tissue corresponding to a pixel or a voxel point in the scanned image; acquiring a first perfusion model; a perfusion model and the initial time concentration curve, determining a first perfusion parameter; acquiring a second perfusion model; and determining a second perfusion parameter based on the second perfusion model and the first perfusion parameter.
  • the initial time concentration curve can include an initial input vascular time concentration curve and an initial tissue time concentration curve.
  • the first perfusion model can include a singular value model or a maximum slope model.
  • determining the first perfusion parameter according to the first perfusion model and the initial time concentration curve may include: deconvolving a residual function curve by a singular value model; and according to the residual function curve The first perfusion parameter is determined.
  • determining the first perfusion parameter according to the residual function curve may include: dividing the residual function curve into a delay segment, an intravascular segment, and an extravascular segment in chronological order; and according to the delay segment The intravascular segment and the extravascular segment determine the first perfusion parameter.
  • determining the first perfusion parameter according to the first perfusion model and the initial time concentration curve may include: a time concentration curve according to the initial input blood vessel and a time concentration curve of the initial tissue Determining an area under the time concentration curve of the initial input vessel, an area under a time concentration curve of the initial tissue, a maximum slope of an ascending segment of the time concentration curve of the initial tissue, and a peak time concentration curve of the initial input vessel; Determining the area under the time concentration curve of the initial input blood vessel, the area under the time concentration curve of the initial tissue, the maximum slope of the rising phase of the time concentration curve of the initial tissue, and the peak concentration curve of the initial input blood vessel The first perfusion parameter is described.
  • the first perfusion parameter can include blood volume, blood flow, and average transit time.
  • determining the second perfusion parameter according to the second perfusion model and the first perfusion parameter may include: a. determining an objective function; b. determining an estimated perfusion parameter; c. Determining an objective function value by the objective function, the predicted perfusion parameter, and the second perfusion model; and d. determining whether the target function value satisfies an end condition, and if the objective function value satisfies the end condition, The estimation function is determined as a second perfusion parameter, and if the target function value does not satisfy the end condition, bd may be performed to perform a new round of iteration, wherein in the first iteration, the estimated perfusion parameter It can be the first perfusion parameter.
  • the objective function may represent a sum of squared residuals and/or a sum of squared mean residuals
  • the sum of squared residuals may represent a tissue time concentration curve based on a second perfusion model and the initial tissue concentration curve
  • the sum of squared differences of contrast concentrations at the one or more time points, the sum of squared residuals may represent the ratio of the sum of squared residuals to the number of time points.
  • the time concentration curve of the tissue based on the second perfusion model may refer to a convolution of the initial input vessel time concentration curve to a residual function, which may be a function related to the perfusion parameter.
  • the end condition may include at least one of the following conditions: the sum of squared mean residuals is less than a first threshold; the absolute value of the difference of the estimated perfusion parameters of two adjacent iterative processes The value is less than the second threshold.
  • determining the predicted perfusion parameter may include: determining, according to the estimated perfusion parameter of the last iteration process, a feasible descent direction within a preset range of the perfusion parameter, according to the feasible descent direction Determining the predicted perfusion parameter, wherein the predictive model can include a Levenberg-Marquardt model.
  • the second perfusion model can include an AATH model (Adiabatic Approximation to Tissue Homogeneity) and/or a DP model (Distributed Parameter model).
  • AATH model Aligntic Approximation to Tissue Homogeneity
  • DP model Distributed Parameter model
  • the second perfusion parameter can include blood flow, blood volume, mean transit time, and capillary surface permeability.
  • a perfusion analysis device can include: a computer readable storage medium configured to store an executable module; and a processor executable to execute the executable module of the computer readable storage medium storage.
  • the feasible execution module may include: an image acquisition module configured to acquire a scanned image, wherein the scanned image may include one or more images corresponding to one or more time points; and a discrete point acquisition module configured to Scanning the image to obtain a discrete point of time concentration; the curve determining module is configured to obtain an initial time concentration curve according to the time concentration discrete point, wherein the initial time concentration curve may represent a pixel or a voxel in the scanned image a change in contrast agent concentration in the organ tissue corresponding to the point; a first model acquisition module configured to acquire the first perfusion model; a first parameter determination module configured to be according to the first perfusion model and the An initial time concentration curve determining a first perfusion parameter; a second model acquisition module configured to acquire a second perfusion model;
  • FIG. 1 is a schematic illustration of a perfusion imaging system, in accordance with some embodiments of the present application.
  • FIG. 2 is an exemplary flow diagram of perfusion imaging, in accordance with some embodiments of the present application.
  • FIG. 3 is an architecture of a computer device of a perfusion analysis device, in accordance with some embodiments of the present application.
  • FIG. 4 is an exemplary schematic diagram of a perfusion analysis device, in accordance with some embodiments of the present application.
  • Figure 5 is an exemplary flow chart for determining a second perfusion parameter, in accordance with some embodiments of the present application.
  • Figure 6 is an exemplary flow chart for determining a second perfusion parameter, in accordance with some embodiments of the present application.
  • Figure 7 is a graph of the fit effect of a tissue time concentration curve, in accordance with some embodiments of the present application.
  • FIG. 8 is a schematic diagram of verification of a second perfusion parameter result.
  • Figures 9-A through 9-D are brain tumor perfusion parameter maps obtained from a second perfusion parameter.
  • Perfusion can refer to the important function of blood flow through the capillary network to deliver oxygen and metabolites to the organ cells of the organ. Through the perfusion measurement of the organ, the hemodynamic state of the microcirculation can be reflected, and then its function can be understood.
  • Perfusion imaging can be used to measure organ blood perfusion with contrast agents by imaging techniques, and to obtain perfusion parameters and perfusion parameter maps to understand organ microcirculation and functional status. In perfusion imaging, the process of determining perfusion parameters may be referred to as perfusion analysis.
  • Perfusion imaging system 100 can include one or more imaging Device 110, one or more networks 120, one or more perfusion analysis devices 130, one or more databases 140.
  • the imaging device 110 can scan the detected object to obtain scan data, which can be sent to the perfusion analysis device 130 through the network 120 for further processing, or can be directly stored in the database 140 through the network 120.
  • the detection object may include a human body, an animal, or the like.
  • Imaging device 110 may include, but is not limited to, a Computed Tomography (CT) device, a Magnetic Resonance Imaging (MRI) device, or a Positron Emission Computed Tomography (PET) device.
  • CT Computed Tomography
  • MRI Magnetic Resonance Imaging
  • PET Positron Emission Computed Tomography
  • the perfusion analysis device 130 can process and analyze the input data (eg, scan data obtained by the imaging device 110 and/or stored in the database 140, scanned images) to generate a processing result. For example, the perfusion analysis device 130 can generate a scan image based on the scan data. As another example, perfusion analysis device 130 can process, analyze, and obtain perfusion parameter results and/or generate perfusion parameter maps.
  • the scanned image may be a two-dimensional image or a three-dimensional image.
  • the perfusion analysis device 130 can include a processor and input/output devices (not shown).
  • the processor can be a server or a server group.
  • a server group can be centralized, such as a data center.
  • a server group can also be distributed, such as a distributed system.
  • the processor may be one or a combination of a cloud server, a file server, a database server, a File Transfer Protocol (FTP) server, an application server, a proxy server, a mail server, and the like.
  • the processor can be local or remote.
  • the local processor can include a processor integrated in the perfusion analysis device 130.
  • the remote processor can include a processor coupled to the perfusion analysis device 130 over a network (eg, network 120).
  • the processor can access information stored in database 140 (eg, a medical image stored in database 140), information in imaging device 110 (eg, a medical image taken by imaging device 110).
  • the input/output device may input data to the processor, or may receive data output by the processor, and output the data in numbers, characters, images, videos, animations, sounds, and the like. The form is expressed.
  • the input/output devices may include, but are not limited to, one or a combination of input devices, output devices, and the like.
  • the input device may include, but is not limited to, a character input device (eg, a keyboard), an optical reading device (eg, an optical tag reader, an optical character reader), a graphic input device (eg, a mouse, a joystick, a stylus), an image A combination of one or more of an input device (eg, a video camera, a scanner, a fax machine), an analog input device (eg, a language analog to digital conversion recognition system), and the like.
  • the output device may include but is not limited to a display device, a printing device, a plotter, and an image. A combination of one or more of an output device, a voice output device, a magnetic recording device, and the like.
  • the perfusion analysis device 130 can further include a storage device (not shown).
  • the storage device can store various information such as programs, data, and the like.
  • data and/or processing results eg, scan images, perfusion parameters, perfusion parameter maps, etc.
  • perfusion analysis device 130 may be stored in storage device of database 140 and/or perfusion analysis device 130, It can be output through the input/output device.
  • Database 140 can be broadly referred to as a device having storage capabilities.
  • the database 140 can store scan data collected from the imaging device 110 and various data generated in the operation of the perfusion analysis device 130.
  • Database 140 can be local or remote.
  • the local database may include a device having a storage function integrated in the database 140.
  • the remote database may include a storage-enabled device connected to the database 140 over a network (eg, network 120).
  • Database 140 may include, but is not limited to, a combination of one or more of a hierarchical database, a networked database, and a relational database.
  • the database 140 can digitize the information and store it in a storage device that utilizes electrical, magnetic or optical means.
  • the database 140 can be used to store various information such as programs and data.
  • the database 140 may be a device that stores information by means of electrical energy, such as various memories, random access memory (RAM), read only memory (ROM), and the like.
  • the random access memory includes but is not limited to a decimal counter tube, a selection tube, a delay line memory, a Williams tube, a dynamic random access memory (DRAM), a static random access memory (SRAM), a thyristor random access memory (T-RAM), and a zero capacitor.
  • DRAM dynamic random access memory
  • SRAM static random access memory
  • T-RAM thyristor random access memory
  • Z-RAM random access memory
  • Read-only memory includes, but is not limited to, bubble memory, magnetic button line memory, thin film memory, magnetic plate line memory, magnetic core memory, drum memory, optical disk drive, hard disk, magnetic tape, early non-volatile memory (NVRAM), phase change Memory, magnetoresistive random storage memory, ferroelectric random access memory, nonvolatile SRAM, flash memory, electronic erasable rewritable read only memory, erasable programmable read only memory, programmable read only memory, shielded A combination of one or more of a heap read memory, a floating connection gate random access memory, a nano random access memory, a track memory, a variable resistive memory, a programmable metallization cell, and the like.
  • the database 140 may be a device that stores information using magnetic energy, such as a hard disk, a floppy disk, a magnetic tape, a magnetic core memory, a magnetic bubble memory, a USB flash drive, a flash memory, or the like.
  • the database 140 may be a device that optically stores information, such as a CD or a DVD.
  • the database 140 may be a device that stores information using magneto-optical means, such as a magneto-optical disk or the like.
  • the access mode of the database 140 may be one or a combination of random storage, serial access storage, read-only storage, and the like.
  • the database 140 can be a non-permanent memory or a permanent memory.
  • the storage device mentioned above is merely an example, and the database that can be used in the perfusion imaging system 100 is not limited thereto.
  • Network 120 can be a single network or a combination of multiple networks.
  • Network 120 may include, but is not limited to, a combination of one or more of a local area network, a wide area network, a public network, a private network, a wireless local area network, a virtual network, a metropolitan area network, a public switched telephone network, and the like.
  • Network 120 may include a variety of network access points, such as wired or wireless access points, base stations, or network switching points, through which the data sources are connected to network 120 and transmitted over the network.
  • database 140 may be a cloud computing platform with data storage capabilities, including but not limited to public clouds, private clouds, community clouds, hybrid clouds, and the like. Variations such as these are within the scope of the present application.
  • a contrast agent can be injected into the test subject.
  • the detection object may include a human body, an animal, or the like.
  • blood circulation and organ microcirculation and functional status can be understood by tracking the flow of the contrast agent in the subject.
  • the contrast agent can include a high density contrast agent or a low density contrast agent.
  • the high density contrast agent can include a barium sulfate or iodine formulation.
  • the iodine preparation may include an inorganic iodide, an organic iodide, an iodized oil or a fatty acid iodide.
  • the organic iodide may include an ionic iodine preparation, a nonionic iodine preparation, or a nonionic dimer iodine preparation.
  • a contrast agent can be administered intravenously to the subject.
  • the test subject can also enter the subject with a contrast agent (eg, barium sulfate) into the subject.
  • the detected object may be scanned by the imaging device 110.
  • the region of interest of the detected object can be scanned.
  • the region of interest may be an entirety or a part of the detection object, for example, a head, a chest, an abdomen, a heart, a liver, an upper limb, a lower limb, a spine, a bone, a blood vessel, a lesion, a tumor site, etc., or the above random combination.
  • scanned images of a plurality of time points may be acquired for selected layers of the region of interest to record changes in contrast agent concentration over time in the selected layer of organ tissue.
  • the change of contrast agent concentration in organ tissues with time can be expressed by Time-Density Curve (TDC).
  • the time concentration curve indicates the change of the concentration of the contrast agent in the organ tissue corresponding to a certain pixel or voxel point in the scanned image with time.
  • the time concentration curve may include the input blood vessel time concentration curve and the output blood vessel time concentration curve.
  • tissue time concentration curve can represent the change in contrast agent concentration over time in the organ tissue of the region of interest.
  • the input vascular time concentration curve can represent the change in concentration of the contrast agent in the blood vessels supplying blood to the organ tissue of the region of interest over time.
  • the input blood vessels may include, but are not limited to, an artery that supplies blood to organ tissues, for example, a anterior cerebral artery, a middle cerebral artery, and the like.
  • the output vessel time concentration curve can represent the change in concentration of the contrast agent in the output vessel over time.
  • the output blood vessels can include, but are not limited to, veins.
  • the perfusion parameters may be determined by the perfusion analysis device 130.
  • the perfusion parameter can be determined using a perfusion model based on a time concentration curve.
  • the perfusion parameter can refer to parameters related to hemodynamics.
  • the perfusion parameters may include, but are not limited to, Blood Flow (BF), Blood Volume (BV), Mean Transition Time (MTT), Permeability Surface (PS), etc.
  • BF Blood Flow
  • BV Blood Volume
  • MTT Mean Transition Time
  • PS Permeability Surface
  • Blood flow refers to the total amount of blood flow through the vascular structure of the region of interest per unit time.
  • Blood volume refers to the total amount of blood in the area of interest.
  • the mean transit time refers to the average time that blood flows through the vascular structure in the region of interest.
  • Vascular structures within the region of interest may include arteries, capillaries, venous sinuses, veins, and the like.
  • the blood flow through the region of interest differs in the type of blood vessel, and its transit time varies.
  • the application can use the average transit time as one of the perfusion parameters.
  • the average transit time can reflect the time that the contrast agent passes through the capillaries.
  • Capillary surface permeability refers to the rate of unidirectional delivery of contrast agent into the intercellular space via the capillary endothelium. In general, the capillary surface permeability at the tumor lesion is greater than the capillary surface permeability at the non-tumor lesion. Therefore, capillary surface permeability can be used for the study of tumor lesions.
  • the perfusion model can include, but is not limited to, a non-deconvolution model and a deconvolution model.
  • the non-deconvolution model can include, but is not limited to, a maximum slope model.
  • the deconvolution model refers to a model that uses a residual function to determine perfusion parameters.
  • the residual function refers to a curve of a contrast agent that resides in the organ tissue of the test subject over time after a unit amount of the contrast agent is injected into the test subject.
  • the deconvolution model may include, but is not limited to, a singular value decomposition deconvolution model, an AATH model (Adiabatic Approximation to Tissue Homogeneity), or a DP model (Distributed Parameter).
  • a perfusion parameter map can be generated from the perfusion parameters by the perfusion analysis device 130.
  • the perfusion parameter map may be generated based on the acquired scan image of the selected region of interest and the perfusion parameter value corresponding to each pixel or voxel point in the image.
  • different perfusion parameter values can be represented in different colors.
  • a color may represent a perfusion parameter value or a perfusion parameter range.
  • the computer can be a general purpose computer or a computer with a specific purpose.
  • Perfusion The device 130 can be implemented by the computer device architecture through its hardware devices, software programs, firmware, and combinations thereof. For ease of illustration, only one computer device architecture is depicted in FIG. 3, but the associated computer functions of the perfusion analysis device 130 may be distributed across multiple computer devices.
  • the computer device architecture can include a communication port 330 to which a network (eg, network 120 in FIG. 1) can be implemented for data communication.
  • the computer device architecture may also include a central processing system (CPU) unit 340 for executing program instructions comprised of one or more processors.
  • the computer device architecture includes an internal communication bus 370, different forms of program storage units and data storage units, such as a hard disk 310, a read only memory (ROM) 350, a random access memory (RAM) 360, which can be configured as a storage device.
  • the computer device architecture can also include an input/output component 320 that supports data and/or information interaction between the computer device architecture and an external (eg, user).
  • the computer device architecture can also accept programs and data over a communication network.
  • the perfusion analysis device 130 can include, but is not limited to, an image acquisition module 410, a discrete point acquisition module 420, a curve determination module 430, a first model acquisition module 440, a second model acquisition module 450, and a first parameter determination module. 460 and a second parameter determination module 470.
  • the image acquisition module 410, the discrete point acquisition module 420, the curve determination module 430, the first model acquisition module 440, the second model acquisition module 450, the first parameter determination module 460, and the second parameter determination module 470 may be as shown in FIG.
  • the computer is implemented by the CPU 340.
  • the image acquisition module 410 can acquire a scanned image.
  • the scanned image may include, but is not limited to, a CT image, an MRI image, or a PET image.
  • the scanned image may be a two-dimensional image or a three-dimensional image.
  • the scanned image may include a scanned image that detects selected layers of the region of interest of the object at different points in time.
  • imaging device 110 may scan selected layers of the same region of interest at different points in time to obtain scan data corresponding to different points in time.
  • the image obtaining module 410 may reconstruct, according to the scan data, scan images corresponding to different time points.
  • image acquisition module 410 can retrieve scanned images corresponding to different points in time from a storage device (eg, database 140).
  • the intervals of adjacent time points may be equal or unequal.
  • the time point interval and the number of time points may be default values of the perfusion analysis device 130, or may be settings of a user (eg, a doctor, imaging technician, etc.).
  • the discrete point acquisition module 420 can acquire discrete points of time concentration.
  • the discrete point acquisition module 420 can The time concentration discrete points are acquired according to the scanned images corresponding to the acquired different time points.
  • the time concentration discrete point represents the contrast agent concentration in the organ tissue corresponding to a certain pixel or voxel point in the scanned image at a certain time point.
  • the time concentration discrete point may include a discrete point of the time concentration of the input blood vessel, a discrete point of the time concentration of the output blood vessel, and a discrete point of the time concentration of the tissue.
  • the discrete concentration of time concentration of the tissue can represent the concentration of contrast agent in the organ tissue of the region of interest at a certain point in time.
  • the time concentration discrete point of the input blood vessel may represent the contrast agent concentration in the blood vessel that supplies blood to the organ tissue of the region of interest at a certain point in time.
  • the input blood vessels may include, but are not limited to, an artery that supplies blood to organ tissues, for example, a anterior cerebral artery, a middle cerebral artery, and the like.
  • the discrete concentration of the time concentration of the output vessel can indicate the contrast agent concentration in the output vessel at a certain point in time.
  • the output blood vessels can include, but are not limited to, veins.
  • the contrast agent concentration can be obtained by measuring the CT value.
  • the CT value can refer to a unit of measure for determining the density of a local tissue or organ, and is related to the linear absorption coefficient of X-rays by various tissues.
  • the unit of the CT value can be expressed as HU (Hounsfield Unit).
  • HU Hounsfield Unit
  • the iodine concentration of 1 mg/ml is equivalent to 25 HU, that is, 1 mg of iodine can increase the CT value of 1 ml of organ tissue by 25 HU. Therefore, the amount of iodine accumulated per ml of organ tissue can be obtained by the change in the CT value of the organ tissue, thereby obtaining the concentration of the iodine preparation.
  • the curve determination module 430 can determine an initial time concentration curve based on discrete points of time concentration.
  • the initial time concentration curve represents a time concentration curve obtained by connecting adjacent two time concentration discrete points by a straight line or a curved line.
  • the first model acquisition module 440 can acquire the first perfusion model.
  • the first perfusion model may include, but is not limited to, a Singular Value Decomposition (SVD) deconvolution model and/or a maximum slope model.
  • SVD Singular Value Decomposition
  • the first parameter determination module 450 can determine the first perfusion parameter based on the first perfusion model and the initial time concentration curve.
  • the first perfusion parameter may include, but is not limited to, a combination of one or more of Blood Flow (BF), Blood Volume (BV), Mean Transition Time (MTT), and the like.
  • the second model acquisition module 460 can acquire the second perfusion model.
  • the second perfusion model can include, but is not limited to, an AATH model (Adiabatic Approximation to Tissue Homogeneity) and/or a DP model (Distributed Parameter).
  • the second parameter determination module 470 can determine the second perfusion parameter based on the second perfusion model and the first perfusion parameter.
  • the second perfusion parameter can include, but is not limited to, Blood Flow (BF), blood volume (Blood) A combination of one or more of Volume, BV), Mean Transition Time (MTT), Permeability Surface (PS), and the like.
  • the perfusion analysis device 130 can further include a storage module.
  • each module may share a single storage module, and each module may also have its own storage module. Variations such as these are within the scope of the present application.
  • Figure 5 is an exemplary flow chart for determining a second perfusion parameter, in accordance with some embodiments of the present application.
  • the image acquisition module 410 can acquire a scanned image.
  • the scanned image may include, but is not limited to, a CT image, an MRI image, or a PET image.
  • the scanned image may be a two-dimensional image or a three-dimensional image.
  • the scanned image may include a scanned image that detects selected layers of the region of interest of the object at different points in time.
  • the discrete point acquisition module 420 can acquire a time concentration discrete point from the scanned image.
  • the time concentration discrete point may represent a contrast agent concentration in an organ tissue corresponding to a certain pixel or voxel point in the scanned image at a certain time point.
  • the discrete point acquisition module 420 can acquire discrete points in time concentration of input blood vessels (eg, input arteries) and discrete points in time concentration of tissue.
  • the curve determination module 430 can determine an initial time concentration curve based on the time concentration discrete points.
  • the initial time concentration curve (as shown in Figure 7) can be determined by connecting two adjacent time concentration discrete points by a straight line or a curve.
  • the initial time concentration time curve determined according to the time concentration discrete point of the input blood vessel may be an initial input blood vessel time concentration curve
  • the initial time concentration time curve determined according to the tissue time concentration discrete point of the tissue may be an initial tissue time concentration curve.
  • the first model acquisition module 440 can acquire the first perfusion model.
  • the first perfusion model may include, but is not limited to, a Singular Value Decomposition (SVD) deconvolution model and/or a maximum slope model.
  • SVD Singular Value Decomposition
  • the first parameter determination module 450 can determine the first perfusion parameter based on the first perfusion model and the initial time concentration curve.
  • the first perfusion parameter may include, but is not limited to, a combination of one or more of Blood Flow (BF), Blood Volume (BV), Mean Transition Time (MTT), and the like.
  • the first parameter determination module 450 can determine blood volume by the SVD deconvolution model and the initial time concentration curve. Specifically, the first parameter determination module 450 may obtain a residual function curve by using an SVD deconvolution model. Based on the residual function curve, the first parameter determination module 450 can determine blood flow, average transit time, delay time, and uptake constant. Based on the blood flow and the average transit time, the first parameter determination module 450 can determine the blood volume.
  • the residual function may refer to a curve of contrast agent residing in organ tissue over time after a unit amount of contrast agent is injected into the test subject.
  • the residual function can be a function related to the perfusion parameter.
  • the residual function can be expressed by equation (1):
  • r(t) is the residual function
  • BF is the blood flow
  • MTT is the mean transit time
  • BV is the blood volume
  • PS is the capillary surface permeability
  • E is the uptake constant
  • T 0 is the delay time.
  • the initial tissue time concentration curve and the initial input blood vessel time concentration curve may be used first, and the residual function curve is obtained according to the SVD deconvolution model, and then the residual function curve is divided into the delay segment and the blood vessel according to the characteristics of the residual function curve. Inner segment and extravascular segment.
  • the delay period may be the delay time T 0
  • the peak blood flow in the blood vessel may be the blood flow BF
  • the duration of the intravascular segment may be the mean transit time MTT
  • the ratio of the mean blood flow of the extravascular segment to the peak blood flow of the intravascular segment may be
  • the uptake constant E, the product of the blood flow rate and the average transit time, may be the blood volume BV.
  • the first parameter determination module 450 can determine blood flow, blood volume, and average transit time based on an initial tissue time concentration curve, an initial input vessel time concentration curve, and a maximum slope model. Specifically, when the first perfusion parameter is determined using the maximum slope model, it can be assumed that the contrast agent flowing through the organ tissue has no vascular outflow. Under this premise, the ratio of the area under the initial tissue time concentration curve to the area under the initial input vessel time concentration curve may be the blood volume BV, and the ratio of the maximum slope of the rising phase of the initial tissue time concentration curve to the peak value of the initial input vessel time concentration curve may be For blood flow BF, the ratio of blood volume to blood flow may be the mean transit time MTT.
  • the second model acquisition module 460 can acquire the second perfusion model.
  • the second perfusion model may include an AATH model (Adiabatic Approximation to Tissue Homogeneity) and/or a DP model (Distributed Parameter).
  • the second parameter determination module 470 can determine the second perfusion parameter based on the second perfusion model and the first perfusion parameter.
  • the second perfusion parameter may include, but is not limited to, Blood Flow (BF), Blood Volume (BV), Mean Transition Time (MTT), Permeability Surface (PS), etc. One or a combination of several.
  • determining the operation of the second perfusion parameter according to the second perfusion model and the first perfusion parameter may include: determining an objective function; performing a loop iteration using the objective function and the second perfusion model to determine a second perfusion parameter.
  • the first perfusion parameter can be used as an initial value in the iterative process, that is, the substitution value of the first iteration process.
  • Figure 6 is an exemplary flow chart for determining a second perfusion parameter, in accordance with some embodiments of the present application.
  • the process 600 can be used to determine a second perfusion parameter of step 570 in the process 500 based on the second perfusion model and the first perfusion parameter.
  • the second parameter determination module 470 can determine the objective function.
  • the objective function can be used to determine an equation solution that satisfies the requirements of a user (eg, a doctor or imaging technician), ie, a second perfusion parameter.
  • the objective function can be derived from a least squares method.
  • the least squares method refers to determining the solution of the equation according to the principle that the sum of squared residuals is the smallest. Residual is the difference between the data point and the corresponding position on the curve or line obtained from the data point simulation.
  • the sum of squared residuals is the sum of the squares after each residual is squared.
  • the objective function can include a sum of squared residuals and/or a sum of squared mean residuals.
  • the sum of squared residuals may refer to the sum of squared residuals divided by the number of time points.
  • the objective function determined according to the least squares method can be expressed by equation (2):
  • C tiss (t i ) represents the initial tissue time concentration curve
  • t i represents a time point
  • N represents the number of time points
  • p represents a perfusion parameter corresponding to the second perfusion model
  • the tissue time concentration curve based on the AATH model may be a convolution of the initial input vessel time concentration curve with the residual function.
  • the tissue time concentration curve based on the AATH model can be expressed by the formula (3):
  • C art (t) represents the initial input vessel time concentration curve
  • r(t) denotes a residual function, which can be expressed by the formula (1)
  • r(t) satisfies the following conditions:
  • the second parameter determination module 470 can determine an estimated perfusion parameter.
  • the predicted perfusion parameter may represent a perfusion parameter that is substituted into the residual function during each iteration.
  • the first perfusion parameter may be substituted into the residual function as an estimated perfusion parameter during the first iteration.
  • the second parameter determination module 470 can determine the perfusion parameters and the The two perfusion model determines the objective function value.
  • the process of determining the objective function value may include substituting the estimated perfusion parameter into the residual function, according to the residual function and The initial input vessel time concentration curve determines the tissue time concentration curve based on the AATH model, and then determines the sum of squared residuals of the tissue time concentration curve and the initial tissue time concentration curve based on the AATH model.
  • the sum of squared residuals may be the sum of the squares of the difference in contrast concentration at each time point based on the tissue time concentration curve of the AATH model and the initial tissue time concentration curve.
  • the second parameter determination module 470 can determine if the end condition is met. If the end condition is met, then step 650 is entered to determine the estimated perfusion parameter substituted into the residual function during the iteration process as the second perfusion parameter. If the end condition is not met, then step 620 is entered to enter a new iterative process to determine a new estimated perfusion parameter.
  • the estimated perfusion parameters for this iterative process can be determined from the estimated perfusion parameters of the last iteration process. For example, the estimated perfusion parameters during the second iteration may be determined based on the first perfusion parameter substituted into the residual function during the first iteration.
  • the method of determining an estimated perfusion parameter can include adjusting a value of the predicted perfusion parameter within a preset range of perfusion parameters to reduce an objective function value (eg, a sum of squared residuals).
  • the predetermined range may include a normal range of values for the perfusion parameter, for example, the normal value of blood flow to the brain artery may range from 0.1 to 0.9 ml/min/ml.
  • the predicted perfusion parameters can be determined using the Levenberg-Marquardt model.
  • the end condition may include, but is not limited to, an average residual sum squared less than the first threshold and/or an adjusted value of each of the estimated perfusion parameters of the two successive iterations is less than the second Threshold.
  • the sum of squared residuals is the sum of squared residuals divided by the number of time points.
  • the adjustment value refers to the absolute value of the difference of the estimated perfusion parameters of the two successive iterations.
  • the first threshold may be 0.01.
  • the second threshold may be 0.001.
  • Figure 7 is a graph of the fit effect of a tissue time concentration curve, in accordance with some embodiments of the present application.
  • the abscissa in Fig. 7 indicates the time point at which the imaging device scans the selected layer of the region of interest of the detection object, and the ordinate indicates the organ tissue CT of the region of interest after the addition of the contrast agent.
  • the value of the change in value. 710 represents one of the discrete points of tissue time concentration.
  • Curve 1 represents an initial tissue time concentration curve determined from discrete points 710 of a plurality of tissue time concentrations.
  • Curve 2 represents the initial input vessel time concentration curve determined from the discrete points of the input vessel time concentration (not shown).
  • Curve 3 represents a tissue time concentration curve based on the second perfusion model obtained by simulation according to the second perfusion model.
  • FIG. 8 is a schematic diagram of verification of a second perfusion parameter result.
  • the second perfusion model takes the AATH model as an example.
  • the second perfusion parameter result verification method may include: artificially setting the perfusion parameter, then substituting the set perfusion parameter into the residual function, and rolling the residual function with the initial input vessel time concentration curve.
  • the ideal tissue time concentration curve is obtained, and the interference signal existing in the random noise simulation actual data is added to the ideal tissue time concentration curve to generate a simulated tissue time concentration curve (for example, the data signal to noise ratio is 10 dB), and the simulation tissue time is simulated.
  • the concentration curve is used as an initial tissue time concentration curve, and the method for obtaining perfusion parameters involved in the process 500 and/or the process 600 is obtained according to the initial tissue time concentration curve, the initial input blood vessel time concentration curve, the first perfusion model, and the second perfusion model.
  • the calculated value of the perfusion parameter is compared and the calculated value of the perfusion parameter is compared to the set value to verify the accuracy of the method of determining the perfusion parameter.
  • the abscissa is the set value of blood flow (BF), and the ordinate is the calculated value of blood flow (BF).
  • 0.1, 0.4, 0.7, 0.9 can be selected as the set value of blood flow (BF).
  • a data point (eg, 810) may represent a set value of blood flow and a blood flow calculation value corresponding to the blood flow set value.
  • the perfusion analysis device 130 can perform a plurality of (eg, 500) repeated tests in accordance with the process 500 and/or the process 600 when determining a blood flow calculation value corresponding to the blood flow setpoint. The calculated value of the blood flow can be obtained according to the repeated test results.
  • the calculated value may be an average of repeated test results.
  • 820 can represent the standard deviation of multiple test results corresponding to the calculated values of the data points 810 shown.
  • Half of the 820 height can represent the standard deviation.
  • the height of 820 can be determined based on, for example, a coordinate system as shown in FIG. For example, in the coordinate system shown in FIG. 8, the height of 820 is 0.08, and the standard deviation of the calculated value corresponding to the data point 810 is 0.04.
  • the standard deviation can refer to the square root of the arithmetic mean of the sum of the squares of the deviations of the values of a set of values from their mean.
  • the standard deviation can reflect the degree of dispersion between individuals within a group.
  • the sum of squared deviations can refer to the sum of the squares of the differences between the values of a set of values and their average. As can be seen from Fig. 8, the consistency between the set value of the blood flow and the calculated value is high, indicating that the method for determining the perfusion parameter involved in the present application is highly accurate.
  • Figures 9-A through 9-D are brain tumor perfusion parameter maps obtained from a second perfusion parameter.
  • Figure 9-A is a perfusion parameter diagram of blood flow
  • Figure 9-B is a perfusion parameter diagram of blood volume
  • Figure 9-C is a perfusion parameter diagram of mean transit time
  • Figure 9-D is a perfusion parameter diagram of surface permeability.
  • tumor blood vessels can have high microvascular density, high blood flow, high blood volume and high permeability.
  • 910 is a brain tumor, and the capillary surface permeability at the brain tumor obtained by the method for determining perfusion parameters involved in the present application is significantly increased relative to other brain parenchymal tissues.
  • the first threshold and the second threshold involved in the present application may be a range of values or a specific value.
  • the first threshold and the second threshold may be determined based on historical data, default values of perfusion imaging system 100, or user (eg, doctor, imaging technician, etc.) instructions.
  • Tangible, permanent storage media includes the memory or memory used by any computer, processor, or similar device or associated module. For example, various semiconductor memories, tape drives, disk drives, or the like that can provide storage functions for software at any time.
  • All software or parts of it may sometimes communicate over a network, such as the Internet or other communication networks.
  • Such communication can load software from one computer device or processor to another.
  • a hardware platform loaded from a management server or host computer of an imaging system to a computer environment, or other computer environment implementing the system, or a similar function related to the information required to provide on-demand services. Therefore, another medium capable of transmitting software elements can also be used as a physical connection between local devices, such as light waves, electric waves, electromagnetic waves, etc., through cable, fiber optic cable or air.
  • Physical media used for carrier waves such as cables, wireless connections, or fiber optic cables can also be considered as media for carrying software.
  • a computer readable medium can take many forms, including but not limited to, a tangible storage medium, carrier medium or physical transmission medium.
  • Stable storage media include: optical or magnetic disks, as well as storage systems used in other computers or similar devices that enable the implementation of the system components described in the figures.
  • Unstable storage media include dynamic memory, such as the main memory of a computer platform.
  • Tangible transmission media include coaxial cables, copper cables, and fiber optics, including the circuitry that forms the bus within the computer system.
  • the carrier transmission medium can transmit electrical signals, electromagnetic signals, acoustic signals or optical signals, which can be generated by radio frequency or infrared data communication methods.
  • Typical computer readable media include hard disks, floppy disks, magnetic tape, any other magnetic media; CD-ROM, DVD, DVD-ROM, any other optical media; perforated cards, any other physical storage media containing aperture patterns; RAM, PROM , EPROM, FLASH-EPROM, any other memory slice or tape; a carrier, cable or carrier for transmitting data or instructions, any other program code and/or data that can be read by a computer. Many of these forms of computer readable media appear in the process of the processor executing instructions, passing one or more results.

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Abstract

本申请提供了一种灌注分析方法。该方法可以包括：获取扫描图像，其中所述扫描图像包括一个或多个时间点对应的一个或多个图像；根据所述扫描图像，获取时间浓度离散点；根据所述时间浓度离散点，获取初始时间浓度曲线，所述初始时间浓度曲线可以表示所述扫描图像中某个像素或体素点对应的器官组织中的对比剂浓度随时间的变化情况；获取第一灌注模型；根据所述第一灌注模型以及所述初始时间浓度曲线，确定第一灌注参数；获取第二灌注模型；以及根据所述第二灌注模型和所述第一灌注参数，确定第二灌注参数。

Description

一种灌注分析方法与设备
交叉引用
本申请要求以下申请的优先权：
2016年2月29日提交的编号为CN201610112593.X的中国申请；
上述申请的内容以引用方式被包含于此。
技术领域
本申请涉及医学影像领域，尤其涉及一种灌注分析方法及设备。
背景技术
灌注可以指血流通过毛细血管网，将携带的氧和代谢物质等输送给器官的组织细胞的重要功能。通过对器官的灌注测量，可以反映其微循环血流动力学状态，进而了解其功能情况。灌注成像可以通过影像学技术，借助对比剂测量器官血液灌注参数，通过灌注参数了解器官微循环和功能状态。在灌注成像中，确定灌注参数的过程可以称作灌注分析。现有的灌注分析方法存在处理速度不够快以及容易受到噪声影响的问题，因此，需要提供一种灌注分析方法及设备，能够对现有的灌注分析方法加以改善，提高获取灌注参数的速度和准确性。
简述
根据本申请的一个方面，提供了一种灌注分析方法。该方法可以包括：获取扫描图像，其中所述扫描图像包括一个或多个时间点对应的一个或多个图像；根据所述扫描图像，获取时间浓度离散点；根据所述时间浓度离散点，获取初始时间浓度曲线，所述初始时间浓度曲线可以表示所述扫描图像中某个像素或体素点对应的器官组织中的对比剂浓度随时间的变化情况；获取第一灌注模型；根据所述第一灌注模型以及所述初始时间浓度曲线，确定第一灌注参数；获取第二灌注模型；以及根据所述第二灌注模型和所述第一灌注参数，确定第二灌注参数。
在一些实施例中，所述初始时间浓度曲线可以包括初始输入血管时间浓度曲线和初始组织时间浓度曲线。
在一些实施例中，所述第一灌注模型可以包括奇异值模型或最大斜率模型。
在一些实施例中，根据所述第一灌注模型以及所述初始时间浓度曲线，确定所述第一灌注参数可以包括：通过奇异值模型去卷积得到残余函数曲线；以及根据所述残余函数曲线确定所述第一灌注参数。
在一些实施例中，根据所述残余函数曲线确定所述第一灌注参数可以包括：将所述残余函数曲线按照时间顺序分为延迟段、血管内段和血管外段；以及根据所述延迟段、所述血管内段和所述血管外段确定所述第一灌注参数。
在一些实施例中，根据所述第一灌注模型以及所述初始时间浓度曲线，确定所述第一灌注参数可以包括：根据所述初始输入血管的时间浓度曲线和所述初始组织的时间浓度曲线确定所述初始输入血管的时间浓度曲线下面积、所述初始组织的时间浓度曲线下面积、所述初始组织的时间浓度曲线上升段的最大斜率和所述初始输入血管的时间浓度曲线峰值；以及根据所述初始输入血管的时间浓度曲线下面积、所述初始组织的时间浓度曲线下面积、所述初始组织的时间浓度曲线上升段的最大斜率和所述初始输入血管的时间浓度曲线峰值确定所述第一灌注参数。
在一些实施例中，所述第一灌注参数可以包括血容量、血流量和平均通过时间。
在一些实施例中，根据所述第二灌注模型和所述第一灌注参数，确定所述第二灌注参数可以包括：a.确定目标函数；b.确定预估灌注参数；c.根据所述目标函数、所述预估灌注参数和所述第二灌注模型，确定目标函数值；以及d.判断所述目标函数值是否满足结束条件，如果所述目标函数值满足所述结束条件，可以将所述预估函数确定为第二灌注参数，如果所述目标函数值不满足所述结束条件，可以执行b-d，进行新一轮迭代，其中，第一次迭代过程中，所述预估灌注参数可以为所述第一灌注参数。
在一些实施例中，所述目标函数可以表示残差平方和和/或平均残差平方和，所述残差平方和可以表示基于第二灌注模型的组织时间浓度曲线和所述初始组织浓度曲线在所述一个或多个时间点的对比剂浓度差值的平方和，所述平均残差平方和可以表示所述残差平方和与时间点数的比值。
在一些实施例中，所述基于第二灌注模型的组织的时间浓度曲线可以指所述初始输入血管时间浓度曲线与残余函数的卷积，所述残余函数可以为与灌注参数相关的函数。
在一些实施例中，所述结束条件可以包括以下条件中的至少一种：所述平均残差平方和小于第一阈值；相邻两次迭代过程的所述预估灌注参数的差值的绝对值小于第二阈值。
在一些实施例中，确定所述预估灌注参数可以包括：根据上一次迭代过程的预估灌注参数，利用预估模型在灌注参数的预设范围内确定可行下降方向，根据所述可行下降方向，确定所述预估灌注参数，其中，所述预估模型可以包括Levenberg-Marquardt模型。
在一些实施例中，所述第二灌注模型可以包括AATH模型(Adiabatic Approximation to Tissue Homogeneity)和/或DP模型(Distributed Parameter model)。
在一些实施例中，所述第二灌注参数可以包括血流量、血容量、平均通过时间和毛细血管表面渗透性。
根据本申请的另一个方面，提供了一种灌注分析设备。该设备可以包括：一种计算机可读的存储媒介，被配置为存储可执行模块；以及一个处理器，所述处理器能够执行所述计算机可读的存储媒介存储的可执行模块。所述可行执行模块可以包括：图像获取模块，被配置为获取扫描图像，其中所述扫描图像可以包括一个或多个时间点对应的一个或多个图像；离散点获取模块，被配置为根据所述扫描图像，获取时间浓度离散点；曲线确定模块，被配置为根据所述时间浓度离散点，获取初始时间浓度曲线，所述初始时间浓度曲线可以表示所述扫描图像中某个像素或体素点对应的器官组织中的对比剂浓度随时间的变化情况；第一模型获取模块，被配置为获取第一灌注模型；第一参数确定模块，被配置为根据所述第一灌注模型以及所述初始时间浓度曲线，确定第一灌注参数；第二模型获取模块，被配置为获取第二灌注模型；以及第二参数确定模块，被配置为根据所述第二灌注模型和所述第一灌注参数，确定第二灌注参数。
附图描述
在此所述的附图用来提供对本申请的进一步理解，构成本申请的一部分，本申请的示意性实施例及其说明用于解释本申请并不构成对本申请的限定。在各图中，相同标号表示相同部件。
根据本申请的一些实施例，图1是一个灌注成像系统的示意图。
根据本申请的一些实施例，图2是灌注成像的一种示例性流程图。
根据本申请的一些实施例，图3是灌注分析设备的一种计算机设备的架构。
根据本申请的一些实施例，图4是灌注分析设备的一种示例性示意图。
根据本申请的一些实施例，图5是确定第二灌注参数的一种示例性流程图。
根据本申请的一些实施例，图6是确定第二灌注参数的一种示例性流程图。
根据本申请的一些实施例，图7是组织时间浓度曲线的拟合效果图。
根据本申请的一些实施例，图8是第二灌注参数结果验证示意图。
根据本申请的一些实施例，图9-A至图9-D是根据第二灌注参数得到的脑肿瘤灌注参数图。
具体描述
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发明，而并非以任何方式限制本发明的范围。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。
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他的步骤或元素。
虽然本申请对根据本申请的实施例的系统中的某些模块做出了各种引用，然而，任何数量的不同模块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，前面或下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种步骤。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。
根据本申请的一些实施例，图1所示的是一个灌注成像系统的示意图。灌注可以指血流通过毛细血管网，将携带的氧和代谢物质等输送给器官的组织细胞的重要功能。通过对器官的灌注测量，可以反映其微循环血流动力学状态，进而了解其功能情况。灌注成像可以通过影像学技术，借助对比剂测量器官血液灌注情况，得到灌注参数和灌注参数图，从而了解器官微循环和功能状态。在灌注成像中，确定灌注参数的过程可以称作灌注分析。灌注成像系统100可以包括一个或多个成像 设备110、一个或多个网络120、一个或多个灌注分析设备130、一个或多个数据库140。
成像设备110可以对检测对象进行扫描，得到扫描数据，所述扫描数据可以通过网络120发送到灌注分析设备130进行进一步处理，也可以通过网络120直接存储到数据库140。所述检测对象可以包括人体、动物等。成像设备110可以包括但不限于计算机断层成像(Computed Tomography，CT)设备、磁共振成像(Magnetic Resonance Imaging，MRI)设备或正电子发射断层成像(Positron Emission Computed Tomography，PET)设备。
灌注分析设备130可以对输入的数据(例如，成像设备110得到的和/或数据库140中存储的扫描数据、扫描图像)进行处理、分析以生成处理结果。例如，灌注分析设备130可以根据扫描数据生成扫描图像。又例如，灌注分析设备130可以对扫描图像进行处理、分析，得到灌注参数结果和/或生成灌注参数图。所述扫描图像可以是二维图像，也可以是三维图像。灌注分析设备130可以包括处理器和输入/输出装置(图中未画出)。在一些实施例中，所述处理器可以是一个服务器，也可以是一个服务器群组。一个服务器群组可以是集中式的，例如数据中心。一个服务器群组也可以是分布式的，例如一个分布式系统。所述处理器可以是云服务器、文件服务器、数据库服务器、File Transfer Protocol(FTP)服务器、应用程序服务器、代理服务器、邮件服务器等中的一种或几种的组合。所述处理器可以是本地的，也可以是远程的。本地处理器可以包括集成在灌注分析设备130中的处理器。远程处理器可以包括通过网络(例如，网络120)与灌注分析设备130连接的处理器。在一些实施例中，所述处理器可以访问数据库140中存储的信息(例如，存储在数据库140中的医学图像)、成像设备110中的信息(例如，成像设备110拍摄的医学图像)。在一些实施例中，所述输入/输出装置可以向所述处理器输入数据，也可以接收所述处理器输出的数据，并将输出的数据以数字、字符、图像、录像，动画，声音等形式表示出来。在一些实施例中，所述输入/输出装置可以包括但不限于输入装置、输出装置等中的一种或几种的组合。所述输入装置可以包括但不限于字符输入装置(例如，键盘)、光学阅读装置(例如，光学标记阅读机、光学字符阅读机)、图形输入装置(例如，鼠标器、操作杆、光笔)、图像输入装置(例如，摄像机、扫描仪、传真机)、模拟输入装置(例如，语言模数转换识别系统)等中的一种或几种的组合。所述输出装置可以包括但不限于显示装置、打印装置、绘图仪、影像 输出装置、语音输出装置、磁记录装置等中的一种或几种的组合。在一些实施例中，灌注分析设备130可以进一步包括存储装置(图中未画出)。所述存储装置可以存储各种信息，例如，程序和数据等。在一些实施例中，灌注分析设备130产生的数据和/或处理结果(例如，扫描图像、灌注参数、灌注参数图等)可以存储在数据库140和/或灌注分析设备130的存储装置中，也可以通过输入/输出装置输出。
数据库140可以泛指具有存储功能的设备。数据库140可以存储从成像设备110收集的扫描数据和灌注分析设备130工作中产生的各种数据。数据库140可以是本地的，也可以是远程的。本地数据库可以包括集成在数据库140中的具有存储功能的设备。远程数据库可以包括通过网络(例如，网络120)与数据库140连接的具有存储功能的设备。数据库140可以包括但不限于层次式数据库、网络式数据库和关系式数据库等其中的一种或几种的组合。数据库140可以将信息数字化后再以利用电、磁或光学等方式的存储设备加以存储。数据库140可以用来存放各种信息，例如程序和数据等。数据库140可以是利用电能方式存储信息的设备，例如各种存储器、随机存取存储器(Random Access Memory(RAM))、只读存储器(Read Only Memory(ROM))等。其中随机存储器包括但不限于十进计数管、选数管、延迟线存储器、威廉姆斯管、动态随机存储器(DRAM)、静态随机存储器(SRAM)、晶闸管随机存储器(T-RAM)、零电容随机存储器(Z-RAM)等中的一种或几种的组合。只读存储器包括但不限于磁泡存储器、磁钮线存储器、薄膜存储器、磁镀线存储器、磁芯内存、磁鼓存储器、光盘驱动器、硬盘、磁带、早期非易失存储器(NVRAM)、相变化内存、磁阻式随机存储式内存、铁电随机存储内存、非易失SRAM、闪存、电子抹除式可复写只读存储器、可擦除可编程只读存储器、可编程只读存储器、屏蔽式堆读内存、浮动连接门随机存取存储器、纳米随机存储器、赛道内存、可变电阻式内存、可编程金属化单元等中的一种或几种的组合。数据库140可以是利用磁能方式存储信息的设备，例如硬盘、软盘、磁带、磁芯存储器、磁泡存储器、U盘、闪存等。数据库140可以是利用光学方式存储信息的设备，例如CD或DVD等。数据库140可以是利用磁光方式存储信息的设备，例如磁光盘等。数据库140的存取方式可以是随机存储、串行访问存储、只读存储等中的一种或几种的组合。数据库140可以是非永久记忆存储器，也可以是永久记忆存储器。以上提及的存储设备只是列举了一些例子，在灌注成像系统100中可以使用的数据库并不局限于此。
网络120可以是单一网络，也可以是多种网络的组合。网络120可以包括但不限于局域网、广域网、公用网络、专用网络、无线局域网、虚拟网络、都市城域网、公用开关电话网络等中的一种或几种的组合。网络120可以包括多种网络接入点，如有线或无线接入点、基站或网络交换点，通过以上接入点使数据源连接网络120并通过网络发送信息。
需要注意的是，以上对于灌注成像系统的描述，仅为示例，并不能把本申请限制在所举实施例范围之内。可以理解，对于本领域的技术人员来说，在了解该系统的原理后，可能在不背离这一原理的情况下，对各个模块进行任意组合，或者构成子系统与其他模块连接，对实施上述方法和系统的应用领域形式和细节上的各种修正和改变。例如，在一些实施例中，数据库140可以是具有数据存储功能的云计算平台，包括但不限于公用云、私有云、社区云和混合云等。诸如此类的变形，均在本申请的保护范围之内。
根据本申请的一些实施例，图2所示的是灌注成像的一种示例性流程图。在步骤210中，可以向检测对象注射对比剂。所述检测对象可以包括人体、动物等。在灌注成像中，通过跟踪所述对比剂在检测对象体内的流动情况，可以了解血液循环情况以及器官微循环和功能状态。所述对比剂可以包括高密度对比剂或低密度对比剂。高密度对比剂可以包括硫酸钡或碘制剂。碘制剂可以包括无机碘化物、有机碘化物、碘化油或脂肪酸碘化物。有机碘化物可以包括离子型碘制剂、非离子型碘制剂或非离子型二聚体碘制剂。在一些实施例中，可以向检测对象静脉注射对比剂。在一些实施例中，检测对象也可以通过口服对比剂(例如，硫酸钡)使对比剂进入检测对象体内。
在步骤220中，可以通过成像设备110对检测对象进行扫描。在一些实施例中，可以对检测对象的感兴趣区域进行扫描。所述感兴趣区域可以是检测对象的整体或其中的一部分，例如，头部、胸部、腹部、心脏、肝脏、上肢、下肢、脊椎、骨骼、血管、病变部位、肿瘤部位等，或者上述部位的任意组合。在一些实施例中，可以对感兴趣区域的选定层面采集多个时间点的扫描图像，以记录该选定层面器官组织中对比剂浓度随时间的变化情况。器官组织中对比剂浓度随时间的变化情况可以用时间浓度曲线(Time-Density Curve，TDC)表示。时间浓度曲线表示扫描图像中某一像素或体素点对应的器官组织中对比剂浓度随时间的变化情况。根据时间浓度曲线对应的器官组织，时间浓度曲线可以包括输入血管时间浓度曲线、输出血管时间浓度曲线 和组织时间浓度曲线。组织时间浓度曲线可以表示感兴趣区域器官组织中对比剂浓度随时间的变化情况。输入血管时间浓度曲线可以表示为所述感兴趣区域器官组织供血的血管中对比剂的浓度随时间的变化情况。输入血管可以包括但不限于为器官组织供血的动脉，例如，大脑前动脉、大脑中动脉等。输出血管时间浓度曲线可以表示输出血管中对比剂的浓度随时间的变化情况。输出血管可以包括但不限于静脉。
在步骤230中，可以通过灌注分析设备130确定灌注参数。在一些实施例中，可以根据时间浓度曲线，利用灌注模型确定所述灌注参数。所述灌注参数可以指血流动力学的有关参数。所述灌注参数可以包括但不限于血流量(Blood Flow，BF)、血容量(Blood Volume，BV)、平均通过时间(Mean Transition Time，MTT)、毛细血管表面渗透性(Permeability Surface，PS)等中的一种或几种的组合。血流量指单位时间内流经感兴趣区域血管结构的血流总量。血容量指感兴趣区域内的血液总量。平均通过时间指血液流经感兴趣区域内血管结构的平均时间。感兴趣区域内血管结构可以包括动脉、毛细血管、静脉窦、静脉等。血液流经感兴趣区域的血管类别不同，其通过时间也不同。本申请可以用平均通过时间作为灌注参数之一。在一些实施例中，平均通过时间可以反映对比剂通过毛细血管的时间。毛细血管表面渗透性指对比剂经由毛细血管内皮进入细胞间隙的单向传递速率。一般情况下，肿瘤病变处的毛细血管表面渗透性要大于非肿瘤病变处的毛细血管表面渗透性。所以，毛细血管表面渗透性可以用于肿瘤病变的研究。所述灌注模型可以包括但不限于非去卷积模型和去卷积模型。所述非去卷积模型可以包括但不限于最大斜率模型。所述去卷积模型是指利用残余函数确定灌注参数的模型。所述残余函数指当单位量的对比剂被注入到检测对象中后，驻留在检测对象器官组织中的对比剂随着时间的变化曲线。所述去卷积模型可以包括但不限于奇异值分解去卷积模型、AATH模型(Adiabatic Approximation to Tissue Homogeneity)或DP模型(Distributed Parameter)。
在步骤240中，可以通过灌注分析设备130根据灌注参数生成灌注参数图。在一些实施例中，可以根据采集的感兴趣区域选定层面的扫描图像和图像中每个像素或体素点对应的灌注参数值生成灌注参数图。在灌注参数图中，不同的灌注参数值可以用不同的颜色表示。在一些实施例中，一种颜色可以表示一个灌注参数值，也可以表示一个灌注参数范围。
根据本申请的一些实施例，图3是灌注分析设备的一种计算机设备的架构。所述计算机可以是一个通用目的的计算机，或是一个有特定目的的计算机。灌注分 析设备130能够被所述计算机设备架构通过其硬件设备、软件程序、固件以及它们的组合所实现。为了示例方便，图3中只绘制了一台计算机设备的架构，但是灌注分析设备130的相关计算机功能可以由多个计算机设备分布式实施。
所述计算机设备架构可以包括通信端口330，与之相连的可以是实现数据通信的网络(例如，图1中网络120)。所述计算机设备架构还可以包括一个中央处理系统(CPU)单元340用于执行程序指令，由一个或多个处理器组成。所述计算机设备架构包括一个内部通信总线370，不同形式的程序储存单元以及数据储存单元，例如硬盘310，只读存储器(ROM)350，随机存取存储器(RAM)360，能够被配置为存储所述计算机设备架构处理和/或通信使用的各种数据文件，以及CPU单元340所执行的可能的程序指令。所述计算机设备架构还可以包括一个输入/输出组件320，支持所述计算机设备架构与外部(例如，用户)之间的数据和/或信息交互。所述计算机设备架构也可以通过通信网络接受程序及数据。
根据本申请的一些实施例，图4所示的是灌注分析设备的一种示例性示意图。灌注分析设备130可以包括但不限于一个图像获取模块410、一个离散点获取模块420、一个曲线确定模块430、一个第一模型获取模块440、一个第二模型获取模块450、一个第一参数确定模块460和一个第二参数确定模块470。图像获取模块410、离散点获取模块420、曲线确定模块430、第一模型获取模块440、第二模型获取模块450、第一参数确定模块460和第二参数确定模块470可以被如图3中的计算机通过CPU340所实现。
图像获取模块410可以获取扫描图像。所述扫描图像可以包括但不限于CT图像、MRI图像或PET图像。所述扫描图像可以是二维图像，也可以是三维图像。所述扫描图像可以包括检测对象感兴趣区域的选定层面在不同时间点的扫描图像。在一些实施例中，成像设备110可以在不同时间点对同一个感兴趣区域的选定层面进行扫描，得到不同时间点对应的扫描数据。图像获取模块410可以根据所述扫描数据重建得到不同时间点对应的扫描图像。在一些实施例中，图像获取模块410可以从存储设备(例如，数据库140)中获取不同时间点对应的扫描图像。相邻时间点的间隔可以是相等的，也可以是不等的。在一些实施例中，时间点间隔和时间点数量可以是灌注分析设备130的默认值，也可以是用户(例如，医生、成像技师等)的设定值。
离散点获取模块420可以获取时间浓度离散点。离散点获取模块420可以 根据获取的不同时间点对应的扫描图像获取时间浓度离散点。所述时间浓度离散点表示在某个时间点扫描图像中某个像素或体素点对应的器官组织中的对比剂浓度。根据时间浓度离散点对应的器官组织，时间浓度离散点可以包括输入血管的时间浓度离散点、输出血管的时间浓度离散点和组织的时间浓度离散点。组织的时间浓度离散点可以表示在某个时间点感兴趣区域器官组织中的对比剂浓度。输入血管的时间浓度离散点可以表示在某个时间点为所述感兴趣区域器官组织供血的血管中的对比剂浓度。输入血管可以包括但不限于为器官组织供血的动脉，例如，大脑前动脉、大脑中动脉等。输出血管的时间浓度离散点可以表示在某个时间点输出血管中的对比剂浓度。输出血管可以包括但不限于静脉。在CT灌注成像中，对比剂浓度可以通过测量CT值得到。CT值可以指测定某一局部组织或器官密度大小的一种计量单位，与各种组织对X射线的线性吸收系数相关。CT值的单位可以表示为HU(Hounsfield Unit)。例如，在CT灌注成像中，如果对比剂是碘制剂，1mg/ml的碘浓度相当于25HU，即1mg碘可以使1ml器官组织的CT值增加25HU。因此，可以通过器官组织CT值的变化得到每毫升器官组织内碘的聚集量，从而得到碘制剂的浓度。
曲线确定模块430可以根据时间浓度离散点，确定初始时间浓度曲线。初始时间浓度曲线表示通过直线或曲线连接相邻两个时间浓度离散点得到的时间浓度曲线。
第一模型获取模块440可以获取第一灌注模型。第一灌注模型可以包括但不限于奇异值分解(Singular Value Decomposition，SVD)去卷积模型和/或最大斜率模型。
第一参数确定模块450可以根据第一灌注模型和初始时间浓度曲线确定第一灌注参数。第一灌注参数可以包括但不限于血流量(Blood Flow，BF)、血容量(Blood Volume，BV)、平均通过时间(Mean Transition Time，MTT)等中的一种或几种的组合。
第二模型获取模块460可以获取第二灌注模型。第二灌注模型可以包括但不限于AATH模型(Adiabatic Approximation to Tissue Homogeneity)和/或DP模型(Distributed Parameter)。
第二参数确定模块470可以根据第二灌注模型和第一灌注参数确定第二灌注参数。第二灌注参数可以包括但不限于血流量(Blood Flow，BF)、血容量(Blood  Volume，BV)、平均通过时间(Mean Transition Time，MTT)、毛细血管表面渗透性(Permeability Surface，PS)等中的一种或几种的组合。
需要注意的是，以上对于灌注分析设备130的描述，仅为示例，并不能把本申请限制在所举实施例范围之内。可以理解，对于本领域的技术人员来说，在了解该系统的原理后，可能在不背离这一原理的情况下，对各个模块进行任意组合，或者构成子系统与其他模块连接。例如，在一些实施例中，灌注分析设备130可以进一步包括存储模块。例如，在一些实施例中，各个模块可以共用一个存储模块，各个模块也可以分别具有各自的存储模块。诸如此类的变形，均在本申请的保护范围之内。
根据本申请的一些实施例，图5是确定第二灌注参数的一种示例性流程图。在步骤510中，图像获取模块410可以获取扫描图像。所述扫描图像可以包括但不限于CT图像、MRI图像或PET图像。所述扫描图像可以是二维图像，也可以是三维图像。所述扫描图像可以包括检测对象感兴趣区域的选定层面在不同时间点的扫描图像。
在步骤520中，离散点获取模块420可以根据所述扫描图像获取时间浓度离散点。所述时间浓度离散点可以表示某个时间点扫描图像中某个像素或体素点对应的器官组织中的对比剂浓度。在一些实施例中，离散点获取模块420可以获取输入血管(例如，输入动脉)的时间浓度离散点和组织的时间浓度离散点。
在步骤530中，曲线确定模块430可以根据时间浓度离散点确定初始时间浓度曲线。在一些实施例中，可以通过直线或曲线连接相邻两个时间浓度离散点确定初始时间浓度曲线(如图7所示)。在一些实施例中，根据输入血管的时间浓度离散点确定的初始时间浓度时间曲线可以是初始输入血管时间浓度曲线，根据组织的时间浓度离散点确定的初始时间浓度时间曲线可以是初始组织时间浓度曲线。
在步骤540中，第一模型获取模块440可以获取第一灌注模型。第一灌注模型可以包括但不限于奇异值分解(Singular Value Decomposition，SVD)去卷积模型和/或最大斜率模型。
在步骤550中，第一参数确定模块450可以根据第一灌注模型和初始时间浓度曲线确定第一灌注参数。第一灌注参数可以包括但不限于血流量(Blood Flow，BF)、血容量(Blood Volume，BV)、平均通过时间(Mean Transition Time，MTT)等中的一种或几种的组合。
作为示例，第一参数确定模块450可以通过SVD去卷积模型和初始时间浓度曲线确定血容量。具体地，第一参数确定模块450可以通过SVD去卷积模型得到残余函数曲线。根据所述残余函数曲线，第一参数确定模块450可以确定血流量、平均通过时间、延迟时间和摄取常数。根据血流量和平均通过时间，第一参数确定模块450可以确定血容量。所述残余函数可以指当单位量的对比剂被注入到检测对象中后，驻留在器官组织中的对比剂随着时间的变化曲线。所述残余函数可以是与灌注参数相关的函数。所述残余函数可以用公式(1)表示：
r(t)＝f(BF,MTT,BV,PS,E,T0,…)，         (1)
其中，r(t)为残余函数，BF表示血流量，MTT表示平均通过时间，BV表示血容量，PS表示毛细血管表面渗透性，E表示摄取常数，T0表示延迟时间。具体地，可以先利用初始组织时间浓度曲线和初始输入血管时间浓度曲线，根据SVD去卷积模型得到残余函数曲线，然后根据残余函数曲线的特点按照时间顺序将残余函数曲线分为延迟段、血管内段和血管外段。延迟段时长可以为延迟时间T0，血管内段血流量峰值可以为血流量BF，血管内段时长可以为平均通过时间MTT，血管外段血流量均值与血管内段血流量峰值的比值可以为摄取常数E，血流量与平均通过时间的乘积可以为血容量BV。
作为又一示例，第一参数确定模块450可以根据初始组织时间浓度曲线、初始输入血管时间浓度曲线和最大斜率模型确定血流量、血容量和平均通过时间。具体地，在利用最大斜率模型确定第一灌注参数时，可以假设流经器官组织的对比剂无血管流出。在此前提下，初始组织时间浓度曲线下面积与初始输入血管时间浓度曲线下面积的比值可以为血容量BV，初始组织时间浓度曲线上升段的最大斜率与初始输入血管时间浓度曲线峰值的比值可以为血流量BF，血容量与血流量的比值可以为平均通过时间MTT。
在步骤560中，第二模型获取模块460可以获取第二灌注模型。第二灌注模型可以包括AATH模型(Adiabatic Approximation to Tissue Homogeneity)和/或DP模型(Distributed Parameter)。
在步骤570中，第二参数确定模块470可以根据第二灌注模型和第一灌注参数确定第二灌注参数。第二灌注参数可以包括但不限于血流量(Blood Flow，BF)、血容量(Blood Volume，BV)、平均通过时间(Mean Transition Time，MTT)、毛细血管表面渗透性(Permeability Surface，PS)等中的一种或几种的组合。在一些实 施例中，根据第二灌注模型和第一灌注参数确定第二灌注参数的操作可以包括：确定目标函数；利用目标函数和第二灌注模型进行循环迭代，确定第二灌注参数。其中，第一灌注参数可以作为迭代过程中的初始值，即第一次迭代过程的代入值。
根据本申请的一些实施例，图6是确定第二灌注参数的一种示例性流程图。在一些实施例中，流程600可以用于根据第二灌注模型和第一灌注参数确定流程500中步骤570的第二灌注参数。
在步骤610中，第二参数确定模块470可以确定目标函数。所述目标函数可以用于确定满足用户(例如，医生或成像技师)要求的方程解，即第二灌注参数。在一些实施例中，所述目标函数可以根据最小二乘法得到。所述最小二乘法是指根据残差平方和最小的原则确定方程的解。残差是指数据点与根据数据点模拟得到的曲线或直线上相应位置的差异。残差平方和是指每个残差进行平方之后的加和。在一些实施中，目标函数可以包括残差平方和和/或平均残差平方和。所述平均残差平方和可以指残差平方和除以时间点的数量。作为示例，若目标函数为残差平方和，根据最小二乘法确定的目标函数可以用公式(2)表示：
Figure PCTCN2017074703-appb-000001
其中，
Figure PCTCN2017074703-appb-000002
表示目标函数，Ctiss(ti)表示初始组织时间浓度曲线，
Figure PCTCN2017074703-appb-000003
表示基于第二灌注模型的组织时间浓度曲线，ti表示时间点，N表示时间点的数量，p表示所述第二灌注模型对应的灌注参数。
作为示例，如果第二灌注模型为AATH模型，则基于AATH模型的组织时间浓度曲线可以为初始输入血管时间浓度曲线与残余函数的卷积。基于AATH模型的组织时间浓度曲线可以用公式(3)表示：
Figure PCTCN2017074703-appb-000004
其中，Cart(t)表示初始输入血管时间浓度曲线，
Figure PCTCN2017074703-appb-000005
表示卷积算子，r(t)表示残余函数，可以用公式(1)表示，并且r(t)满足以下条件：
BF>0，0≤E≤1，MTT>0，T0≥0
在步骤620中，第二参数确定模块470可以确定预估灌注参数。所述预估灌注参数可以表示在每次迭代过程中代入残余函数的灌注参数。在一些实施例中，在第一次迭代过程中，可以将第一灌注参数作为预估灌注参数代入残余函数中。
在步骤630中，第二参数确定模块470可以根据目标函数、预估灌注参数和第 二灌注模型确定目标函数值。作为示例，如果目标函数是根据最小二乘法确定的平方残差和并且第二灌注模型为AATH模型，则确定目标函数值的过程可以包括：将预估灌注参数代入残余函数中，根据残余函数与初始输入血管时间浓度曲线确定基于AATH模型的组织时间浓度曲线，然后确定基于AATH模型的组织时间浓度曲线和初始组织时间浓度曲线的残差平方和。所述残差平方和可以为基于AATH模型的组织时间浓度曲线和初始组织时间浓度曲线在每个时间点的对比剂浓度差值的平方和。
在步骤640中，第二参数确定模块470可以判断是否满足结束条件。如果满足结束条件，则进入步骤650，将本次迭代过程中代入残余函数的预估灌注参数确定为第二灌注参数。如果不满足结束条件，则进入步骤620，进入新一轮迭代过程，确定新的预估灌注参数。在一些实施例中，可以根据上一次迭代过程的预估灌注参数确定本次迭代过程的预估灌注参数。例如，可以根据第一次迭代过程中代入残余函数的第一灌注参数确定第二次迭代过程中的预估灌注参数。在一些实施例中，确定预估灌注参数的方法可以包括在灌注参数的预设范围内调整预估灌注参数的值，以使目标函数值(例如，残差平方和)变小。在一些实施例中，所述预设范围可以包括灌注参数的正常数值范围，例如，脑部动脉的血流量正常数值范围可以是0.1-0.9ml/min/ml。在一些实施例中，可以利用Levenberg-Marquardt模型确定预估灌注参数。在一些实施例中，所述结束条件可以包括但不限于平均残差平方和小于第一阈值和/或相邻两次迭代过程的预估灌注参数中的每个参数的调整值均小于第二阈值。所述平均残差平方和是指残差平方和除以时间点的数量。所述调整值是指相邻两次迭代过程的预估灌注参数的差值的绝对值。所述第一阈值可以为0.01。所述第二阈值可以为0.001。
根据本申请的一些实施例，图7是组织时间浓度曲线的拟合效果图。如图7所示，以CT灌注成像为例，图7中横坐标表示成像设备对检测对象感兴趣区域的选定层面进行扫描的时间点，纵坐标表示加入对比剂后感兴趣区域器官组织CT值的变化值。710表示组织时间浓度离散点之一。曲线1表示根据多个组织时间浓度离散点710确定的初始组织时间浓度曲线。曲线2表示根据输入血管时间浓度离散点(图中未画出)确定的初始输入血管时间浓度曲线。曲线3表示根据第二灌注模型进行模拟得到的基于第二灌注模型的组织时间浓度曲线。
根据本申请的一些实施例，图8是第二灌注参数结果验证示意图。第二灌注模型以AATH模型为例，第二灌注参数结果验证方法可以包括：人为设定灌注参数，然后将设定的灌注参数代入残余函数中，将残余函数与初始输入血管时间浓度曲线进行卷 积得到理想的组织时间浓度曲线，向理想的组织时间浓度曲线中加入随机噪声模拟实际数据中存在的干扰信号以生成仿真组织时间浓度曲线(例如，数据信噪比为10dB)，将仿真组织时间浓度曲线作为初始组织时间浓度曲线，利用流程500和/或流程600中涉及的获取灌注参数的方法，根据初始组织时间浓度曲线、初始输入血管时间浓度曲线、第一灌注模型和第二灌注模型得到灌注参数的计算值，并将灌注参数的计算值与设定值进行比较，以验证所述确定灌注参数方法的准确性。
以血流量为例，如图8所示，横坐标为血流量(BF)的设定值，纵坐标为血流量(BF)的计算值。在验证过程中，可以选取0.1，0.4，0.7，0.9作为血流量(BF)的设定值。如图8所示，一个数据点(例如，810)可以表示血流量的设定值和所述血流量设定值对应的血流量计算值。在一些实施例中，在确定血流量设定值对应的血流量计算值时，灌注分析设备130可以根据流程500和/或流程600进行多次(例如，500次)重复测试。所述血流量的计算值可以根据所述多次重复测试结果得到。所述计算值可以是多次重复测试结果的平均值。如图8所示，820可以表示所示数据点810的计算值对应的多次测试结果的标准差。820高度的一半可以表示标准差值。820的高度可以根据，例如，如图8所示的坐标系确定。例如，在图8所示的坐标系中，820的高度为0.08，则数据点810对应的计算值的标准差为0.04。标准差可以指一组数值中各数值与其平均值的离差平方和的算术平均数的平方根。标准差可以反映组内个体间的离散程度。离差平方和可以指一组数值中各数值与其平均值之差的平方和。从图8中可以看出，血流量的设定值和计算值的一致性较高，说明本申请中涉及的确定灌注参数的方法的准确性较高。
根据本申请的一些实施例，图9-A至图9-D是根据第二灌注参数得到的脑肿瘤灌注参数图。图9-A是血流量的灌注参数图，图9-B是血容量的灌注参数图，图9-C是平均通过时间的灌注参数图，图9-D是表面渗透性的灌注参数图。一般情况下，在灌注参数图中，像素点或体素点的灰度值越大，相应的灌注参数值越高。一般情况下，肿瘤血管可以具有较高的微血管密度、高血流量、高血容量及高通透性的特点。如图9-D所示，910为脑肿瘤，通过本申请涉及的确定灌注参数的方法得到的脑肿瘤处的毛细血管表面渗透性相对于其他脑实质组织有明显增加。
在一些实施例中，本申请中涉及的第一阈值和第二阈值可以是一个数值范围，也可以是一个具体数值。第一阈值和第二阈值可以根据历史数据、灌注成像系统100的默认值或用户(例如，医生、成像技师等)指令确定。
以上概述了成像系统级方法的不同方面和/或通过程序实现其他步骤的方法。技术中的程序部分可以被认为是以可执行的代码和/或相关数据的形式而存在的“产品”或“制品”，是通过计算机可读的介质所参与或实现的。有形的、永久的储存介质包括任何计算机、处理器、或类似设备或相关的模块所用到的内存或存储器。例如各种半导体存储器、磁带驱动器、磁盘驱动器或者类似任何时间能够为软件提供存储功能的设备。
所有软件或其中的一部分有时可能会通过网络进行通信，如互联网或其他通信网络。此类通信能够将软件从一个计算机设备或处理器加载到另一个。例如：从成像系统的一个管理服务器或主机计算机加载至一个计算机环境的硬件平台，或其他实现系统的计算机环境，或与提供按需服务所需要的信息相关的类似功能的系统。因此，另一种能够传递软件元素的介质也可以被用作局部设备之间的物理连接，例如光波、电波、电磁波等，通过电缆、光缆或者空气实现传播。用来载波的物理介质如电缆、无线连接或光缆等类似设备，也可以被认为是承载软件的介质。在这里的用法除非限制了有形的“储存”介质，其他表示计算机或机器“可读介质”的术语都表示在处理器执行任何指令的过程中参与的介质。
因此，一个计算机可读的介质可能有多种形式，包括但不限于，有形的存储介质，载波介质或物理传输介质。稳定的储存介质包括：光盘或磁盘，以及其他计算机或类似设备中使用的，能够实现图中所描述的系统组件的存储系统。不稳定的存储介质包括动态内存，例如计算机平台的主内存。有形的传输介质包括同轴电缆、铜电缆以及光纤，包括计算机系统内部形成总线的线路。载波传输介质可以传递电信号、电磁信号，声波信号或光波信号，这些信号可以由无线电频率或红外数据通信的方法所产生的。通常的计算机可读介质包括硬盘、软盘、磁带、任何其他磁性介质；CD-ROM、DVD、DVD-ROM、任何其他光学介质；穿孔卡、任何其他包含小孔模式的物理存储介质；RAM、PROM、EPROM、FLASH-EPROM，任何其他存储器片或磁带；传输数据或指令的载波、电缆或传输载波的连接装置、任何其他可以利用计算机读取的程序代码和/或数据。这些计算机可读介质的形式中，会有很多种出现在处理器在执行指令、传递一个或更多结果的过程之中。
本领域技术人员能够理解，本申请所披露的内容可以出现多种变型和改进。例如，以上所描述的不同系统组件都是通过硬件设备所实现的，但是也可能只通过软件的解决方案得以实现。例如：在现有的服务器上安装系统。此外，这里所披露 的位置信息的提供可能是通过一个固件、固件/软件的组合、固件/硬件的组合或硬件/固件/软件的组合得以实现。
以上内容描述了本申请和/或一些其他的示例。根据上述内容，本申请还可以作出不同的变形。本申请披露的主题能够以不同的形式和例子所实现，并且本申请可以被应用于大量的应用程序中。后文权利要求中所要求保护的所有应用、修饰以及改变都属于本申请的范围。

Claims (28)

  1. 一种灌注分析方法，包括：
    获取扫描图像，其中所述扫描图像包括一个或多个时间点对应的一个或多个图像；
    根据所述扫描图像，获取时间浓度离散点；
    根据所述时间浓度离散点，获取初始时间浓度曲线，所述初始时间浓度曲线表示所述扫描图像中某个像素或体素点对应的器官组织中的对比剂浓度随时间的变化情况；
    获取第一灌注模型；
    根据所述第一灌注模型以及所述初始时间浓度曲线，确定第一灌注参数；
    获取第二灌注模型；以及
    根据所述第二灌注模型和所述第一灌注参数，确定第二灌注参数。
  2. 根据权利要求1所述的灌注分析方法，所述初始时间浓度曲线包括初始输入血管时间浓度曲线和初始组织时间浓度曲线。
  3. 根据权利要求2所述的灌注分析方法，所述第一灌注模型包括奇异值模型或最大斜率模型。
  4. 根据权利要求3所述的灌注分析方法，根据所述第一灌注模型以及所述初始时间浓度曲线，确定所述第一灌注参数包括：
    通过奇异值模型去卷积得到残余函数曲线；以及
    根据所述残余函数曲线确定所述第一灌注参数。
  5. 根据权利要求4所述的灌注分析方法，根据所述残余函数曲线确定所述第一灌注参数包括：
    将所述残余函数曲线按照时间顺序分为延迟段、血管内段和血管外段；以及
    根据所述延迟段、所述血管内段和所述血管外段确定所述第一灌注参数。
  6. 根据权利要求3所述的灌注分析方法，根据所述第一灌注模型以及所述初始时间浓度曲线，确定所述第一灌注参数包括：
    根据所述初始输入血管的时间浓度曲线和所述初始组织的时间浓度曲线确定所述初始输入血管的时间浓度曲线下面积、所述初始组织的时间浓度曲线下面积、所述初始组织的时间浓度曲线上升段的最大斜率和所述初始输入血管的时间浓度曲线峰值；以及
    根据所述初始输入血管的时间浓度曲线下面积、所述初始组织的时间浓度曲线下面积、所述初始组织的时间浓度曲线上升段的最大斜率和所述初始输入血管的时间浓度曲线峰值确定所述第一灌注参数。
  7. 根据权利要求1所述的灌注分析方法，所述第一灌注参数包括血容量、血流量和平均通过时间。
  8. 根据权利要求2所述的灌注分析方法，根据所述第二灌注模型和所述第一灌注参数，确定所述第二灌注参数包括：
    a.确定目标函数；
    b.确定预估灌注参数；
    c.根据所述目标函数、所述预估灌注参数和所述第二灌注模型，确定目标函数值；以及
    d.判断所述目标函数值是否满足结束条件，如果所述目标函数值满足所述结束条件，则将所述预估函数确定为第二灌注参数，如果所述目标函数值不满足所述结束条件，则执行b-d，进行新一轮迭代，其中，第一次迭代过程中，所述预估灌注参数为所述第一灌注参数。
  9. 根据权利要求8所述的灌注分析方法，所述目标函数表示残差平方和或平均残差平方和，所述残差平方和表示基于第二灌注模型的组织时间浓度曲线和所述初始组织浓度曲线在所述一个或多个时间点的对比剂浓度差值的平方和，所述平均残差平方和表示所述残差平方和与时间点数的比值。
  10. 根据权利要求9所述的灌注分析方法，所述基于第二灌注模型的组织的时间浓度曲线是指所述初始输入血管时间浓度曲线与残余函数的卷积，所述残余函数为与灌注参数相关的函数。
  11. 根据权利要求9所述的灌注分析方法，所述结束条件包括以下条件中的至少一种：
    所述平均残差平方和小于第一阈值；
    相邻两次迭代过程的所述预估灌注参数的差值的绝对值小于第二阈值。
  12. 根据权利要求8所述的灌注分析方法，确定所述预估灌注参数包括：
    根据上一次迭代过程的预估灌注参数，利用预估模型在灌注参数的预设范围内确定可行下降方向，根据所述可行下降方向，确定所述预估灌注参数，其中，所述预估模型包括Levenberg-Marquardt模型。
  13. 根据权利要求1所述的灌注分析方法，所述第二灌注模型包括AATH模型(Adiabatic Approximation to Tissue Homogeneity)或DP模型(Distributed Parameter model)。
  14. 根据权利要求1所述的灌注分析方法，所述第二灌注参数包括血流量、血容量、平均通过时间和毛细血管表面渗透性。
  15. 一种灌注分析设备，包括：
    一种计算机可读的存储媒介，被配置为存储可执行模块，包括：
    图像获取模块，被配置为获取扫描图像，其中所述扫描图像包括一个或多个时间点对应的一个或多个图像；
    离散点获取模块，被配置为根据所述扫描图像，获取时间浓度离散点；
    曲线确定模块，被配置为根据所述时间浓度离散点，获取初始时间浓度曲线，所述初始时间浓度曲线表示所述扫描图像中某个像素或体素点对应的器官组织中的对比剂浓度随时间的变化情况；
    第一模型获取模块，被配置为获取第一灌注模型；
    第一参数确定模块，被配置为根据所述第一灌注模型以及所述初始时间浓度曲线，确定第一灌注参数；
    第二模型获取模块，被配置为获取第二灌注模型；以及
    第二参数确定模块，被配置为根据所述第二灌注模型和所述第一灌注参数，确定第二灌注参数；以及
    一个处理器，所述处理器能够执行所述计算机可读的存储媒介存储的可执行模块。
  16. 根据权利要求15所述的灌注分析设备，所述初始时间浓度曲线包括初始输入血管时间浓度曲线和初始组织时间浓度曲线。
  17. 根据权利要求16所述的灌注分析设备，所述第一灌注模型包括奇异值模型或最大斜率模型。
  18. 根据权利要求17所述的灌注分析设备，根据所述第一灌注模型以及所述初始时间浓度曲线，确定所述第一灌注参数包括：
    通过奇异值模型去卷积得到残余函数曲线；以及
    根据所述残余函数曲线确定所述第一灌注参数。
  19. 根据权利要求18所述的灌注分析设备，根据所述残余函数曲线确定所述第一灌注参数包括：
    将所述残余函数曲线按照时间顺序分为延迟段、血管内段和血管外段；以及
    根据所述延迟段、所述血管内段和所述血管外段确定所述第一灌注参数。
  20. 根据权利要求17所述的灌注分析设备，根据所述第一灌注模型以及所述初始时间浓度曲线，确定所述第一灌注参数包括：
    根据所述初始输入血管的时间浓度曲线和所述初始组织的时间浓度曲线确定所述初始输入血管的时间浓度曲线下面积、所述初始组织的时间浓度曲线下面积、所述初始组织的时间浓度曲线上升段的最大斜率和所述初始输入血管的时间浓度曲线峰值；以及
    根据所述初始输入血管的时间浓度曲线下面积、所述初始组织的时间浓度曲线下面积、所述初始组织的时间浓度曲线上升段的最大斜率和所述初始输入血管的时间浓度曲线峰值确定所述第一灌注参数。
  21. 根据权利要求15所述的灌注分析设备，所述第一灌注参数包括血容量、血流量和平均通过时间。
  22. 根据权利要求16所述的灌注分析设备，根据所述第二灌注模型和所述第一灌注参数，确定所述第二灌注参数包括：
    a.确定目标函数；
    b.确定预估灌注参数；
    c.根据所述目标函数、所述预估灌注参数和所述第二灌注模型，确定目标函数值；以及
    d.判断所述目标函数值是否满足结束条件，如果所述目标函数值满足所述结束条件，则将所述预估函数确定为第二灌注参数，如果所述目标函数值不满足所述结束条件，则执行b-d，进行新一轮迭代，其中，第一次迭代过程中，所述预估灌注参数为所述第一灌注参数。
  23. 根据权利要求22所述的灌注分析设备，所述目标函数表示残差平方和或平均残差平方和，所述残差平方和表示基于第二灌注模型的组织时间浓度曲线和所述初始组织浓度曲线在所述一个或多个时间点的对比剂浓度差值的平方和，所述平均残差平方和表示所述残差平方和与时间点数的比值。
  24. 根据权利要求23所述的灌注分析设备，所述基于第二灌注模型的组织的时间浓度曲线是指所述初始输入血管时间浓度曲线与残余函数的卷积，所述残余函数为与灌注参数相关的函数。
  25. 根据权利要求23所述的灌注分析设备，所述结束条件包括以下条件中的至少一种：
    所述平均残差平方和小于第一阈值；
    相邻两次迭代过程的所述预估灌注参数的差值的绝对值小于第二阈值。
  26. 根据权利要求22所述的灌注分析设备，确定所述预估灌注参数包括：
    根据上一次迭代过程的预估灌注参数，利用预估模型在灌注参数的预设范围内确定可行下降方向，根据所述可行下降方向，确定所述预估灌注参数，其中，所述预估模型包括Levenberg-Marquardt模型。
  27. 根据权利要求15所述的灌注分析设备，所述第二灌注模型包括AATH模型(Adiabatic Approximation to Tissue Homogeneity)或DP模型(Distributed Parameter model)。
  28. 根据权利要求15所述的灌注分析设备，所述第二灌注参数包括血流量、血容量、平均通过时间和毛细血管表面渗透性。
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