WO2024109762A1 - Pet参数确定方法、装置、设备和存储介质 - Google Patents

Pet参数确定方法、装置、设备和存储介质 Download PDF

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WO2024109762A1
WO2024109762A1 PCT/CN2023/133057 CN2023133057W WO2024109762A1 WO 2024109762 A1 WO2024109762 A1 WO 2024109762A1 CN 2023133057 W CN2023133057 W CN 2023133057W WO 2024109762 A1 WO2024109762 A1 WO 2024109762A1
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pet
activity
expression
tracer
tissue
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PCT/CN2023/133057
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English (en)
French (fr)
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孙涛
王振国
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中国科学院深圳先进技术研究院
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • 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

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  • the embodiments of the present invention relate to the field of image processing, and in particular to a PET parameter determination method, apparatus, device and storage medium.
  • the dynamic images collected by whole-body PET are of high quality, which is conducive to more accurate parameter estimation.
  • whole-body PET has more pixels than traditional PET, the computational cost of using traditional nonlinear estimation methods to determine PET parameters is high.
  • PET parameter determination based on linear regression of graphical estimation methods requires that the tracer reaches a blood/tissue equilibrium steady state in the body, which may result in large parameter estimation errors. Therefore, a method for accurately calculating PET parameters is needed to increase the speed of PET parameter determination.
  • the present invention provides a PET parameter determination method, device, equipment and storage medium to solve the problem of slow speed in the existing parameter determination method.
  • a method for determining PET parameters comprising:
  • the value of at least one dynamic parameter corresponding to the PET image set is determined according to the updated activity sum expression, and the dynamic parameter includes the flow velocity between each tissue compartment in the tissue compartment model and/or the net inflow rate of the tracer.
  • a PET parameter determination device comprising:
  • An acquisition module used for acquiring PET scan data of the scanned part and extracting the tracer identifier from the PET scan data
  • An image reconstruction module used for performing image reconstruction on the PET scanning data to obtain a PET image set
  • a model determination module used to determine a sampling time activity curve corresponding to each pixel according to the PET image, and determine a tissue compartment model corresponding to the sampling time activity curve according to a tracer identifier and a correspondence between the tracer identifier and the tissue compartment model created in advance;
  • a model updating module used to modify the activity sum expression corresponding to the gray value of each pixel point corresponding to the tissue chamber model based on the tissue chamber model, so as to update the activity sum expression
  • the parameter determination module is used to determine the value of at least one dynamic parameter corresponding to the PET image set according to the updated activity sum expression, and the dynamic parameter includes the flow velocity between each tissue compartment in the tissue compartment model and/or the net inflow rate of the tracer.
  • an electronic device comprising:
  • the memory stores a computer program that can be executed by at least one processor.
  • the computer program is executed by at least one processor so that the at least one processor can perform the PET parameter determination method according to any embodiment of the present invention.
  • a computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the PET parameter determination method of any embodiment of the present invention when executed.
  • the technical solution of the embodiment of the present invention obtains PET scan data of the scanned part, and
  • the invention relates to a method for extracting a tracer identifier from PET scanning data; reconstructing the PET scanning data to obtain a PET image set; determining a sampling time activity curve corresponding to each pixel according to the PET image set, and determining a tissue compartment model corresponding to the sampling time activity curve according to the tracer identifier and a correspondence between the tracer identifier and the tissue compartment model created in advance; modifying an activity sum expression corresponding to a gray value of each pixel point corresponding to the tissue compartment model based on the tissue compartment model to update the activity sum expression; determining a value of at least one dynamic parameter corresponding to the PET image set according to the updated activity sum expression, the dynamic parameter including a flow velocity between each tissue compartment in the tissue compartment model and/or a net inflow rate of the tracer, thereby realizing the determination of PET parameters of the PET image by a linear estimation method based on the tissue compartment model and the corresponding activity sum
  • FIG1 is a flow chart of a method for determining PET parameters provided by an embodiment of the present invention.
  • FIG2 is a flow chart of another PET parameter determination method provided by an embodiment of the present invention.
  • FIG3 is a flow chart of another PET parameter determination method provided by an embodiment of the present invention.
  • FIG4 is a structural block diagram of a PET parameter determination device provided by an embodiment of the present invention.
  • FIG5 is a structural block diagram of an electronic device provided by an embodiment of the present invention.
  • Fig. 1 is a flow chart of a PET parameter determination method provided by an embodiment of the present invention, and this embodiment is applicable to PET parameter determination scenarios.
  • the method can be executed by a PET parameter determination device, and the PET parameter determination device can be implemented in the form of hardware and/or software, and can also be configured in an electronic device.
  • the PET parameter determination method includes the following steps:
  • the PET scan data is scan data of any part of the human body or the whole body.
  • PET is the only new imaging technology that can display the metabolism of biological molecules, receptors and neurotransmitter activities in vivo. It is used in the diagnosis and differential diagnosis of various diseases, disease assessment, efficacy evaluation, organ function research and new drug development. PET uses annihilation radiation and positron collimation (or photon collimation) technology to measure the spatial distribution, quantity and dynamic changes of tracer or its metabolite molecules in vivo, and obtains biochemical, physiological and functional metabolic changes caused by the interaction between PET tracers and targets (such as receptors, enzymes, ion channels, antigenic determinants and nucleic acids) in vivo at the molecular level. ized image information.
  • targets such as receptors, enzymes, ion channels, antigenic determinants and nucleic acids
  • the tracer identifier is the name or code of the tracer.
  • a tracer is a marker added to observe, study and measure the behavior or properties of a substance in a specified process.
  • the tracer identifier can be an existing tracer identifier such as a sugar metabolism tracer identifier (18F-FDG), a prostate cancer radioactive tracer identifier (68Ga-PSMA) or 18F-FAPI (18F-fibroblast activation protein inhibitor).
  • PET scan data is obtained by a PET-CT scanner, for example, a uEXPLORER PET-CT scanner.
  • a CT scan is performed on the subject for attenuation correction; after 18F-FDG is injected into the vein from the lower limb, a 60-minute PET list mode acquisition is started; then, the scan data from 0 to 60 minutes is divided into 66 PET scan data subsets, including 5 seconds ⁇ 24 frames, 10 seconds ⁇ 6 frames, 30 seconds ⁇ 6 frames, 60 seconds ⁇ 6 frames, and 120 seconds ⁇ 24 frames.
  • Image reconstruction algorithms include iterative image reconstruction algorithms, GPU-accelerated particle filter PET image reconstruction algorithms, PET image reconstruction algorithms based on void U-Net neural networks, PET image reconstruction algorithms based on anisotropic diffusion filtering and non-local priors, and reconstruction algorithms based on sinusoidal graphs.
  • reconstruction algorithms based on sinusoidal graphs include: filtered back projection (FBP), maximum likelihood expectation maximization (MLEM), and ordered subset expectation maximization (OSEM).
  • FBP method is a reconstruction algorithm that uses a filter function to perform filtering before back projection
  • the MLEM method is an iterative image reconstruction algorithm based on maximum likelihood estimation, which uses the maximum expectation method to update the estimated value of the pixel.
  • the iterative reconstruction algorithm divides all the projection data into multiple subsets. Each time a subset of data is used, all pixels are updated once. All subsets are used in turn as one iteration. Specifically, first, the expression for calculating the conditional expected value of the likelihood function is determined; then, the pixel update value corresponding to the maximum conditional expected value of the likelihood function is derived by using the derivative extremum method. Each time the likelihood function value obtained by updating the pixel is greater than or equal to the previous value, the pixel value eventually converges to the maximum likelihood function.
  • the PET scan data is reconstructed using an existing image reconstruction algorithm, for example, the 3D ordered subset OSEM algorithm built into the uEXPLORER PET-CT scanner control system reconstructs each PET scan data subset into a 192 ⁇ 192 ⁇ 673 image matrix with a voxel size of 3.125 ⁇ 3.125 ⁇ 2.866 mm 3.
  • the image reconstruction uses 3 iterations, 28 subsets, and 2 mm Gaussian smoothing, and attenuation and scatter correction is also performed based on the attenuation correction image of the CT.
  • the images in the PET image set are images with standard uptake values.
  • the standard uptake value refers to the ratio of the radioactivity of the tracer taken up by local tissues to the average injected activity of the whole body. For example, it can be the ratio of the radioactivity uptake at the lesion to the average uptake of the whole body.
  • SUV is also affected by factors such as equipment performance, imaging conditions, acquisition mode, reconstruction algorithm, and attenuation correction.
  • a time-activity curve is a curve that reflects the concentration of a radioactive tracer in a region, with the vertical axis representing concentration and the horizontal axis representing time. For example, it can be a curve that reflects the concentration of a radioactive tracer in tissue, plasma, or other region of interest.
  • the tissue chamber model can be selected as an irreversible two-tissue chamber model, a reversible two-tissue chamber model, a reversible one-tissue chamber model, etc.
  • the tracer marker corresponding to the irreversible two-tissue chamber model can be selected as 18F-FDG (fluorodeoxyglucose, the full chemical name is 2-fluoro-2-deoxy-D-glucose).
  • the grayscale value of each pixel of the PET image is used to represent the activity sum of all chambers in the tissue chamber model, and the activity sum expression is updated based on the tissue chamber model to obtain an updated activity sum expression.
  • the flow velocity between tissue compartments and/or the net influx rate of the tracer are calculated using a linear estimation method based on the updated activity summation expression.
  • the method includes: respectively determining images corresponding to the numerical value of at least one dynamic parameter to obtain at least one dynamic parameter image corresponding to the PET image set.
  • the corresponding images are determined according to the dynamic values, that is, the K1 image, k2 image and k3 image corresponding to the flow velocities K1 , k2 and k3 between the tissue compartments and/or the K1 image corresponding to the net glucose metabolic rate of the tissue organ.
  • the technical solution of the embodiment of the present invention obtains PET scanning data of the scanned part and extracts the tracer identifier from the PET scanning data; performs image reconstruction on the PET scanning data to obtain a PET image set; determines the sampling time activity curve corresponding to each pixel according to the PET image, and determines the tissue chamber model corresponding to the sampling time activity curve according to the tracer identifier and the correspondence between the tracer identifier and the tissue chamber model created in advance; based on the tissue chamber model, modifies the activity sum expression corresponding to the gray value of each pixel point corresponding to the tissue chamber model to update the activity sum expression; determines the value of at least one dynamic parameter corresponding to the PET image set according to the updated activity sum expression, and the dynamic parameter includes the flow velocity between each tissue chamber in the tissue chamber model and/or the net inflow rate of the tracer, so as to realize the determination of the PET parameters of the PET image by a linear estimation method based on the tissue chamber model and the corresponding activity sum expression, thereby improving the estimation speed of the PET parameters.
  • FIG. 2 is a flow chart of another PET parameter determination method provided in an embodiment of the present invention.
  • This example belongs to the same inventive concept as the PET parameter determination method in the above embodiment, and further describes the process of determining the value of at least one dynamic parameter corresponding to the PET image set according to the updated activity sum expression on the basis of the above embodiment.
  • the PET parameter determination method includes the following steps:
  • the tracer is labeled as 18F-FDG
  • the tissue compartment model is an irreversible two-tissue compartment model as an example to describe the technical solution in detail, wherein the irreversible two-tissue compartment model can be described by a set of linear ordinary differential equations:
  • Cp (t), C1 (t) and C2 (t) correspond to the activity of FDG in blood, the activity of free FDG and the activity of phosphorylated FDG, respectively.
  • K1 , k2 , k3 and k4 represent the flow rate between tissue compartments. Because the phosphorylation process is an irreversible process in most tissues, k4 in the irreversible two-tissue compartment model is 0.
  • a first setting parameter set is introduced, and the expressions of the parameters in the first setting parameter set are as follows:
  • the first setting parameters in the first setting parameter set (10) are used to replace the coefficients of the variables in the current activity expression, and formula (9) is rewritten as:
  • the net influx rate (K i ) of the tracer can indirectly refer to the net metabolic rate of glucose, and the unit is ml/g/min.
  • the corresponding relationship between the net influx rate of the tracer and the flow rate between each tissue compartment is After obtaining K 1 , k 2 and k 3 , the net inflow rate data Ki of the tracer can be calculated.
  • the user can select each pixel point in the area of interest to calculate K i and form a K i parameter image according to needs.
  • the technical solution of this embodiment determines the net inflow rate of the tracer and the flow velocity between each tissue compartment by using an irreversible two-tissue compartment model, realizes the determination of the PET parameters of the PET image by a linear estimation method based on the tissue compartment model and the corresponding activity sum expression, and further improves the speed of PET parameter determination.
  • FIG3 is a flow chart of another PET parameter determination method provided by an embodiment of the present invention.
  • This embodiment and the PET parameter determination method in the above embodiment belong to the same inventive concept, and further provides a process for determining the numerical value of at least one dynamic parameter corresponding to a PET image set based on an updated activity sum expression on the basis of the above embodiment.
  • the PET parameter determination method includes the following steps:
  • a second setting parameter set is introduced, and the expressions of the parameters in the second setting parameter set are as follows:
  • the technical solution of this embodiment uses an irreversible two-tissue compartment model to determine the net inflow rate of the tracer, thereby realizing a linear estimation method based on the tissue compartment model and the corresponding activity sum expression to determine the PET parameters of the PET image, thereby further improving the estimation speed of the PET parameters.
  • Fig. 4 is a structural block diagram of a PET parameter determination device provided by an embodiment of the present invention, and this embodiment is applicable to the scenario of PET parameter determination.
  • the device can be implemented in the form of hardware and/or software and integrated into a computer device with application development function.
  • the PET parameter determination device includes:
  • An acquisition module 410 is used to acquire PET scan data of the scanned part and extract the tracer marker from the PET scan data;
  • Image reconstruction module 420 used to reconstruct the PET scan data to obtain a PET image set
  • a model determination module 430 is used to determine a sampling time activity curve corresponding to each pixel according to the PET image set, and determine a tissue compartment model corresponding to the sampling time activity curve according to the tracer identifier and the correspondence between the tracer identifier and the tissue compartment model created in advance;
  • a model updating module 440 is used to modify the activity sum expression corresponding to the gray value of each pixel point corresponding to the tissue compartment model based on the tissue compartment model to update the activity sum expression;
  • the parameter determination module 450 is used to determine the value of at least one dynamic parameter corresponding to the PET image set according to the updated activity sum expression, and the dynamic parameter includes the flow velocity between each tissue compartment in the tissue compartment model and/or the net inflow rate of the tracer.
  • the parameter determination module 450 is further configured to:
  • each first setting parameter in the first setting parameter set to replace the coefficient of each variable in the current activity expression respectively, so as to update the current activity expression; wherein the number of the first setting parameters in the first setting parameter set is the same as the number of the variables;
  • the net inflow rate data of the tracer are determined according to the flow velocity data between the tissue chambers and the corresponding relationship between the net inflow rate data of the tracer and the flow velocity between the tissue chambers.
  • the parameter determination module 450 is further configured to:
  • the current activity expression is transformed to update the current activity expression
  • each second setting parameter in the second setting parameter set to replace the coefficient of each variable in the updated activity expression respectively, so as to update the current activity expression; wherein the number of the second setting parameters in the second setting parameter set is the same as the number of the variables;
  • the device is also used to: respectively determine images corresponding to the numerical value of at least one dynamic parameter to obtain at least one dynamic parameter image corresponding to the PET image set.
  • the technical solution of this embodiment obtains PET scan data of the scanned part through the mutual cooperation between various modules, extracts the tracer identifier from the PET scan data; performs image reconstruction on the PET scan data to obtain a PET image set; determines the sampling time activity curve corresponding to each pixel according to the PET image set, and determines the set tissue chamber model corresponding to the sampling time activity curve according to the tracer identifier and the correspondence between the tracer identifier and the tissue chamber model created in advance; based on the tissue chamber model, modifies the activity sum expression corresponding to the gray value of each pixel point corresponding to the tissue chamber model to update the activity sum expression; determines the value of at least one dynamic parameter corresponding to the PET image set according to the updated activity sum expression, and the dynamic parameter includes the flow velocity between each tissue chamber in the tissue chamber model and/or the net inflow rate of the tracer, so as to realize the determination of the PET parameters of the PET image by a linear estimation method based on the tissue chamber model and the corresponding activity sum expression, thereby improving the estimation speed of
  • the PET parameter determination device provided in the embodiment of the present invention can execute the PET parameter determination method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.
  • FIG5 is a block diagram of an electronic device provided by an embodiment of the present invention.
  • the electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, or other suitable computers.
  • the electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.), or other similar computing devices.
  • the components shown herein, their connections, and their functions are merely examples and are not intended to limit the implementation of the present invention described and/or required herein.
  • the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11 in communication, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can execute the computer program stored in the read-only memory (ROM) 12.
  • the processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14.
  • An input/output (I/O) interface 15 is also connected to the bus 14.
  • a number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16, such as a keyboard or a mouse, etc.; an output unit 17, such as various types of displays or speakers, etc.; a storage unit 18, such as a disk or an optical disk, etc.; and a communication unit 19, such as a network card, a modem, or a wireless communication transceiver, etc.
  • the communication unit 19 allows the electronic device 10 to exchange information/data with other devices through a computer network such as the Internet and/or various telecommunication networks.
  • the processor 11 may be a variety of general and/or dedicated processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), any appropriate processor, controller or microcontroller, etc.
  • the processor 11 executes the various methods and processes described above, such as a PET parameter determination method.
  • the PET parameter determination method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18.
  • part or all of the computer program may be loaded and/or installed on the electronic device 10 via the ROM 12 and/or the communication unit 19.
  • the processor 11 may be configured to perform the PET parameter determination method in any other suitable manner (e.g., by means of firmware).
  • Various implementations of the systems and techniques described above can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system on a chip systems (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof.
  • FPGAs field programmable gate arrays
  • ASICs application specific integrated circuits
  • ASSPs application specific standard products
  • SOCs system on a chip systems
  • CPLDs load programmable logic devices
  • An embodiment may include: being implemented in one or more computer programs, which may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special-purpose or general-purpose programmable processor, which may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.
  • a programmable processor which may be a special-purpose or general-purpose programmable processor, which may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.
  • Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions/operations specified in the flow chart and/or block diagram are implemented.
  • the computer program may be executed entirely on the machine or partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.
  • a computer readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or equipment.
  • a computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing.
  • a computer readable storage medium may be a machine readable signal medium.
  • a more specific example of a machine readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
  • RAM random access memory
  • ROM read-only memory
  • EPROM or flash memory erasable programmable read-only memory
  • CD-ROM portable compact disk read-only memory
  • CD-ROM compact disk read-only memory
  • magnetic storage device or any suitable combination of the foregoing.
  • the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (Cathode Ray Tube) or LCD (Liquid Crystal Display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device.
  • a display device e.g., a CRT (Cathode Ray Tube) or LCD (Liquid Crystal Display) monitor
  • a keyboard and pointing device e.g., a mouse or trackball
  • Other types of devices may also be used.
  • the feedback provided to the user can be any form of sensory feedback (for example, visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).
  • the systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components.
  • the components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.
  • a computing system may include a client and a server.
  • the client and the server are generally remote from each other and usually interact through a communication network.
  • the client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other.
  • the server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

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Abstract

公开了一种PET参数确定方法、装置、设备和存储介质。其中，方法包括：获取被扫描部位的PET扫描数据，从PET扫描数据中提取示踪剂标识；对PET扫描数据进行图像重建以得到PET图像集；根据PET图像集确定各像素对应的采样时间活度曲线，根据示踪剂标识以及预先创建的示踪剂标识与组织室模型的对应关系，确定采样时间活度曲线对应的组织室模型；基于组织室模型，修改组织室模型对应的每个像素点的灰度值对应的活度加和表达式，以更新活度加和表达式；根据更新后活度加和表达式确定PET图像集对应的至少一个动态参数的数值，实现了基于组织室模型以及对应的活度加和表达式，采用线性估计的方法确定PET图像的PET参数，提高了PET参数的估计速度。

Description

PET参数确定方法、装置、设备和存储介质 技术领域
本发明实施例涉及图像处理领域，尤其涉及PET参数确定方法、装置、设备和存储介质。
背景技术
全身PET(Positron Emission Computed Tomography，正电子发射型计算机断层显像)采集的动态图像质量高，有利于更加精准的参数估计。但是由于全身PET像素点比传统PET更多，所以使用传统的非线性估计方法进行PET参数确定的计算成本较高。此外，基于图形估计方法的线性回归的PET参数确定要求示踪剂在体内达到血液/组织间的平衡稳态，可能产生较大的参数估计误差。因此，需要一种准确计算PET参数的方法以提高PET参数确定的速度。
发明内容
本发明提供了一种PET参数确定方法、装置、设备和存储介质，以解决现有参数确定方法存在速度较慢的问题。
根据本发明的一方面，提供了一种PET参数确定方法，该方法包括：
获取被扫描部位的PET扫描数据，从PET扫描数据中提取示踪剂标识；
对PET扫描数据进行图像重建以得到PET图像集；
根据PET图像集确定各像素对应的采样时间活度曲线，根据示踪剂标识以及预先创建的示踪剂标识与组织室模型的对应关系，确定采样时间活度曲线对应的组织室模型；
基于组织室模型，修改组织室模型对应的每个像素点的灰度值对应的活 度加和表达式，以更新活度加和表达式；
根据更新后活度加和表达式确定PET图像集对应的至少一个动态参数的数值，动态参数包括组织室模型中各组织室间的流动速度和/或示踪剂的净流入率。
根据本发明的另一方面，提供了一种PET参数确定装置，该装置包括：
获取模块，用于获取被扫描部位的PET扫描数据，从PET扫描数据中提取示踪剂标识；
图像重建模块，用于对PET扫描数据进行图像重建以得到PET图像集；
模型确定模块，用于根据PET图像确定各像素对应的采样时间活度曲线，根据示踪剂标识以及预先创建的示踪剂标识与组织室模型的对应关系，确定采样时间活度曲线对应的组织室模型；
模型更新模块，用于基于组织室模型，修改组织室模型对应的每个像素点的灰度值对应的活度加和表达式，以更新活度加和表达式；
参数确定模块，用于根据更新后活度加和表达式确定PET图像集对应的至少一个动态参数的数值，动态参数包括组织室模型中各组织室间的流动速度和/或示踪剂的净流入率。
根据本发明的另一方面，提供了一种电子设备，该电子设备包括：
至少一个处理器；以及
与至少一个处理器通信连接的存储器；其中，
存储器存储有可被至少一个处理器执行的计算机程序，计算机程序被至少一个处理器执行，以使至少一个处理器能够执行本发明任一实施例的PET参数确定方法。
根据本发明的另一方面，提供了一种计算机可读存储介质，该计算机可读存储介质存储有计算机指令，计算机指令用于使处理器执行时实现本发明任一实施例的PET参数确定方法。
本发明实施例的技术方案，获取被扫描部位的PET扫描数据，从PET扫 描数据中提取示踪剂标识；对PET扫描数据进行图像重建以得到PET图像集；根据PET图像集确定各像素对应的采样时间活度曲线，根据示踪剂标识以及预先创建的示踪剂标识与组织室模型的对应关系，确定采样时间活度曲线对应的组织室模型；基于组织室模型，修改组织室模型对应的每个像素点的灰度值对应的活度加和表达式，以更新活度加和表达式；根据更新后活度加和表达式确定PET图像集对应的至少一个动态参数的数值，动态参数包括组织室模型中各组织室间的流动速度和/或示踪剂的净流入率，实现了基于组织室模型以及对应的活度加和表达式，采用线性估计的方法确定PET图像的PET参数，提高了PET参数的估计速度。
应当理解，本部分所描述的内容并非旨在标识本发明的实施例的关键或重要特征，也不用于限制本发明的范围。本发明的其它特征将通过以下的说明书而变得容易理解。
附图说明
为了更清楚地说明本发明实施例中的技术方案，下面将对实施例描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1是本发明实施例提供的一种PET参数确定方法的流程图；
图2是本发明实施例提供的另一种PET参数确定方法的流程图；
图3是本发明实施例提供的又一种PET参数确定方法的流程图；
图4是本发明实施例提供的一种PET参数确定装置的结构框图；
图5是本发明实施例提供的一种电子设备的结构框图。
具体实施方式
为了使本技术领域的人员更好地理解本发明方案，下面将结合本发明 实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分的实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都应当属于本发明保护的范围。
需要说明的是，本发明的说明书和权利要求书及上述附图中的术语“第一”和“第二”等是用于区别类似的对象，而不必用于描述特定的顺序或先后次序。应该理解这样使用的数据在适当情况下可以互换，以便这里描述的本发明的实施例能够以除了在这里图示或描述的那些以外的顺序实施。此外，术语“包括”和“具有”以及他们的任何变形，意图在于覆盖不排他的包含，例如，包含了一系列步骤或单元的过程、方法、系统、产品或设备不必限于清楚地列出的那些步骤或单元，而是可包括没有清楚地列出的或对于这些过程、方法、产品或设备固有的其它步骤或单元。
图1是本发明实施例提供的一种PET参数确定方法的流程图，本实施例可适用于PET参数确定场景。该方法可以由PET参数确定装置来执行，该PET参数确定装置可以采用硬件和/或软件的形式实现，也可以配置于电子设备中。
如图1所示，PET参数确定方法包括以下步骤：
S110、获取被扫描部位的PET扫描数据，从PET扫描数据中提取示踪剂标识。
其中，PET扫描数据为人体任意部位或者全身的扫描数据。
PET是唯一可在活体上显示生物分子代谢、受体及神经介质活动的新型影像技术，用于多种疾病的诊断与鉴别诊断、病情判断、疗效评价、脏器功能研究和新药开发等方面。PET采用湮没辐射和正电子准直(或光子准直)技术，测定示踪剂或其代谢物分子在活体内的空间分布、数量及其动态变化，从分子水平上获得活体内PET示踪剂与靶点(如受体、酶、离子通道、抗原决定簇和核酸)相互作用所产生的生化、生理及功能代谢变 化的影像信息。
示踪剂标识为示踪剂的名称或编码。示踪剂为观察、研究和测量某物质在指定过程中的行为或性质而加入的一种标记物。在一个实施例中，示踪剂标识可以是糖代谢示踪剂标识(18F-FDG)、前列腺癌放射性示踪剂标识(68Ga-PSMA)或18F-FAPI(18F-成纤维细胞激活蛋白抑制剂)等现有示踪剂标识。
在一个具体的实施例中，通过PET-CT扫描仪获得PET扫描数据，例如，可以是uEXPLORERPET-CT扫描仪。首先，对被测者进行CT扫描以进行衰减校正；从下肢静脉注射到静脉的18F-FDG后，开始60分钟的PET列表模式采集；然后，将0-60分钟的扫描数据划分为66个PET扫描数据子集，包括5秒×24帧，10秒×6帧，30秒×6帧，60秒×6帧和120秒×24帧。
S120、对PET扫描数据进行图像重建以得到PET图像集。
使用图像重建算法对PET扫描数据进行图像重建以得到对应的PET图像，进而得到PET图像集。
图像重建算法包括迭代图像重建算法、GPU加速的粒子滤波PET图像重建算法、基于空洞U-Net神经网络的PET图像重建算法、基于各向异性扩散滤波与非局部先验的PET图像重建算法和基于正弦图的重建算法等。其中，基于正弦图的重建算法包括：滤波反投影(filtered back projection，FBP)法、最大似然最大期望值(maximum likelihood expectation maximization，MLEM)法和有序子集最大期望值(ordered subset expectation maximization，OSEM)法。其中，FBP法是在反投影前通过滤波函数进行滤波处理的重建算法；MLEM法是一种基于最大似然估计的迭代图像重建算法，使用最大期望值法更新像素的估计值，每一次都会使似然函数增大，最终使似然函数逼近收敛到最大，由此得到每个像素的最大似然估计值，每更新一次像素估计值都使用全部测量数据，所以速度慢；OSEM法是一种基于最大似然期望法的图像 迭代重建算法，将全部投影数据划分为多个子集，每使用一个子集的数据，全部像素被更新一次。所有子集轮流使用一遍为一次迭代。具体的，首先，确定计算似然函数的条件期望值的表达式；然后，通过用导数求极值法导出使似然函数的条件期望值达到最大时对应的像素更新值。每次更新像素得到的似然函数值都大于或等于上一次的值，像素值最终收敛到使似然函数达到最大。
在一个具体的实施例中，采用现有图像重建算法对PET扫描数据进行图像重建，例如，可以是内置于uEXPLORER PET-CT扫描仪控制系统的3D有序子集OSEM算法将每个PET扫描数据子集重建为体素大小为3.125×3.125×2.866mm3的192×192×673图像矩阵。图像重建使用了3次迭代、28个子集和2mm高斯平滑，还基于CT的衰减校正图像进行了衰减和散射校正。
进一步的，PET图像集中的图像为具有标准摄取值的图像。
标准摄取值(standard uptake value，SUV)指局部组织摄取的示踪剂的放射性活度与全身平均注射活度的比值，例如，可以是病灶处放射性摄取与全身平均摄取之比。除了血糖水平、受检者的体格、病灶的大小、感兴趣区的勾画、注射后显像时间和18F-FDG在血循环中的清除率等因素外，SUV还受设备性能、成像条件、采集模式、重建算法和衰减校正等因素的影响。
S130、根据PET图像集确定各像素对应的采样时间活度曲线，根据示踪剂标识以及预先创建的示踪剂标识与组织室模型的对应关系，确定采样时间活度曲线对应的组织室模型。
时间活度曲线(time‐activity curve，TAC)是反映区域内放射性示踪剂浓度的曲线，纵轴表示浓度，横轴表示时间，例如，可以是反映组织、血浆或其他感兴趣的区域中放射性示踪剂浓度的曲线。
组织室模型可选为不可逆的两组织室模型、可逆的两组织室模型和可逆的一组织室模型等。其中，不可逆的两组织室模型对应示踪剂标识可选为18F-FDG(氟代脱氧葡萄糖，完整的化学名称为2-氟-2-脱氧-D-葡萄糖)。
S140、基于组织室模型，修改组织室模型对应的每个像素点的灰度值对应的活度加和表达式，以更新活度加和表达式。
具体的，使用PET图像的每个像素点的灰度值代表组织室模型中的所有室的活度加和，并基于组织室模型对活度加和表达式进行更新，得到更新后的活度加和表达式。
S150、根据更新后活度加和表达式确定PET图像集对应的至少一个动态参数的数值，动态参数包括组织室模型中各组织室间的流动速度和/或示踪剂的净流入率。
根据更新后的活度加和表达式进行计算，使用线性估计方法计算组织室间的流动速度和/或示踪剂的净流入率。
进一步的，该方法包括：分别确定至少一个动态参数的数值对应的图像，以得到PET图像集合对应的至少一个动态参数图像。
具体的，根据动态数值确定对应的图像，也就是各组织室间的流动速度K1、k2和k3对应的K1图像、k2图像和k3图像和/或用于反映组织器官葡萄糖净代谢率对应的Ki图像。
本发明实施例的技术方案，通过获取被扫描部位的PET扫描数据，从PET扫描数据中提取示踪剂标识；对PET扫描数据进行图像重建以得到PET图像集；根据PET图像确定各像素对应的采样时间活度曲线，根据示踪剂标识以及预先创建的示踪剂标识与组织室模型的对应关系，确定采样时间活度曲线对应的组织室模型；基于组织室模型，修改组织室模型对应的每个像素点的灰度值对应的活度加和表达式，以更新活度加和表达式；根据更新后活度加和表达式确定PET图像集对应的至少一个动态参数的数值，动态参数包括组织室模型中各组织室间的流动速度和/或示踪剂的净流入率，实现了基于组织室模型以及对应的活度加和表达式，采用线性估计的方法确定PET图像的PET参数，提高了PET参数的估计速度。
图2是本发明实施例提供的另一种PET参数确定方法的流程图，本实施 例与上述实施例中的PET参数确定方法属于同一个发明构思，在上述实施例的基础上进一步的描述了根据更新后活度加和表达式确定PET图像集对应的至少一个动态参数的数值的过程。
如图2所示，PET参数确定方法包括以下步骤：
S210、获取被扫描部位的PET扫描数据，从PET扫描数据中提取示踪剂标。
S220、对PET扫描数据进行图像重建以得到PET图像集。
S230、根据PET图像集确定各像素对应的采样时间活度曲线，根据所述示踪剂标识以及预先创建的示踪剂标识与组织室模型的对应关系，确定采样时间活度曲线对应的组织室模型。
在一个实施例中，以示踪剂标识为18F-FDG，组织室模型为不可逆的两组织室模型为例进行技术方案的详细说明，其中，不可逆的两组织室模型可通过线性常微分方程组描述：
其中，Cp(t)、C1(t)和C2(t)分别对应血液中的FDG的活度、自由的FDG的活度和磷酸化的FDG的活度。K1、k2、k3和k4代表组织室间的流动速率。因为磷酸化过程在大部分组织中是不可逆过程，所以不可逆的两组织室模型中的k4为0。
S240、基于组织室模型，修改组织室模型对应的每个像素点的灰度值对应的活度加和表达式，以更新活度加和表达式。
首先，由PET图像集的图像，在早期(0-30s)的加和图像中勾画一个10mm×10mm×20mm的区域，于升主动脉弓获得血液输入函数Cp(t)。基于不可逆的两组织室模型的线性常微分方程组(1)，PET图像每个像素点的灰度值代表所有室的活度加和得到，具体如下：
CT(t)＝CBV·Cp(t)+C1(t)+C2(t)   (2)
其中CT(t)是组织内测量的活度，Cp(t)是血液内的活度，CBV是血液体积占比。然后，基于公式(1)对公式(2)进行改写，得到如下公式：
对公式(3)进行整理，以得到C1(t)的表达式，具体如下：
然后，将C1(t)代入公式(2)，其结果如下：
根据公式(5)得到C2(t)的表达式，具体如下：
根据线性常微分方程组(1)中的第二个公式，以及C1(t)和C2(t)得出：
S2501、对更新后的活动加和表达式进行同类项合并，以得到当前活度表达式。
对公式(7)进行同类项合并得到公式(8)，具体如下：
对公式(8)进行两次积分可以得到当前活度表达式：
S2502、采用第一设定参数集中的各第一设定参数分别替换当前活度表达式中的各变量的系数，以更新当前活度表达式；其中，第一设定参数集中的第一设定参数数量与变量的数量相同。
具体的，引入第一设定参数集，该第一设定参数集中的各参数的表达式如下：
采用第一设定参数集(10)中的各第一设定参数分别替换当前活度表达式中的各变量的系数，公式(9)被改写为：
S2503、根据当前活度表达式确定PET图像集合对应的各组织室间的流动速度数据。
首先，基于第一设定参数集(10)中的P1、P3和P4的表达式和PET图像 集合对应的图像数据得出：P3＝P1P4+K1，因此K1＝P3-P1P4；结果为一个最小二乘问题min||EP-C||，可以通过Lawson-Hanson NNLS(non-negative least squares，非负最小二乘)算法求解。快速线性计算后获得K1和CBV后，将CBV从公式(2)消除，改写公式(11)得到：
其中，CT *(t)是CT *(t)＝CT(t)-CBV·Cp(t)，根据PET图像数据和公式(12)即可计算出K1、k2和k3。
S2504、根据各组织室间的流动速度数据，以及示踪剂的净流入率数据与各组织室间的流动速度之间的对应关系，确定示踪剂的净流入率数据。
其中，示踪剂的净流入率(Ki)可间接指代葡萄糖的净代谢率水平，单位是ml/g/min，示踪剂的净流入率与各组织室间的流动速度之间的对应关系得到K1、k2和k3后，即可计算出示踪剂的净流入率数据Ki。
用户可根据需求选择感兴趣的区域的每个像素点计算Ki，组成Ki参数图像。
本实施例的技术方案，通过使用不可逆的两组织室模型确定了示踪剂的净流入率与各组织室间的流动速度，实现了基于组织室模型以及对应的活度加和表达式，采用线性估计的方法确定PET图像的PET参数，进一步提高了PET参数确定的速度。
图3是本发明实施例提供的又一种PET参数确定方法的流程图，本实施例与上述实施例中的PET参数确定方法属于同一个发明构思，在上述实施例的基础上进一步的根据更新后活度加和表达式确定PET图像集对应的至少一个动态参数的数值的过程。
如图3所示，PET参数确定方法包括以下步骤：
S310、获取被扫描部位的PET扫描数据，从PET扫描数据中提取示踪剂 标识。
S320、对PET扫描数据进行图像重建以得到PET图像集。
S330、根据PET图像集确定各像素对应的采样时间活度曲线，根据示踪剂标识以及预先创建的示踪剂标识与组织室模型的对应关系，确定采样时间活度曲线对应的组织室模型。
S340、基于组织室模型，修改组织室模型对应的每个像素点的灰度值对应的活度加和表达式，以更新活度加和表达式。
S3501、对更新后的活动加和表达式进行同类项合并，以得到当前活度表达式。
对公式(9)进行同类项合并得到当前活度表达式：
S3502、基于示踪剂的净流入率和各组织室间的流动速度之间的关系，对当前活度表达式进行变换，以更新当前活度表达式。
S3503、采用第二设定参数集中的各第二设定参数分别替换更新后的活度表达式中的各变量的系数，以更新当前活度表达式；其中，第二设定参数集中的第二设定参数数量与变量的数量相同。
具体的，引入第二设定参数集，该第二设定参数集中的各参数的表达式如下：
采用第二设定参数集中的各第二设定参数分别替换当前活度表达式中的各变量的系数，公式(13)被改写为：
S3504、根据更新后的当前活度表达式确定PET图像集合对应的示踪剂的净流入率。
首先，基于公式(15)和PET图像集对应的图像数据可以得出遍历每个像素点的组织室间的流动速度数据确定示踪剂的净流入率数据Ki。
本实施例的技术方案，通过使用不可逆的两组织室模型确定示踪剂的净流入率，实现了基于组织室模型以及对应的活度加和表达式，采用线性估计的方法确定PET图像的PET参数，进一步提高了PET参数的估计速度。
图4是本发明实施例提供的一种PET参数确定装置的结构框图，本实施例可适用于PET参数确定的场景。该装置可以采用硬件和/或软件的形式实现，集成于具有应用开发功能的计算机设备中。
如图4所示，该PET参数确定装置包括：
获取模块410，用于获取被扫描部位的PET扫描数据，从PET扫描数据中提取示踪剂标识；
图像重建模块420，用于对PET扫描数据进行图像重建以得到PET图像 集；
模型确定模块430，用于根据PET图像集确定各像素对应的采样时间活度曲线，根据所述示踪剂标识以及预先创建的示踪剂标识与组织室模型的对应关系，确定采样时间活度曲线对应的组织室模型；
模型更新模块440，用于基于组织室模型，修改组织室模型对应的每个像素点的灰度值对应的活度加和表达式，以更新活度加和表达式；
参数确定模块450，用于根据更新后活度加和表达式确定PET图像集对应的至少一个动态参数的数值，动态参数包括组织室模型中各组织室间的流动速度和/或示踪剂的净流入率。
可选的，参数确定模块450还用于：
对更新后的活动加和表达式进行同类项合并，以得到当前活度表达式；
采用第一设定参数集中的各第一设定参数分别替换当前活度表达式中的各变量的系数，以更新当前活度表达式；其中，第一设定参数集中的第一设定参数数量与变量的数量相同；
根据当前活度表达式确定所述PET图像集合对应的各组织室间的流动速度数据；
根据各组织室间的流动速度数据，以及示踪剂的净流入率数据与各组织室间的流动速度之间的对应关系，确定示踪剂的净流入率数据。
可选的，参数确定模块450还用于：
对更新后的活动加和表达式进行同类项合并，以得到当前活度表达式；
基于示踪剂的净流入率和各组织室间的流动速度之间的关系，对当前活度表达式进行变换，以更新当前活度表达式；
采用第二设定参数集中的各第二设定参数分别替换更新后的活度表达式中的各变量的系数，以更新当前活度表达式；其中，第二设定参数集中的第二设定参数数量与变量的数量相同；
根据更新后的当前活度表达式确定PET图像集合对应的示踪剂的净流入 率。
可选的，该装置还用于：分别确定至少一个动态参数的数值对应的图像，以得到PET图像集合对应的至少一个动态参数图像。
本实施例的技术方案，通过各个模块之间的相互配合，获取被扫描部位的PET扫描数据，从PET扫描数据中提取示踪剂标识；对PET扫描数据进行图像重建以得到PET图像集；根据PET图像集确定各像素对应的采样时间活度曲线，根据示踪剂标识以及预先创建的示踪剂标识与组织室模型的对应关系，确定采样时间活度曲线对应的设定组织室模型；基于组织室模型，修改组织室模型对应的每个像素点的灰度值对应的活度加和表达式，以更新活度加和表达式；根据更新后活度加和表达式确定PET图像集对应的至少一个动态参数的数值，动态参数包括组织室模型中各组织室间的流动速度和/或示踪剂的净流入率，实现了基于组织室模型以及对应的活度加和表达式，采用线性估计的方法确定PET图像的PET参数，提高了PET参数的估计速度。
本发明实施例所提供的PET参数确定装置可执行本发明任一实施例所提供的PET参数确定方法，具备执行方法相应的功能模块和有益效果。
图5是本发明的实施例提供的一种电子设备的结构框图。电子设备10旨在表示各种形式的数字计算机，诸如，膝上型计算机、台式计算机、工作台、个人数字助理、服务器、刀片式服务器、大型计算机或其它适合的计算机。电子设备还可以表示各种形式的移动装置，诸如，个人数字处理、蜂窝电话、智能电话、可穿戴设备(如头盔、眼镜、手表等)或其它类似的计算装置。本文所示的部件、它们的连接关系以及它们的功能仅仅作为示例，并且不意在限制本文中描述的和/或者要求的本发明的实现。
如图5所示，电子设备10包括至少一个处理器11，以及与至少一个处理器11通信连接的存储器，如只读存储器(ROM)12、随机访问存储器(RAM)13等，其中，存储器存储有可被至少一个处理器执行的计算机程序，处理器11可以根据存储在只读存储器(ROM)12中的计算机程 序或者从存储单元18加载到随机访问存储器(RAM)13中的计算机程序，来执行各种适当的动作和处理。在RAM 13中，还可存储电子设备10操作所需的各种程序和数据。处理器11、ROM 12以及RAM 13通过总线14彼此相连。输入/输出(I/O)接口15也连接至总线14。
电子设备10中的多个部件连接至I/O接口15，包括：输入单元16，例如键盘或鼠标等；输出单元17，例如各种类型的显示器或扬声器等；存储单元18，例如磁盘或光盘等；以及通信单元19，例如网卡、调制解调器或无线通信收发机等。通信单元19允许电子设备10通过诸如因特网的计算机网络和/或各种电信网络与其他设备交换信息/数据。
处理器11可以是各种具有处理和计算能力的通用和/或专用处理组件。处理器11的一些示例包括但不限于中央处理单元(CPU)、图形处理单元(GPU)、各种专用的人工智能(AI)计算芯片、各种运行机器学习模型算法的处理器、数字信号处理器(DSP)、任何适当的处理器、控制器或微控制器等。处理器11执行上文所描述的各个方法和处理，例如PET参数确定方法。
在一些实施例中，PET参数确定方法可被实现为计算机程序，其被有形地包含于计算机可读存储介质，例如存储单元18。在一些实施例中，计算机程序的部分或者全部可以经由ROM 12和/或通信单元19而被载入和/或安装到电子设备10上。当计算机程序加载到RAM 13并由处理器11执行时，可以执行上文描述的PET参数确定方法的一个或多个步骤。备选地，在其他实施例中，处理器11可以通过其他任何适当的方式(例如，借助于固件)而被配置为执行PET参数确定方法。
本文中以上描述的系统和技术的各种实施方式可以在数字电子电路系统、集成电路系统、场可编程门阵列(FPGA)、专用集成电路(ASIC)、专用标准产品(ASSP)、芯片上系统的系统(SOC)、负载可编程逻辑设备(CPLD)、计算机硬件、固件、软件和/或它们的组合中实现。这些各 种实施方式可以包括：实施在一个或者多个计算机程序中，该一个或者多个计算机程序可在包括至少一个可编程处理器的可编程系统上执行和/或解释，该可编程处理器可以是专用或者通用可编程处理器，可以从存储系统、至少一个输入装置和至少一个输出装置接收数据和指令，并且将数据和指令传输至该存储系统、该至少一个输入装置和该至少一个输出装置。
用于实施本发明的方法的计算机程序可以采用一个或多个编程语言的任何组合来编写。这些计算机程序可以提供给通用计算机、专用计算机或其他可编程数据处理装置的处理器，使得计算机程序当由处理器执行时使流程图和/或框图中所规定的功能/操作被实施。计算机程序可以完全在机器上执行或部分地在机器上执行，作为独立软件包部分地在机器上执行且部分地在远程机器上执行或完全在远程机器或服务器上执行。
在本发明的上下文中，计算机可读存储介质可以是有形的介质，其可以包含或存储以供指令执行系统、装置或设备使用或与指令执行系统、装置或设备结合地使用的计算机程序。计算机可读存储介质可以包括但不限于电子的、磁性的、光学的、电磁的、红外的或半导体系统、装置或设备，或者上述内容的任何合适组合。备选地，计算机可读存储介质可以是机器可读信号介质。机器可读存储介质的更具体示例会包括基于一个或多个线的电气连接、便携式计算机盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦除可编程只读存储器(EPROM或快闪存储器)、光纤、便捷式紧凑盘只读存储器(CD-ROM)、光学储存设备、磁储存设备或上述内容的任何合适组合。
为了提供与用户的交互，可以在电子设备上实施此处描述的系统和技术，该电子设备具有：用于向用户显示信息的显示装置(例如，CRT(Cathode Ray Tube，阴极射线管)或者LCD(Liquid Crystal Display，液晶显示器)监视器)；以及键盘和指向装置(例如，鼠标或者轨迹球)，用户可以通过该键盘和该指向装置来将输入提供给电子设备。其它种类的装置还可以 用于提供与用户的交互；例如，提供给用户的反馈可以是任何形式的传感反馈(例如，视觉反馈、听觉反馈或者触觉反馈)；并且可以用任何形式(包括声输入、语音输入或者触觉输入)来接收来自用户的输入。
可以将此处描述的系统和技术实施在包括后台部件的计算系统(例如，作为数据服务器)、或者包括中间件部件的计算系统(例如，应用服务器)、或者包括前端部件的计算系统(例如，具有图形用户界面或者网络浏览器的用户计算机，用户可以通过该图形用户界面或者该网络浏览器来与此处描述的系统和技术的实施方式交互)或者包括这种后台部件、中间件部件、或者前端部件的任何组合的计算系统中。可以通过任何形式或者介质的数字数据通信(例如，通信网络)来将系统的部件相互连接。通信网络的示例包括：局域网(LAN)、广域网(WAN)、区块链网络和互联网。
计算系统可以包括客户端和服务器。客户端和服务器一般远离彼此并且通常通过通信网络进行交互。通过在相应的计算机上运行并且彼此具有客户端-服务器关系的计算机程序来产生客户端和服务器的关系。服务器可以是云服务器，又称为云计算服务器或云主机，是云计算服务体系中的一项主机产品，以解决了传统物理主机与VPS服务中，存在的管理难度大，业务扩展性弱的缺陷。
应该理解，可以使用上面所示的各种形式的流程，重新排序、增加或删除步骤。例如，本发明中记载的各步骤可以并行地执行也可以顺序地执行也可以不同的次序执行，只要能够实现本发明的技术方案所期望的结果，本文在此不进行限制。
上述具体实施方式，并不构成对本发明保护范围的限制。本领域技术人员应该明白的是，根据设计要求和其他因素，可以进行各种修改、组合、子组合和替代。任何在本发明的精神和原则之内所作的修改、等同替换和改进等，均应包含在本发明保护范围之内。

Claims (10)

  1. 一种PET参数确定方法，其特征在于，包括：
    获取被扫描部位的PET扫描数据，从所述PET扫描数据中提取示踪剂标识；
    对所述PET扫描数据进行图像重建以得到PET图像集；
    根据所述PET图像集确定各像素对应的采样时间活度曲线，根据所述示踪剂标识以及预先创建的示踪剂标识与组织室模型的对应关系，确定所述采样时间活度曲线对应的组织室模型；
    基于所述组织室模型，修改所述组织室模型对应的每个像素点的灰度值对应的活度加和表达式，以更新所述活度加和表达式；
    根据更新后所述活度加和表达式确定所述PET图像集对应的至少一个动态参数的数值，所述动态参数包括所述组织室模型中各组织室间的流动速度和/或示踪剂的净流入率。
  2. 根据权利要求1所述的方法，其特征在于，所述根据更新后所述活度加和表达式确定所述PET图像集对应的至少一个动态参数的数值，包括：
    对所述更新后的活动加和表达式进行同类项合并，以得到当前活度表达式；
    采用第一设定参数集中的各第一设定参数分别替换所述当前活度表达式中的各变量的系数，以更新所述当前活度表达式；其中，所述第一设定参数集中的第一设定参数数量与所述变量的数量相同；
    根据所述当前活度表达式确定所述PET图像集合对应的各组织室间的流动速度数据；
    根据所述各组织室间的流动速度数据，以及所述示踪剂的净流入率数据与各组织室间的流动速度之间的对应关系，确定示踪剂的净流入率数据。
  3. 根据权利要求1所述的方法，其特征在于，所述根据更新后所述活度加和表达式确定所述PET图像集对应的至少一个动态参数的数值，包括：
    对所述更新后的活动加和表达式进行同类项合并，以得到当前活度表达式；
    基于所述示踪剂的净流入率和各所述组织室间的流动速度之间的关系，对所述当前活度表达式进行变换，以更新当前活度表达式；
    采用第二设定参数集中的各第二设定参数分别替换更新后的所述活度表达式中的各变量的系数，以更新所述当前活度表达式；其中，所述第二设定参数集中的第二设定参数数量与所述变量的数量相同；
    根据更新后的所述当前活度表达式确定所述PET图像集合对应的所述示踪剂的净流入率。
  4. 根据权利要求1所述的方法，其特征在于，所述示踪剂标识为18FDG，所述组织室模型为不可逆的两组织室模型。
  5. 根据权利要求1项所述的方法，其特征在于，
    所述组织室模型为可逆的一组织室模型。
  6. 根据权利要求1所述的方法，其特征在于，
    所述PET图像集中的图像为具有标准摄取值的图像。
  7. 根据权利要求1-6中任一项所述的方法，其特征在于，还包括：
    分别确定所述至少一个动态参数的数值对应的图像，以得到所述PET图像集合对应的至少一个动态参数图像。
  8. 一种PET参数确定装置，其特征在于，包括：
    获取模块，用于获取被扫描部位的PET扫描数据，从所述PET扫描数据中提取示踪剂标识；
    图像重建模块，用于对所述PET扫描数据进行图像重建以得到PET图像集；
    模型确定模块，用于根据所述PET图像集确定各像素对应的采样时间活度曲线，根据所述示踪剂标识以及预先创建的示踪剂标识与组织室模型的对应关系，确定所述采样时间活度曲线对应的组织室模型；
    模型更新模块，用于基于所述组织室模型，修改所述组织室模型对应的每个像素点的灰度值对应的活度加和表达式，以更新所述活度加和表达式；
    参数确定模块，用于根据更新后所述活度加和表达式确定所述PET图像集对应的至少一个动态参数的数值，所述动态参数包括所述组织室模型中各组织室间的流动速度和/或示踪剂的净流入率。
  9. 一种电子设备，其特征在于，所述电子设备包括：
    至少一个处理器；以及
    与所述至少一个处理器通信连接的存储器；其中，
    所述存储器存储有可被所述至少一个处理器执行的计算机程序，所述计算机程序被所述至少一个处理器执行，以使所述至少一个处理器能够执行权利要求1-7中任一项所述的PET参数确定方法。
  10. 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质存储有计算机指令，所述计算机指令用于使处理器执行时实现权利要求1-7中任一项所述的PET参数确定方法。
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WO2019136469A1 (en) * 2018-01-08 2019-07-11 The Regents Of The University Of California Time-varying kinetic modeling of high temporal-resolution dynamic pet data for multiparametric imaging
CN110996800A (zh) * 2018-08-01 2020-04-10 联影美国公司 用于确定pet成像动力学参数的系统、方法
US20210295501A1 (en) * 2020-03-19 2021-09-23 Shanghai United Imaging Healthcare Co., Ltd. Systems and methods for image reconstruction and processing
CN115861205A (zh) * 2022-11-24 2023-03-28 中国科学院深圳先进技术研究院 Pet参数确定方法、装置、设备和存储介质

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