Quantitative parametric brain imaging method and apparatus, and device and medium

Abstract

A quantitative parametric brain imaging method and apparatus, and a device and a medium. The method comprises: on the basis of a short-axis-view positron emission computed tomography (PET) imaging result of a brain, acquiring a reference drug-metabolism time-activity curve of a preset reference brain part (S110); on the basis of a mapping relationship between the reference drug-metabolism time-activity curve and a drug-metabolism time-activity curve of the descending aorta, determining a target drug-metabolism time-activity curve of the descending aorta (S120); and on the basis of the target drug-metabolism time-activity curve and a preset pharmacokinetic model, completing a target cerebral pharmacokinetic parametric image (S130).

Description

Brain quantitative parameter imaging method, device, equipment and medium

[0001] This application claims priority to the Chinese patent application filed with the China Patent Office on November 30, 2023, with application number 202311627417.6, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of medical image processing technology, for example, to a method, apparatus, device and medium for quantitative parameter imaging of the brain. Background Art

[0003] Brain pharmacokinetic parameter imaging technology based on dynamic positron emission tomography (PET) has played an important auxiliary role in the early and accurate diagnosis of clinical brain tumors, epilepsy and neurodegenerative diseases.

[0004] Brain pharmacokinetic parameter imaging is achieved based on PET scanning with a long axial field of view. During the imaging process, brain dynamic images and cardiopulmonary dynamic images can be acquired synchronously, so as to obtain the descending aorta blood input function from the cardiopulmonary dynamic images for brain pharmacokinetic parameter image calculation.

[0005] However, in the short-axial field of view PET imaging system, it is impossible to perform dynamic imaging of the brain and lungs simultaneously, and it is also impossible to obtain the blood input function of the descending aorta, which affects the image calculation of brain pharmacokinetic parameters.

[0006] Summary of the Invention

[0007] The present application provides a method, apparatus, device and medium for quantitative parameter imaging of the brain, which can obtain the blood input function for brain parameter imaging under short-axis PET scanning conditions, and realize the stable and efficient completion of dynamic PET pharmacokinetic parameter analysis imaging tasks based only on dynamic brain images.

[0008] The present invention provides a method for quantitative parameter imaging of the brain, comprising:

[0009] Based on the brain PET imaging results under the short-axis field of view, obtain the reference drug metabolism time activity curve of the preset brain reference site;

[0010] determining a target drug metabolism time-activity curve of the descending aorta based on a mapping relationship between the reference drug metabolism time-activity curve and the drug metabolism time-activity curve of the descending aorta;

[0011] Based on the target drug metabolism time-activity curve and the preset pharmacokinetic model, a target brain drug metabolism kinetic parameter image is completed.

[0012] The present application also provides a device for quantitative parameter imaging of the brain, which includes:

[0013] A reference data acquisition module is configured to obtain a reference drug metabolism time activity curve of a preset brain reference site based on the brain PET imaging results under the short-axis field of view;

[0014] a target data acquisition module configured to determine a target drug metabolism time-activity curve of the descending aorta based on a mapping relationship between the reference drug metabolism time-activity curve and the drug metabolism time-activity curve of the descending aorta;

[0015] The brain parameter imaging module is configured to complete a target brain pharmacokinetic parameter image based on the target drug metabolism time activity curve and a preset pharmacokinetic model.

[0016] The present application also provides a computer device, which includes:

[0017] one or more processors;

[0018] a memory configured to store one or more programs;

[0019] When the one or more programs are executed by the one or more processors, the one or more processors implement the brain quantitative parameter imaging method provided in any embodiment of the present application.

[0020] An embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for quantitative parameter imaging of the brain as provided in any embodiment of the present application is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] FIG1 is a flow chart of a method for quantitative parameter imaging of the brain provided in an embodiment of the present application;

[0022] FIG2 is a flow chart of another method for quantitative brain parameter imaging provided in an embodiment of the present application;

[0023] FIG3 is a flow chart of another method for quantitative brain parameter imaging provided in an embodiment of the present application;

[0024] FIG4 is a comparison diagram of brain parameter imaging results provided in an embodiment of the present application;

[0025] FIG5 is a schematic structural diagram of a brain quantitative parameter imaging device provided in an embodiment of the present application;

[0026] FIG6 is a schematic structural diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0027] The present application is described below in conjunction with the accompanying drawings and embodiments. The embodiments described herein are intended only to explain the present application and are not intended to limit the present application. For ease of description, the accompanying drawings only show portions related to the present application, not all structures.

[0028] Figure 1 is a flowchart of a method for quantitative brain parameter imaging provided in an embodiment of the present application. This embodiment is applicable to scenarios involving quantitative brain parameter imaging, such as PET imaging under short-axis imaging conditions. The method can be performed by a quantitative brain parameter imaging device, which can be implemented using software and / or hardware and integrated into a computer device with application development capabilities.

[0029] As shown in FIG1 , the brain quantitative parameter imaging method of this embodiment includes the following steps.

[0030] S110. Based on the brain PET imaging results in the short-axis field of view, a reference drug metabolism time activity curve of a preset brain reference site is obtained.

[0031] Dynamic PET imaging can track the uptake of radiotracers and directly reflect specific biochemical uptake processes. The local brain glucose metabolism rate is an important indicator of brain function. Dynamic PET combined with kinetic analysis can monitor the dynamic changes of radiotracers in the human body. Pharmacokinetic models can be established based on the glucose uptake process, and derived parameters can reflect blood perfusion and internal tissue metabolism.

[0032] In the process of determining quantitative parameter imaging of the brain, the drug (glucose) metabolism time activity curve of the descending aorta is required as a blood input function and input into the corresponding pharmacokinetic model to obtain the target brain pharmacokinetic parameter image.

[0033] Short-axis PET imaging of the brain has a relatively smaller field of view than the short-axis field of view within the imaging coordinate system. Using short-axis PET imaging can reduce the patient's radiation dose compared to long-axis PET imaging. However, it cannot directly obtain a time-activity curve for drug (glucose) metabolism in the descending aorta.

[0034] In this embodiment, the drug (glucose) metabolism time-activity curve at the descending aorta is estimated using a reference drug metabolism time-activity curve at a preset brain reference site. The preset brain reference site is a part of the brain through which blood flows, including the carotid artery or cavernous sinus.

[0035] S120 . Determine a target drug metabolism time-activity curve of the descending aorta based on a mapping relationship between the reference drug metabolism time-activity curve and the drug metabolism time-activity curve of the descending aorta.

[0036] The mapping relationship between the reference drug metabolism time-activity curve and the drug metabolism time-activity curve of the descending aorta is determined based on analysis of synchronously acquired drug metabolism time-activity curve data of a preset brain reference site and the descending aorta.

[0037] By inputting the reference drug metabolism time-activity curve into the above mapping relationship, a relatively accurate estimation result of the target drug metabolism time-activity curve can be quickly obtained.

[0038] S130 , completing a target brain pharmacokinetic parameter image based on the target drug metabolism time-activity curve and a preset pharmacokinetic model.

[0039] The target drug metabolism time-activity curve and other determined related parameters are input into a preset pharmacokinetic model to obtain a target brain drug metabolism kinetic parameter image.

[0040] Among them, the preset pharmacokinetic model can be a Patlak diagram model or a modified Logan model for brain parameter analysis.

[0041] The technical solution of this embodiment obtains a reference drug metabolism time-activity curve for a preset brain reference site based on brain PET imaging results under a short-axis field of view; determines a target drug metabolism time-activity curve for the descending aorta based on a mapping relationship between the reference drug metabolism time-activity curve and the drug metabolism time-activity curve of the descending aorta; and completes a target brain pharmacokinetic parameter image based on the target drug metabolism time-activity curve and a preset pharmacokinetic model. The technical solution of this embodiment of the application solves the problem of being unable to obtain a drug metabolism time-activity curve for the descending aorta during short-axis PET imaging, and can achieve accurate brain pharmacokinetic parameter imaging under short-axis PET scanning conditions.

[0042] Figure 2 is a flow chart of a method for quantitative brain parameter imaging provided in an embodiment of the present application. This embodiment shares the same concept as the method described above and describes the process of determining a target drug metabolism time-activity curve. This method can be performed by a quantitative brain parameter imaging device, which can be implemented using software and / or hardware and integrated into a computer device with application development capabilities.

[0043] As shown in FIG2 , the brain quantitative parameter imaging method of this embodiment includes the following steps.

[0044] S210. Based on the brain PET imaging results in the short-axis field of view, a reference drug metabolism time activity curve of a preset brain reference site is obtained.

[0045] S220 , dividing the reference drug metabolism time-activity curve into a first-stage reference curve and a second-stage reference curve according to curve characteristics of the analysis curve corresponding to the preset pharmacokinetic model.

[0046] The curve characteristic of the analysis curve corresponding to the preset pharmacokinetic model can be the curve type of the analysis curve, such as a linear curve and a nonlinear curve. For different curve types, curve mapping relationships can be analyzed separately to obtain more accurate analysis results.

[0047] For example, the preset pharmacokinetic model can be a Patlak diagram model. The mapping relationship between the reference drug metabolism time activity curve and the drug metabolism time activity curve of the descending aorta can be expressed as

[0048] Among them, C T and C P are the reference drug metabolism time-activity curve and the descending aorta drug metabolism time-activity curve, t is time, K i and V represent the net metabolic rate of glucose and the distribution volume of the tracer drug, respectively, and τ represents time. In the integral expression, since the letter t is used to indicate the upper limit of integration, τ is used to indicate the direction of integration.

[0049] Based on the curve characteristics of the analysis curve corresponding to the Patlak plot model, the Patlak curve is not linear from the beginning, but begins to enter a "linear segment" approximately 20 to 24 minutes after drug injection. The initial nonlinear segment is the first-stage reference curve, and the linear segment is the second-stage reference curve.

[0050] After the reference drug metabolism time-activity curve is divided into a first-stage reference curve and a second-stage reference curve, the mapping relationship between the reference drug metabolism time-activity curve and the drug metabolism time-activity curve of the descending aorta can be expressed as:

[0051] in, The integral value of the first phase curve of the drug metabolism time activity curve of the descending aorta (t <t * ), t≥t * hour, The second phase curve of the drug metabolism time activity curve corresponding to the descending aorta, t * Indicates the starting point of the Patlak curve entering the linear section, or the starting point of the second stage.

[0052] S230 : Determine a first-stage target drug metabolism time-activity curve according to a first mapping relationship between the first-stage reference curve and the first-stage drug metabolism time-activity curve corresponding to the descending aorta.

[0053] The first-stage reference curve is input into the linear function corresponding to the first mapping relationship to obtain the first-stage target drug metabolism time-activity curve.

[0054] The time-activity curve of target drug metabolism in the first stage can be expressed as E a =k·E h +b, where E h is the reference curve of the first stage, E a This is the time-activity curve of the first-stage target drug metabolism in the descending aorta. k and b are the first mapping relationships determined in advance.

[0055] S240 : Determine a second-stage target drug metabolism time-activity curve according to a second mapping relationship between the second-stage reference curve and the second-stage drug metabolism time-activity curve corresponding to the descending aorta.

[0056] The second-stage reference curve can be input into the linear function corresponding to the second mapping relationship to obtain the second-stage target drug metabolism time-activity curve.

[0057] The second mapping relationship is determined based on the interpolation statistics between the second-stage reference curve and the corresponding second-stage descending aorta drug metabolism time-activity curve. Since the second-stage drug metabolism time-activity curve corresponding to the Patlak plot model is a linear curve, the second-stage target drug metabolism time-activity curve can be determined based on the difference statistics between the second-stage drug metabolism time-activity curves at different locations.

[0058] The time-activity curve of target drug metabolism in the second phase can be expressed as:

[0059] Among them, MD represents the difference statistical result. is the reference curve for the second stage, This is the time-activity curve of target drug metabolism in the second phase.

[0060] S250 . Determine the target drug metabolism time-activity curve of the descending aorta according to the first-stage target drug metabolism time-activity curve and the second-stage target drug metabolism time-activity curve.

[0061] The target drug metabolism time-activity curve of the first stage and the target drug metabolism time-activity curve of the second stage are superimposed to form the target drug metabolism time-activity curve of the descending aorta.

[0062] S260: Based on the target drug metabolism time-activity curve and a preset pharmacokinetic model, a target brain pharmacokinetic parameter image is completed.

[0063] The first-stage target drug metabolism time-activity curve, the second-stage target drug metabolism time-activity curve, and the reference drug metabolism time-activity curve of the preset brain reference site are input into the above formula (2) to obtain K i and the value of V, thus obtaining the target brain pharmacokinetic parameter image.

[0064] The technical solution of this embodiment is to obtain a reference drug metabolism time-activity curve of a preset brain reference site based on the brain PET imaging results under the short-axis field of view; divide the reference drug metabolism time-activity curve into a first-stage reference curve and a second-stage reference curve according to the curve characteristics of the analysis curve corresponding to the preset pharmacokinetic model; determine the first-stage target drug metabolism time-activity curve according to a first mapping relationship between the first-stage reference curve and the first-stage drug metabolism time-activity curve corresponding to the descending aorta; determine the second-stage target drug metabolism time-activity curve according to a second mapping relationship between the second-stage reference curve and the second-stage drug metabolism time-activity curve corresponding to the descending aorta; determine the target drug metabolism time-activity curve of the descending aorta according to the first-stage target drug metabolism time-activity curve and the second-stage target drug metabolism time-activity curve; and complete the target brain pharmacokinetic parameter image based on the target drug metabolism time-activity curve and the preset pharmacokinetic model. The technical solution of the embodiment of the present application solves the problem that the drug metabolism time-activity curve of the descending aorta cannot be obtained during short-axis PET imaging, and can achieve accurate brain pharmacokinetic parameter imaging under short-axis PET scanning conditions. In this embodiment, there is no need to establish a population-based blood input function through a large number of dynamic PET medical imaging data sets, and the dependence on the size of the data set is relatively small. Secondly, the theory and implementation method implemented in this embodiment are relatively simple, and can ensure stable and efficient completion of dynamic PET pharmacokinetic parameter analysis imaging tasks in a short-axis PET imaging system.

[0065] Figure 3 is a flow chart of a method for quantitative brain parameter imaging provided in an embodiment of the present application. This embodiment shares the same concept as the method described above and illustrates the process of mapping a reference drug metabolism time-activity curve to a drug metabolism time-activity curve of the descending aorta. This method can be performed by a quantitative brain parameter imaging device, which can be implemented using software and / or hardware and integrated into a computer device with application development capabilities.

[0066] As shown in FIG3 , the brain quantitative parameter imaging method of this embodiment includes the following steps.

[0067] S3010. Obtain a set of drug metabolism time-activity curve pairs of a preset brain reference site and the descending aorta determined based on the synchronous PET imaging results of the brain and the heart and lungs in the long-axis field of view.

[0068] Based on the acquisition of whole-body dynamic PET imaging data, a set of paired time-activity curves for drug metabolism in a predetermined reference brain region (e.g., cavernous sinus) and the descending aorta can be constructed. Based on this set, the mapping relationship between the two can be analyzed.

[0069] S3020. Perform curve segmentation on each drug metabolism time-activity curve pair in the set according to the curve characteristics of the analysis curve corresponding to the preset pharmacokinetic model to obtain the corresponding first-stage drug metabolism time-activity curve pair and second-stage drug metabolism time-activity curve pair.

[0070] Based on the curve characteristics of the analysis curve corresponding to the Patlak plot model, the Patlak curve is not linear from the beginning, but begins to enter a "linear segment" approximately 20 to 24 minutes after drug injection. In this embodiment, the mapping relationship between the reference drug metabolism time-activity curve and the drug metabolism time-activity curve of the descending aorta is analyzed in stages.

[0071] The drug metabolism time-activity curve pair in the initial nonlinear segment is the first-stage drug metabolism time-activity curve pair; the drug metabolism time-activity curve pair in the linear segment is the second-stage drug metabolism time-activity curve pair. Due to the segmented processing of the curve, the rewriting of the Patlak parameter imaging model can be expressed as:

[0072] in, The integral value of the first phase curve of the drug metabolism time activity curve of the descending aorta (t <t * ), t≥t * hour, This corresponds to the second phase curve of the drug metabolism time activity curve of the descending aorta.

[0073] S3030. Perform linear fitting based on each first-stage drug metabolism time-activity curve to obtain the first mapping relationship.

[0074] The first mapping relationship can be obtained by linear fitting to obtain the mapping relationship between the preset brain reference position and the descending aorta curve in the first stage, that is, E h With E a The approximate linear mapping relationship between: E a =k·E h +b

[0075] Based on each first-stage drug metabolism time-activity curve, linear fitting is performed to obtain the function parameters k and b. h is the time-activity curve of the first phase drug metabolism in the preset brain reference site, E a This is the time-activity curve of the first phase drug metabolism in the descending aorta.

[0076] S3040. Determine the mean of the differences between multiple second-stage drug metabolism time-activity curve pairs based on the difference relationship hypothesis of the second-stage drug metabolism time-activity curve pairs, and use the mean as the second mapping relationship.

[0077] The difference relationship between the time-activity curves of the second stage drug metabolism can be expressed as:

[0078] Wherein, MD is the mean of the differences between multiple pairs of phase II drug metabolism time-activity curves.

[0079] The calculation method of MD is:

[0080] Wherein, N represents the total number of time-activity curve pairs of drug metabolism in the second stage, i.e., the size of the database used to establish the model of the method of this embodiment. In the formula, the integral is the discrete time domain integral, t end is the end point of the dynamic PET scan time, and n represents the data number (n ranges from 1 to N).

[0081] S3050. Based on the brain PET imaging results under the short-axis field of view, obtain a reference drug metabolism time activity curve of a preset brain reference site.

[0082] S3060. Divide the reference drug metabolism time activity curve into a first-stage reference curve and a second-stage reference curve according to the curve characteristics.

[0083] In actual application, the curve is segmented and the Patlak parameter imaging model can be expressed as:

[0084] The subscript pred represents data determined in the current brain PET imaging results based on the short-axis field of view.

[0085] S3070: Determine a first-stage target drug metabolism time-activity curve based on the first-stage reference curve and the first mapping relationship.

[0086] S3080: Determine a second-stage target drug metabolism time-activity curve based on the second-stage reference curve and the second mapping relationship.

[0087] S3090. Determine the target drug metabolism time-activity curve of the descending aorta based on the first-stage target drug metabolism time-activity curve and the second-stage target drug metabolism time-activity curve.

[0088] S3100 , completing a target brain pharmacokinetic parameter image based on the target drug metabolism time-activity curve and a preset pharmacokinetic model.

[0089] The target drug metabolism time activity curve is input into the corresponding formula of S3060, and K can be obtained by fitting and calculation. i and V, thereby obtaining the target brain pharmacokinetic parameter image.

[0090] In one example, a comparison of target brain pharmacokinetic parameter images obtained using the methods provided in the above embodiments and pharmacokinetic parameter images obtained using other methods is shown in Figure 4. The reference images in the rightmost column can be gold standard images used as labels, the images in the middle column are images obtained using the methods of this embodiment, and the images on the leftmost column are images obtained using methods in related art. It can be clearly determined that the images obtained using the methods of this embodiment are closer to the reference images.

[0091] The technical solution of this embodiment is to obtain a set of drug metabolism time-activity curve pairs of a preset brain reference site and the descending aorta determined based on the synchronous PET imaging results of the brain and the heart and lungs under the long-axis field of view; perform curve segmentation on each drug metabolism time-activity curve pair in the set according to the curve characteristics of the analysis curve corresponding to the preset pharmacokinetic model to obtain the corresponding first-stage drug metabolism time-activity curve pair and second-stage drug metabolism time-activity curve pair; perform linear fitting based on each first-stage drug metabolism time-activity curve pair to obtain the first mapping relationship; determine the mean of the differences between multiple second-stage drug metabolism time-activity curve pairs based on the difference relationship hypothesis of the second-stage drug metabolism time-activity curve pairs, and use the mean as the second mapping relationship; based on the short-axis field of view, perform curve segmentation on each drug metabolism time-activity curve pair in the set to obtain the corresponding first-stage drug metabolism time-activity curve pair and second-stage drug metabolism time-activity curve pair; based on the short-axis field of view, perform linear fitting on each first-stage drug metabolism time-activity curve pair to obtain the first mapping relationship; based on the difference relationship hypothesis of the second-stage drug metabolism time-activity curve pairs, determine the mean of the differences between multiple second-stage drug metabolism time-activity curve pairs, and use the mean as the second mapping relationship; based on the short-axis field of view, perform curve segmentation on each drug metabolism time-activity curve pair in the set to obtain the corresponding first-stage drug metabolism time-activity curve pair and second-stage drug metabolism time-activity curve pair; The brain PET imaging results under the field are used to obtain the reference drug metabolism time activity curve of the preset brain reference part; according to the curve characteristics, the reference drug metabolism time activity curve is divided into a first-stage reference curve and a second-stage reference curve; according to the first-stage reference curve and the first mapping relationship, the first-stage target drug metabolism time activity curve is determined; according to the second-stage reference curve and the second mapping relationship, the second-stage target drug metabolism time activity curve is determined; according to the first-stage target drug metabolism time activity curve and the second-stage target drug metabolism time activity curve, the target drug metabolism time activity curve of the descending aorta is determined; based on the target drug metabolism time activity curve and the preset pharmacokinetic model, the target brain pharmacokinetic parameter image is completed. The technical solution of the embodiment of the present application solves the problem that the drug metabolism time activity curve of the descending aorta cannot be obtained during short-axis PET imaging, and can achieve accurate brain pharmacokinetic parameter imaging under short-axis PET scanning conditions. In this embodiment, there is no need to establish a population-based blood input function through a large number of dynamic PET medical imaging data sets, and the dependence on the size of the data set is relatively small. Secondly, the theory and implementation method implemented in this embodiment are relatively simple, and can ensure stable and efficient completion of dynamic PET pharmacokinetic parameter analysis imaging tasks in a short-axis PET imaging system.

[0092] Figure 5 is a structural schematic diagram of a brain quantitative parameter imaging device provided in an embodiment of the present application. This embodiment can be applied to scenarios of brain quantitative parameter imaging, such as brain quantitative parameter imaging under PET imaging conditions in a short-axis imaging field of view. The brain quantitative parameter imaging device can be implemented by software and / or hardware and integrated into a computer terminal device with application development capabilities.

[0093] As shown in FIG5 , the brain quantitative parameter imaging device includes: a reference data acquisition module 410 , a target data acquisition module 420 and a brain parameter imaging module 430 .

[0094] Among them, the reference data acquisition module 410 is configured to obtain a reference drug metabolism time-activity curve of a preset brain reference site based on the brain PET imaging results under the short-axis field of view; the target data acquisition module 420 is configured to determine the target drug metabolism time-activity curve of the descending aorta based on the mapping relationship between the reference drug metabolism time-activity curve and the drug metabolism time-activity curve of the descending aorta; the brain parameter imaging module 430 is configured to complete the target brain pharmacokinetic parameter image based on the target drug metabolism time-activity curve and a preset pharmacokinetic model.

[0095] The technical solution of this embodiment obtains a reference drug metabolism time-activity curve for a preset brain reference site based on brain PET imaging results under a short-axis field of view; determines a target drug metabolism time-activity curve for the descending aorta based on a mapping relationship between the reference drug metabolism time-activity curve and the drug metabolism time-activity curve of the descending aorta; and completes a target brain pharmacokinetic parameter image based on the target drug metabolism time-activity curve and a preset pharmacokinetic model. The technical solution of this embodiment of the application solves the problem of being unable to obtain a drug metabolism time-activity curve for the descending aorta during short-axis PET imaging, and can achieve accurate brain pharmacokinetic parameter imaging under short-axis PET scanning conditions.

[0096] In an optional implementation, the target data acquisition module 420 is configured to:

[0097] According to the curve characteristics of the analysis curve corresponding to the preset pharmacokinetic model, the reference drug metabolism time activity curve is divided into a first-stage reference curve and a second-stage reference curve;

[0098] determining a first-stage target drug metabolism time-activity curve according to a first mapping relationship between the first-stage reference curve and the first-stage drug metabolism time-activity curve corresponding to the descending aorta;

[0099] determining a second-stage target drug metabolism time-activity curve according to a second mapping relationship between the second-stage reference curve and the second-stage drug metabolism time-activity curve corresponding to the descending aorta;

[0100] The target drug metabolism time activity curve of the descending aorta is determined according to the first-stage target drug metabolism time activity curve and the second-stage target drug metabolism time activity curve.

[0101] In an optional implementation, the target data acquisition module 420 may be configured to:

[0102] The first-stage reference curve is input into the linear function corresponding to the first mapping relationship to obtain the first-stage target drug metabolism time-activity curve.

[0103] In an optional implementation, the target data acquisition module 420 may be configured to:

[0104] The second-stage reference curve is input into the linear function corresponding to the second mapping relationship to obtain the second-stage target drug metabolism time-activity curve.

[0105] In an optional embodiment, the brain quantitative parameter imaging device includes a mapping relationship determination module configured to:

[0106] Obtaining a set of drug metabolism time-activity curve pairs between a preset brain reference site and the descending aorta, determined based on the results of simultaneous PET imaging of the brain and heart and lungs in the long-axis field of view;

[0107] performing curve segmentation on each drug metabolism time-activity curve pair in the set according to the curve characteristics to obtain a first-stage drug metabolism time-activity curve pair corresponding to each drug metabolism time-activity curve pair;

[0108] The first mapping relationship is obtained by performing linear fitting based on each first-stage drug metabolism time-activity curve.

[0109] In an optional implementation, the mapping relationship determination module may also be configured to:

[0110] Determining, based on each first-phase drug metabolism time-activity curve pair, a second-phase drug metabolism time-activity curve pair corresponding to each first-phase drug metabolism time-activity curve pair;

[0111] According to the difference relationship hypothesis of the second-stage drug metabolism time-activity curve pairs, the average of the differences between the plurality of second-stage drug metabolism time-activity curve pairs is determined, and the average is used as the second mapping relationship.

[0112] In an optional embodiment, the preset brain reference site is a site in the brain where blood flows, including the carotid artery or the cavernous sinus.

[0113] The brain quantitative parameter imaging device provided in the embodiments of the present application can execute the brain quantitative parameter imaging method provided in any embodiment of the present application, and has the corresponding functional modules and effects of the execution method.

[0114] Figure 6 is a schematic diagram of the structure of a computer device provided in an embodiment of the present application. Figure 6 shows a block diagram of an exemplary computer device 12 suitable for implementing the embodiments of the present application. The computer device 12 shown in Figure 6 is merely an example and should not limit the functionality and scope of use of the embodiments of the present application. The computer device 12 can be any terminal device with computing capabilities, such as an intelligent controller, server, mobile phone, or other terminal device.

[0115] 6 , computer device 12 is implemented as a general-purpose computing device. Components of computer device 12 may include: one or more processors or processing units 16 , system memory 28 , and a bus 18 that connects various system components (including system memory 28 and processing units 16 ).

[0116] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. Examples of these architectures include the Industry Subversive Alliance (ISA) bus, the Micro Channel Architecture (MCA) bus, an enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0117] The computer device 12 includes a variety of computer system readable media. These media can be any available media that can be accessed by the computer device 12, including volatile and non-volatile media, removable and non-removable media.

[0118] System memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Computer device 12 may include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be configured to read and write to non-removable, non-volatile magnetic media (not shown in FIG. 6 , commonly referred to as a “hard drive”). Although not shown in FIG. 6 , a magnetic disk drive may be provided for reading and writing to a removable non-volatile magnetic disk (e.g., a “floppy disk”), as well as an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a compact disc read-only memory (CD-ROM), a digital versatile disc read-only memory (DVD-ROM), or other optical media). In these cases, each drive may be connected to bus 18 via one or more data media interfaces. The system memory 28 may include at least one program product having a set (eg, at least one) of program modules configured to perform the functions of various embodiments of the present application.

[0119] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28. Such program modules 42 may include an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may include an implementation of a network environment. Program modules 42 generally implement the functions and / or methods of the embodiments described herein.

[0120] The computer device 12 can also communicate with one or more external devices 14 (e.g., a keyboard, pointing device, display 24, etc.), one or more devices that enable a user to interact with the computer device 12, and / or any device that enables the computer device 12 to communicate with one or more other computing devices (e.g., a network card, a modem, etc.). This communication can occur via an input / output (I / O) interface 22. Furthermore, the computer device 12 can communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet) via a network adapter 20. As shown, the network adapter 20 communicates with other modules of the computer device 12 via a bus 18. Although not shown in FIG. 6 , other hardware and / or software modules can be used in conjunction with the computer device 12, including microcode, device drivers, redundant processing units, external disk drive arrays, Redundant Arrays of Independent Drives (RAID) systems, tape drives, and data backup storage systems.

[0121] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the brain quantitative parameter imaging method provided in the embodiment of the present application, which includes:

[0122] Based on the brain PET imaging results under the short-axis field of view, obtain the reference drug metabolism time activity curve of the preset brain reference site;

[0123] determining a target drug metabolism time-activity curve of the descending aorta based on a mapping relationship between the reference drug metabolism time-activity curve and the drug metabolism time-activity curve of the descending aorta;

[0124] Based on the target drug metabolism time-activity curve and the preset pharmacokinetic model, a target brain drug metabolism kinetic parameter image is completed.

[0125] The present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for quantitative brain parameter imaging provided in any embodiment of the present application is implemented. The method includes:

[0126] Based on the brain PET imaging results under the short-axis field of view, obtain the reference drug metabolism time activity curve of the preset brain reference site;

[0127] determining a target drug metabolism time-activity curve of the descending aorta based on a mapping relationship between the reference drug metabolism time-activity curve and the drug metabolism time-activity curve of the descending aorta;

[0128] Based on the target drug metabolism time-activity curve and the preset pharmacokinetic model, a target brain drug metabolism kinetic parameter image is completed.

[0129] The computer storage medium of the embodiment of the present application can adopt any combination of one or more computer-readable media. Computer-readable media can be computer-readable signal media or computer-readable storage media. Computer-readable storage media can be, for example: electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or devices, or any combination of the above. Computer-readable storage media include: electrical connections with one or more wires, portable computer disks, hard disks, RAM, read-only memories (ROM), erasable programmable read-only memories (EPROM), flash memories, optical fibers, portable CD-ROMs, optical storage devices, magnetic storage devices, or any suitable combination of the above. In this document, computer-readable storage media can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, device or device. The storage medium can be a non-transitory storage medium.

[0130] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such propagated data signals may take various forms, including electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0131] The program code contained on the computer-readable medium can be transmitted using any appropriate medium, including wireless, wire, optical cable, radio frequency (RF), etc., or any suitable combination of the above.

[0132] The computer program code for performing the operation of the present application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and also conventional procedural programming languages ​​such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a LAN or WAN, or can be connected to an external computer (e.g., using an Internet service provider to connect via the Internet).

[0133] The multiple modules or steps of the present application described above can be implemented using a general-purpose computing device. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Alternatively, they can be implemented using program code executable by a computer device, so that they can be stored in a storage device and executed by the computing device. Alternatively, they can be fabricated into multiple integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

Claims

1. A method for brain quantitative parameter imaging, comprising: Based on the positron emission tomography (PET) imaging results of the brain under a short-axis field of view, obtaining the reference drug metabolism time-activity curve of a preset brain reference region; Based on the mapping relationship between the reference drug metabolism time-activity curve and the drug metabolism time-activity curve of the descending aorta, determining the target drug metabolism time-activity curve of the descending aorta; Based on the target drug metabolism time-activity curve and a preset pharmacokinetic model, completing the target brain pharmacokinetic parameter image.

2. The method according to claim 1, wherein, The step of determining the target drug metabolism time-activity curve of the descending aorta based on the mapping relationship between the reference drug metabolism time-activity curve and the drug metabolism time-activity curve of the descending aorta includes: Dividing the reference drug metabolism time-activity curve into a first-stage reference curve and a second-stage reference curve according to the curve characteristics of the analysis curve corresponding to the preset pharmacokinetic model; Determining the first-stage target drug metabolism time-activity curve according to the first mapping relationship between the first-stage reference curve and the first-stage drug metabolism time-activity curve corresponding to the descending aorta; Determining the second-stage target drug metabolism time-activity curve according to the second mapping relationship between the second-stage reference curve and the second-stage drug metabolism time-activity curve corresponding to the descending aorta; Determining the target drug metabolism time-activity curve of the descending aorta according to the first-stage target drug metabolism time-activity curve and the second-stage target drug metabolism time-activity curve.

3. The method according to claim 2, wherein, The step of determining the first-stage target drug metabolism time-activity curve according to the first mapping relationship between the first-stage reference curve and the first-stage drug metabolism time-activity curve corresponding to the descending aorta includes: Inputting the first-stage reference curve into the linear function corresponding to the first mapping relationship to obtain the first-stage target drug metabolism time-activity curve.

4. The method according to claim 2, wherein, The step of determining the second-stage target drug metabolism time-activity curve according to the second mapping relationship between the second-stage reference curve and the second-stage drug metabolism time-activity curve corresponding to the descending aorta includes: Inputting the second-stage reference curve into the linear function corresponding to the second mapping relationship to obtain the second-stage target drug metabolism time-activity curve.

5. The method according to claim 2, wherein, The determination process of the first mapping relationship includes: Obtaining a set of drug metabolism time-activity curve pairs of a preset brain reference region and the descending aorta determined based on the synchronous PET imaging results of the brain, heart and lungs under a long-axis field of view; According to the curve characteristics, performing curve segmentation on each drug metabolism time-activity curve pair in the set to obtain the corresponding first-stage drug metabolism time-activity curve pairs of each drug metabolism time-activity curve pair; Based on each first-stage drug metabolism time-activity curve pair, performing linear fitting to obtain the first mapping relationship. ​ 6. The method according to claim 5, wherein, the process of determining the second mapping relationship includes: determining, according to each first-stage drug metabolism time-activity curve pair, a corresponding second-stage drug metabolism time-activity curve pair for each first-stage drug metabolism time-activity curve pair; assuming a difference relationship for the second-stage drug metabolism time-activity curve pairs, determining the mean value of the differences between multiple second-stage drug metabolism time-activity curve pairs, and taking the mean value as the second mapping relationship.

7. The method according to any one of claims 1 to 6, wherein, the preset brain reference site is a site in the brain where blood flow passes through, including the carotid artery or the cavernous sinus.

8. A brain quantitative parameter imaging device, comprising: a reference data acquisition module configured to acquire a reference drug metabolism time-activity curve of a preset brain reference site based on a positron emission tomography (PET) imaging result of the brain under a short-axis field of view; a target data acquisition module configured to determine a target drug metabolism time-activity curve of the descending aorta based on a mapping relationship between the reference drug metabolism time-activity curve and the drug metabolism time-activity curve of the descending aorta; a brain parameter imaging module configured to complete a target brain pharmacokinetic parameter image based on the target drug metabolism time-activity curve and a preset pharmacokinetic model.

9. A computer device, comprising: one or more processors; a memory configured to store one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the brain quantitative parameter imaging method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, wherein, when the computer program is executed by a processor, it implements the brain quantitative parameter imaging method according to any one of claims 1 to 7.

Images