Method, device and equipment for determining kinetic parameters based on PET (positron emission tomography) imaging and medium

Abstract

The invention discloses a method, device and equipment for determining kinetic parameters based on PET imaging and a medium, and the method comprises the steps: determining a tracer agent output function according to a blood input function and two atrioventricular dynamic models; according to the dynamic PET image set and the tracer output function, performing iterative solution on the two-atrioventricular dynamic model to obtain model dynamic parameters of the two-atrioventricular dynamic model; determining a dynamic parameter image according to the model dynamic parameters; wherein the dynamic PET image set represents a PET image set within a preset duration after the tracer is injected, the preset duration is greater than or equal to 30 minutes and less than 60 minutes, the two atrioventricular dynamic models correspond to a blood atrioventricle and a tissue atrioventricle, and the blood input function represents activity values of the tracer in the blood atrioventricle at different scanning moments; and the tracer output function represents the sum result of the activity value of the tracer in the blood chamber and the activity value of the tracer in the tissue chamber, so that the inspection flux and the use efficiency of the PET imaging equipment are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical image processing, and in particular to a method, device, equipment and medium for determining dynamic parameters based on PET imaging. Background Art

[0002] In recent years, dynamic positron emission tomography (PET) imaging has been widely used in clinical examination and disease diagnosis. Dynamic PET imaging refers to the continuous acquisition of PET image sets over a period of time to provide a data basis for determining dynamic parameters.

[0003] There are two main scanning protocols currently used. One is a late dynamic scan, such as 30-60 minutes after tracer injection, and the other is two short-term dynamic scans, such as 0-6 minutes and 60-75 minutes, or 0-10 minutes and 55-60 minutes after tracer injection.

[0004] Although the scanning time of the above two scanning protocols is shorter, the required imaging time is still relatively long, which makes the throughput of PET imaging equipment low and the utilization efficiency is not high. Summary of the invention

[0005] The embodiments of the present invention provide a method, device, equipment and medium for determining dynamic parameters based on PET imaging to solve the problem of long imaging time required for the scanning protocol of dynamic PET imaging, thereby improving the inspection throughput and utilization efficiency of PET imaging equipment.

[0006] According to one embodiment of the present invention, a method for determining dynamic parameters based on PET imaging is provided, the method comprising:

[0007] Acquire a dynamic PET image set and obtain a blood input function;

[0008] Determining a tracer output function according to the blood input function and the two-compartment dynamic model;

[0009] Iteratively solving the two-compartment dynamic model according to the dynamic PET image set and the tracer output function to obtain model dynamic parameters of the two-compartment dynamic model;

[0010] Determining a kinetic parameter image according to the model kinetic parameters;

[0011] Among them, the dynamic PET image set represents a PET image set within a preset time after tracer injection, the preset time is greater than or equal to 30 minutes and less than 60 minutes, the two-compartment dynamic model corresponds to the blood compartment and the tissue compartment, the blood input function represents the activity value of the tracer in the blood compartment at different scanning times, and the tracer output function represents the sum of the activity value of the tracer in the blood compartment and the activity value of the tracer in the tissue compartment.

[0012] According to another embodiment of the present invention, a device for determining dynamic parameters based on PET imaging is provided, the device comprising:

[0013] A blood input function acquisition module, used to acquire a dynamic PET image set and obtain a blood input function;

[0014] a tracer output function determination module, used to determine the tracer output function according to the blood input function and the two-chamber dynamic model;

[0015] A model dynamic parameter solving module, used for iteratively solving the two-compartment dynamic model according to the dynamic PET image set and the tracer output function to obtain the model dynamic parameters of the two-compartment dynamic model;

[0016] A kinetic parameter image determination module, used to determine a kinetic parameter image according to the model kinetic parameters;

[0017] Among them, the dynamic PET image set represents a PET image set within a preset time after tracer injection, the preset time is greater than or equal to 30 minutes and less than 60 minutes, the two-compartment dynamic model corresponds to the blood compartment and the tissue compartment, the blood input function represents the activity value of the tracer in the blood compartment at different scanning times, and the tracer output function represents the sum of the activity value of the tracer in the blood compartment and the activity value of the tracer in the tissue compartment.

[0018] According to another embodiment of the present invention, an electronic device is provided, the electronic device comprising:

[0019] at least one processor; and

[0020] a memory communicatively connected to the at least one processor; wherein,

[0021] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the method for determining dynamic parameters based on PET imaging described in any embodiment of the present invention.

[0022] According to another embodiment of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for determining dynamic parameters based on PET imaging described in any embodiment of the present invention when executed.

[0023] According to another embodiment of the present invention, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the method for determining a kinetic parameter based on PET imaging as described in any embodiment of the present invention is implemented.

[0024] The technical solution of the embodiment of the present invention determines the tracer output function according to the blood input function and the two-compartment dynamic model, iteratively solves the two-compartment dynamic model according to the dynamic PET image set and the tracer output function to obtain the model dynamic parameters of the two-compartment dynamic model, and determines the dynamic parameter image according to the model dynamic parameters, wherein the dynamic PET image set represents the PET image set within a preset time after the tracer injection, and the preset time is greater than or equal to 30 minutes and less than 60 minutes. While ensuring the accuracy of the dynamic parameter image, the imaging time of dynamic PET imaging is shortened, the problem of long imaging time required for the scanning protocol of dynamic PET imaging is solved, and the inspection throughput and utilization efficiency of PET imaging equipment are improved.

[0025] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0027] Figure 1 A flowchart of a method for determining dynamic parameters based on PET imaging provided by one embodiment of the present invention;

[0028] Figure 2 A model architecture diagram of a two-chamber dynamic model provided by one embodiment of the present invention;

[0029] Figure 3 A flowchart of another method for determining dynamic parameters based on PET imaging provided by one embodiment of the present invention;

[0030] Figure 4A K provided by an embodiment of the present invention i -30 minutes and K i -65 minutes corresponding ROC curve;

[0031] Figure 5 A schematic diagram of the structure of a device for determining dynamic parameters based on PET imaging provided by one embodiment of the present invention;

[0032] Figure 6 The present invention is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0033] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0034] It should be noted that the terms "including" and "having" and any variations thereof in the specification and claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or apparatus.

[0035] Figure 1 This is a flowchart of a method for determining kinetic parameters based on PET imaging provided by an embodiment of the present invention. This embodiment is applicable to the case of estimating kinetic parameters in dynamic PET imaging. The method can be executed by a device for determining kinetic parameters based on PET imaging. The device for determining kinetic parameters based on PET imaging can be implemented in the form of hardware and / or software. The device for determining kinetic parameters based on PET imaging can be configured in a terminal device. Figure 1 As shown, the method includes:

[0036] S110, obtaining a dynamic PET image set and a blood input function.

[0037] In this embodiment, the dynamic PET image set refers to a PET image set within a preset time period after the tracer is injected, and the preset time period is greater than or equal to 30 minutes and less than 60 minutes.

[0038] The tracer refers to a radioactive substance that has decay characteristics and emits specific rays that can be detected by a detector. Exemplary tracers include, but are not limited to, 18F-FDG (2-deoxy-2-[18F]fluoro-D-glucose), 11C-choline, 18F-FMISO (18F-FMISO) or 18F-FLT (3'-deoxy-3'-18F-fluorothymidine), etc. The tracer used is not limited here, and can be customized according to actual needs.

[0039] In an optional embodiment, obtaining a dynamic PET image set includes: obtaining a dynamic PET image set collected by a PET / CT imaging device (Positron Emission Tomography / Computed Tomography); or reading a pre-stored dynamic PET image set from a cloud server.

[0040] In an optional embodiment, the acquisition object corresponding to the dynamic PET image set is an object that carries the tissue to be tested that is completely located within the field of view of the scanner and has positive tracer uptake, and the expression level of PD-L1 (Programmed Death-Ligand 1) meets the expression level condition.

[0041] Exemplarily, the tissue to be tested may be non-small cell lung cancer (NSCLC), but is not limited to the exemplary case.

[0042] Exemplarily, the expression level of PD-L1 can be based on indicators such as the tumor cell positive proportion score (Tumor Proportion Score, TPS) and the combined positive score (Combined Positive Score, CPS). For example, when TPS is greater than 50%, it means that the expression level of PD-L1 is high expression, when TPS is between 1%-50%, it means that the expression level of PD-L1 is low expression, and when TPS is less than 1%, it means that the expression level of PD-L1 is negative expression. For example, when CPS is greater than 10, it means that the expression level of PD-L1 is high expression, when CPS is between 1-10, it means that the expression level of PD-L1 is low expression, and when CPS is less than 1, it means that the expression level of PD-L1 is negative expression.

[0043] Of course, TPS and CPS can also be normalized separately, and the expression level of PD-L1 can be jointly determined based on the normalization results.

[0044] In an optional embodiment, the expression level condition is that the expression level of PD-L1 is high expression.

[0045] There is no limitation on the method of determining the expression level of PD-L1, the method of classification and the basis of indicators, and the specific settings can be customized according to actual needs.

[0046] For the scanning protocol of dynamic PET imaging, the tracer is 18F-FDG and the preset duration is 30min. In some embodiments, the subject avoids strenuous exercise 24 hours before the PET / CT scan and fasts for at least 6 hours. When 18F-FDG is injected, the blood glucose level is less than 8.0mmol / L. First, the subject undergoes a breath-hold chest CT scan and a whole-body CT scan (from the head to the mid-femur, supine position, arm raised) to obtain CT scan data. The CT scan parameters are as follows: tube voltage 120kV, tube current 10-220mA, pitch 1.375:1, noise index 20. Subsequently, a dynamic PET scan of the chest area (axial field of view is 20cm) is performed immediately after the 18F-FDG injection (through an intravenous indwelling needle, the average injection dose ± standard deviation is 288.78±49.69MBq, and the injection dose range is 203.15-423.65MBq) to obtain dynamic PET data. The specific plan is: 6×10 seconds, 4×30 seconds, 4×60 seconds, 4×120 seconds, and 3×300 seconds for a total of 30 minutes.

[0047] Specifically, the CT scan data is used to perform attenuation correction on the dynamic PET data, and the attenuation-corrected dynamic PET data is reconstructed to obtain a dynamic PET image set. The reconstruction algorithm may adopt a Block Sequential Regularized Expectation Maximization (BSREM) reconstruction algorithm, and the reconstruction parameters include: 25 iterations and 2 subsets, and a matrix size of 256×256.

[0048] In this embodiment, the blood input function represents the activity value of the tracer in the blood at different scanning moments. In an optional embodiment, the blood input function is a discrete array or a continuous curve. The array includes the measured activity values ​​corresponding to each of the multiple scanning moments, and the curve includes the fitted activity values ​​corresponding to each of the multiple scanning moments.

[0049] In an optional embodiment, obtaining the blood input function includes: determining a blood tracer count based on at least two scanning moments corresponding to the dynamic PET image set, and determining the blood input function based on the blood tracer count corresponding to each scanning moment.

[0050] The blood tracer count represents the statistical number of tracers in the blood at the scanning time. In this embodiment, the blood tracer count is used to characterize the measured activity value of the tracer in the blood at the scanning time. Exemplarily, the blood tracer count can be obtained by measuring the blood sample of the collected object using a gamma counter.

[0051] S120. Determine the tracer output function according to the blood input function and the two-chamber dynamic model.

[0052] In this embodiment, the two-compartment dynamic model corresponds to the blood compartment and the tissue compartment. The two-compartment dynamic model is a mathematical model in which the tracer moves to different compartments in unit time based on the kinetic characteristics. The rate of change of the tracer activity in the blood compartment or the tissue compartment is called the rate constant, which can also be called the kinetic parameter.

[0053] Figure 2 This is a model architecture diagram of a two-chamber dynamic model provided by an embodiment of the present invention. Specifically, Figure 2 The "blood" in the figure represents the blood compartment, "C1" and "C2" together represent the tissue compartment, K1 is the forward transport rate, which represents the rate constant of the tracer moving from the blood compartment to the tissue compartment, k2 is the reverse transport rate, which represents the rate constant of the tracer moving from the tissue compartment to the blood compartment, k3 is the phosphorylation rate, which represents the rate constant of the free tracer in the tissue compartment being converted to the phosphorylated tracer, and can also be expressed as the rate constant of the tracer moving from the unphosphorylated compartment to the phosphorylated compartment, and k4 is the dephosphorylation rate, which represents the rate constant of the phosphorylated tracer in the tissue compartment being converted to the free tracer, and can also be expressed as the rate constant of the tracer moving from the phosphorylated compartment to the unphosphorylated compartment.

[0054] In this embodiment, the blood input function represents the activity value of the tracer in the blood compartment at different scanning moments, and the tracer output function represents the sum of the activity values ​​of the tracer in the blood compartment and the tracer in the tissue compartment at each scanning moment.

[0055] In an optional embodiment, the tracer output function is determined based on the blood input function and the two-chamber dynamic model, including: determining the tissue activity function based on the blood input function and the model dynamic parameters; and performing weighted summation of the blood input function and the tissue activity function to obtain the tracer output function.

[0056] In this embodiment, the tissue activity function represents the activity value of the tracer in the tissue chamber at different scanning times, and the weight corresponding to the blood input function is the blood volume ratio.

[0057] In an optional embodiment, the tissue activity function is determined according to the blood input function and the model dynamic parameters, including: determining the transient response function according to the model dynamic parameters; and taking the convolution result of the blood input function and the transient response function as the tissue activity function.

[0058] In this embodiment, the tracer is an irreversible tracer, and accordingly, the model kinetic parameters do not include a dephosphorylation rate, which represents the rate constant of conversion of the phosphorylated tracer into a free tracer in the tissue compartment.

[0059] Exemplarily, the tracer includes but is not limited to 18F-FDG or choline tracers. The tracer used is not limited here and can be customized according to actual needs.

[0060] In this embodiment, the model kinetic parameters include forward transport rate K1, reverse transport rate k2 and phosphorylation rate k3.

[0061] Exemplarily, the tissue activity function C i (t) is expressed by the following formula:

[0062]

[0063] Where t represents the scanning time, C p (t) represents the blood input function, and y(t) represents the transient response function.

[0064] Exemplarily, the tracer output function C T (t) is expressed by the following formula:

[0065] C T (t) = CBV × C p (t)+C i (t);

[0066] Among them, CBV represents the blood volume percentage.

[0067] S130, iteratively solving the two-compartment dynamic model according to the dynamic PET image set and the tracer output function to obtain the model dynamic parameters of the two-compartment dynamic model.

[0068] In an optional embodiment, the two-compartment dynamic model is iteratively solved according to the dynamic PET image set and the tracer output function to obtain the model dynamic parameters of the two-compartment dynamic model, including: determining the objective function according to the dynamic PET image set and the tracer output function; and using a least squares fitting algorithm to minimize the objective function to obtain the model dynamic parameters of the two-compartment dynamic model.

[0069] In this embodiment, the objective function represents the sum of square errors between the voxel values ​​of all pixels in the PET image corresponding to each scanning moment in the dynamic PET image set and the activity value of the tracer output function at the scanning moment.

[0070] Exemplarily, the objective function is expressed by the following formula:

[0071]

[0072] in, represents the parameter set for iterative solution, which includes the model dynamic parameters and blood volume ratio, C PET (t i ) represents the voxel value of the i-th pixel in the PET image corresponding to the scanning time t in the dynamic PET image set, and N represents the number of pixels in the PET image.

[0073] S140, determining a dynamic parameter image according to the model dynamic parameters.

[0074] In an optional embodiment, the kinetic parameter image includes at least one of a forward transport rate image, a reverse transport rate image, a phosphorylation rate image, and a net uptake rate image.

[0075] In a specific embodiment, when the kinetic parameter image includes a net uptake rate image, the net uptake rate is determined based on the forward transport rate, reverse transport rate and phosphorylation rate in the model kinetic parameters; and the net uptake rate image is determined based on the net uptake rate.

[0076] The net uptake rate K i It can be used to reflect the net amount of tracer entering the tissue per unit time. For example, the net uptake rate K i It is expressed by the following formula:

[0077]

[0078] The technical solution of this embodiment determines the tracer output function according to the blood input function and the two-compartment dynamic model, iteratively solves the two-compartment dynamic model according to the dynamic PET image set and the tracer output function to obtain the model dynamic parameters of the two-compartment dynamic model, and determines the dynamic parameter image according to the model dynamic parameters, wherein the dynamic PET image set represents the PET image set within a preset time after the tracer injection, and the preset time is greater than or equal to 30 minutes and less than 60 minutes. While ensuring the accuracy of the dynamic parameter image, the imaging time of dynamic PET imaging is shortened, the problem of long imaging time required for the scanning protocol of dynamic PET imaging is solved, and the inspection throughput and utilization efficiency of PET imaging equipment are improved.

[0079] Figure 3 A flowchart of another method for determining dynamic parameters based on PET imaging provided by an embodiment of the present invention is provided. This embodiment further refines the "obtaining a blood input function" in the above embodiment. In this embodiment, the obtaining of the blood input function includes: obtaining an early PET image set corresponding to the dynamic PET image set; wherein the early PET image set includes multiple frames of PET images before the scanning time after the tracer injection; determining the blood image area corresponding to the blood chamber according to the early PET image set; determining the average voxel value corresponding to each frame of the PET image according to the dynamic PET image set and the blood image area; and constructing a blood input function according to the scanning time and the average voxel value of each frame of the PET image.

[0080] like Figure 3 As shown, the method includes:

[0081] S210: Acquire a dynamic PET image set.

[0082] S210 in this embodiment is similar to that in the above embodiment. Figure 1 The S110 shown corresponds to the same or similar steps, and will not be described in detail in this embodiment.

[0083] S220 , obtaining an early PET image set corresponding to the dynamic PET image set.

[0084] In this embodiment, the early PET image set refers to a PET image set within an early time range after tracer injection, and the scanning time corresponding to each PET image frame in the early PET image set is earlier than the scanning time of other PET images except in the early PET image set.

[0085] Exemplarily, the early time range may be [0, 30s] or [0, 60s]. The early time range is not limited here and may be customized according to actual needs.

[0086] S230 , determining a blood image region corresponding to a blood chamber according to an early PET image set.

[0087] Specifically, the blood image region represents the arterial region. In an optional embodiment, a preset segmentation algorithm is used to determine the blood image region corresponding to the blood chamber based on the early PET image set. Exemplarily, the preset segmentation algorithm includes but is not limited to a threshold-based segmentation algorithm, a region-based segmentation algorithm, an edge-based segmentation algorithm, a cluster-based segmentation algorithm, a deep learning algorithm, and the like.

[0088] Among them, the threshold segmentation algorithm refers to using one or more pixel thresholds, and dividing the blood image area from the PET image according to the comparison result between the voxel value in the PET image and the pixel threshold. The region segmentation algorithm includes a region growing algorithm and a region splitting and merging algorithm, wherein the region growing algorithm refers to selecting a seed pixel point in the PET image, merging the pixel points into the region where the seed pixel point is located according to the similarity between the pixel points in the PET image and the seed pixel point in the neighborhood, until the blood image area is obtained when the stop growth condition is met, and the region splitting and merging algorithm refers to dividing the PET image into several small areas, judging whether the adjacent areas should be merged according to the similarity criterion, or splitting a region into smaller areas until the stop condition is met to obtain the blood image area. The edge segmentation algorithm refers to detecting the discontinuity of the voxel value in the PET image to determine the edge of the blood image area. Exemplarily, the neural network used by the deep learning algorithm includes but is not limited to a fully convolutional neural network or a U-net network.

[0089] There is no limitation on the preset segmentation algorithm used here, and the specific settings can be customized according to actual needs.

[0090] In another optional embodiment, when the blood image region is the ascending aorta region and the blood image region is manually outlined, the region size of the blood image region is 10 mm×10 mm×20 mm.

[0091] S240 , determining an average voxel value corresponding to each frame of the PET image in the dynamic PET image set according to the blood image region.

[0092] In an optional embodiment, the average voxel value corresponding to each frame of PET image in the dynamic PET image set is determined based on the blood image area, including: for each frame of PET image in the dynamic PET image set, a blood segmentation image corresponding to the blood image area in the PET image is obtained, and the average voxel value of the PET image is determined based on the blood segmentation image.

[0093] In an optional embodiment, the average voxel value of the PET image is determined based on the blood segmentation image, including: taking the average voxel value corresponding to all pixels in the blood segmentation image as the average voxel value of the PET image; or taking the voxel value of a specified pixel in the blood segmentation image as the average voxel value of the PET image.

[0094] S250: construct a blood input function according to the scanning time and average voxel value of each frame of PET image.

[0095] In an optional embodiment, a blood input function is constructed according to the scanning time and the average voxel value of each frame of PET image, including: sorting the average voxel value corresponding to each frame of PET image according to the scanning time of each frame of PET image to obtain the blood input function; or, using a preset fitting function, fitting according to the scanning time and the average voxel value of each frame of PET image to obtain the blood input function.

[0096] In this embodiment, the average voxel value is used to characterize the measured activity value of the tracer in the blood at the scanning time. Exemplarily, the preset fitting function may be an exponential function, and the preset fitting function used is not limited here, and can be customized according to actual needs.

[0097] S260. Determine the tracer output function according to the blood input function and the two-chamber dynamic model.

[0098] S270, iteratively solving the two-compartment dynamic model according to the dynamic PET image set and the tracer output function to obtain the model dynamic parameters of the two-compartment dynamic model.

[0099] S280: Determine a dynamic parameter image according to the model dynamic parameters.

[0100] S260-S280 in this embodiment are similar to those in the above embodiment. Figure 1 The S120 - S130 shown correspond to the same or similar ones, and are not described in detail in this embodiment.

[0101] On the basis of the above embodiment, optionally, after determining the kinetic parameter image according to the model dynamic parameters, the method also includes: when the kinetic parameter image includes a net uptake rate constant image, determining the tissue image region corresponding to the tissue chamber according to the dynamic PET image set; according to the tissue image region, cropping the net uptake rate constant image to obtain a tissue segmentation image; and determining, according to the tissue segmentation image, a classification label of the tissue to be tested corresponding to the tissue segmentation image.

[0102] In an optional embodiment, determining the tissue image region corresponding to the tissue chamber based on the dynamic PET image set includes: acquiring a late PET image set corresponding to the dynamic PET image set; and determining the tissue image region corresponding to the tissue chamber based on the late PET image set.

[0103] In this embodiment, the late PET image set refers to a PET image set within a late time range after tracer injection, and the scanning time corresponding to each PET image frame in the late PET image set is later than the scanning time of other PET images except in the late PET image set.

[0104] For example, when the preset duration is 30 minutes, the late time range may be [20 minutes, 30 minutes] or [25 minutes, 30 minutes]. The late time range is not limited here and can be customized according to actual needs.

[0105] Exemplarily, the method for determining the tissue image area is similar to the method for determining the blood image area described above. For example, assuming that the preset segmentation algorithm is a threshold-based segmentation algorithm, the pixel threshold can be the product of the maximum SUV (Standardized Uptake Value) corresponding to the late PET image set and the preset ratio, and the preset ratio can be 40%. Among them, SUV represents the degree of uptake of the tracer by the tissue, SUV = voxel value / injection dose / weight of the collected object.

[0106] In an optional embodiment, based on the tissue segmentation image, the classification label of the tissue to be tested corresponding to the tissue segmentation image is determined, including: taking the average voxel value corresponding to all pixels in the tissue segmentation image as the quantitative parameter value; if the quantitative parameter value is greater than the quantitative threshold, the classification label of the tissue to be tested corresponding to the tissue segmentation image is set to the first label; if the quantitative parameter value is less than or equal to the quantitative threshold, the classification label of the tissue to be tested corresponding to the tissue segmentation image is set to the second label.

[0107] The technical solution of this embodiment obtains an early PET image set corresponding to the dynamic PET image set, determines the blood image area corresponding to the blood chamber based on the early PET image set, determines the average voxel value corresponding to each frame of PET image in the dynamic PET image set based on the blood image area, and constructs a blood input function based on the scanning time and average voxel value of each frame of PET image, thereby solving the problem of invasiveness in constructing the blood input function and achieving the purpose of non-invasively determining the dynamic parameters.

[0108] In some instances, K i -30 min represents the net uptake rate constant image, K, determined from a 30-min dynamic PET image set i -65 minutes represents the net uptake rate constant image determined based on the 65-minute dynamic PET image set. For example, based on the 30-minute dynamic PET image set, 7 more PET images are collected at 300-second intervals to form a 65-minute dynamic PET image set. i -30 minutes and K i The determination process of the net uptake rate constant images corresponding to the respective periods from 40 to 65 minutes is similar or the same.

[0109] K i -30 minutes and K i-65 minutes to evaluate image quality dimensions, such as image quality evaluation indicators including but not limited to artifact reduction, noise suppression, contrast preservation, tissue segmentation and overall quality, etc. i -30 minutes and K i The image quality corresponding to the 30- to 65-minute images meets clinical requirements. Since tissue segmentation depends on the late PET image set of the dynamic PET image set, in this example, the number of tissue image regions obtained by segmenting the 30-minute dynamic PET image set is the same as the number of tissue image regions obtained by segmenting the 65-minute dynamic PET image set. i -30 minutes and K i The image quality of the two groups was tested for differences between the two groups, and no significant difference was found. i -30 minutes and K i A correlation analysis was performed on the image qualities corresponding to the respective images at -65 minutes, which showed that there was a correlation. For example, the correlation coefficients used in the correlation analysis included the Pearson correlation coefficient and / or the Spearman rank correlation coefficient.

[0110] Figure 4 A K provided by an embodiment of the present invention i -30 minutes and K i -65 minutes, the corresponding ROC curve. Specifically, the ROC curve (Receiver Operating Characteristic curve) represents a curve drawn with the false positive rate (FPR) as the horizontal axis and the true positive rate (TPR) as the vertical axis for different pixel thresholds. Figure 4 In the figure, “sensitivity” refers to sensitivity, i.e., true positive rate, and “specificity” refers to specificity, i.e., false positive rate. True positive rate = 100-specificity.

[0111] from Figure 4 Can get, K i The optimal pixel threshold corresponding to -30 minutes was 0.018 ml / g / min, the AUC (Area Under Curve) was 0.816 (95% confidence interval: 0.743–0.875), the sensitivity was 69.50%, and the specificity was 83.30%. K i The optimal pixel threshold corresponding to -65 minutes was 0.022 ml / g / min, with an AUC of 0.816 (95% confidence interval: 0.744–0.876), a sensitivity of 66.40%, and a specificity of 83.30%.

[0112] And, for K i -30 minutes and K i -65 minutes to conduct Delong test, the test results show K i -30 minutes and K i There was no significant difference in classification accuracy between the two groups at -65 minutes.

[0113] The following is an embodiment of a device for determining kinetic parameters based on PET imaging provided in an embodiment of the present invention. The device and the method for determining kinetic parameters based on PET imaging in the above-mentioned embodiment belong to the same inventive concept. For details not fully described in the embodiment of the device for determining kinetic parameters based on PET imaging, reference may be made to the contents of the method for determining kinetic parameters based on PET imaging in the above-mentioned embodiment.

[0114] Figure 5 The schematic diagram of the structure of a device for determining dynamic parameters based on PET imaging provided by one embodiment of the present invention. Figure 5 As shown, the device includes: a blood input function acquisition module 310, a tracer output function determination module 320, a model dynamic parameter solution module 330 and a dynamic parameter image determination module 340.

[0115] The blood input function acquisition module 310 is used to acquire a dynamic PET image set and acquire a blood input function;

[0116] The tracer output function determination module 320 is used to determine the tracer output function according to the blood input function and the two-chamber dynamic model;

[0117] A model dynamic parameter solving module 330 is used to iteratively solve the two-chamber dynamic model according to the dynamic PET image set and the tracer output function to obtain the model dynamic parameters of the two-chamber dynamic model;

[0118] A kinetic parameter image determination module 340 is used to determine a kinetic parameter image according to the model kinetic parameters;

[0119] Among them, the dynamic PET image set represents the PET image set within a preset time after the tracer injection, the preset time is greater than or equal to 30 minutes and less than 60 minutes, the two-compartment dynamic model corresponds to the blood compartment and the tissue compartment, the blood input function represents the activity value of the tracer in the blood compartment at different scanning times, and the tracer output function represents the sum of the activity value of the tracer in the blood compartment and the activity value of the tracer in the tissue compartment.

[0120] The technical solution of this embodiment determines the tracer output function according to the blood input function and the two-compartment dynamic model, iteratively solves the two-compartment dynamic model according to the dynamic PET image set and the tracer output function to obtain the model dynamic parameters of the two-compartment dynamic model, and determines the dynamic parameter image according to the model dynamic parameters, wherein the dynamic PET image set represents the PET image set within a preset time after the tracer injection, and the preset time is greater than or equal to 30 minutes and less than 60 minutes. While ensuring the accuracy of the dynamic parameter image, the imaging time of dynamic PET imaging is shortened, the problem of long imaging time required for the scanning protocol of dynamic PET imaging is solved, and the inspection throughput and utilization efficiency of PET imaging equipment are improved.

[0121] In an optional embodiment, the tracer output function determination module 320 includes:

[0122] A tissue activity function determination unit, used to determine the tissue activity function according to the blood input function and the model dynamic parameters;

[0123] a tracer output function determination unit, used for performing weighted summation of the blood input function and the tissue activity function to obtain the tracer output function;

[0124] Among them, the tissue activity function represents the activity value of the tracer in the tissue compartment at different scanning times, and the weight corresponding to the blood input function is the blood volume ratio.

[0125] In an optional embodiment, the tissue activity function determination unit is specifically configured to:

[0126] Determine the transient response function based on the model dynamic parameters;

[0127] The convolution result of the blood input function and the transient response function is used as the tissue activity function;

[0128] The tracer is an irreversible tracer, and accordingly, the model kinetic parameters do not include a dephosphorylation rate, which represents the rate constant of conversion of the phosphorylated tracer into a free tracer in the tissue compartment.

[0129] In an optional embodiment, the model dynamic parameter solving module 330 is specifically used to:

[0130] Determine the objective function based on the dynamic PET image set and the tracer output function;

[0131] The least squares fitting algorithm is used to minimize the objective function and obtain the model dynamic parameters of the two-compartment dynamic model.

[0132] The objective function represents the sum of square errors between the voxel values ​​of all pixels in the PET image corresponding to each scanning moment in the dynamic PET image set and the activity value of the tracer output function at the scanning moment.

[0133] In an optional embodiment, the blood input function acquisition module 310 is specifically used to:

[0134] Acquire an early PET image set corresponding to the dynamic PET image set; wherein the scanning time corresponding to each PET image frame in the early PET image set is earlier than the scanning time of other PET images except those in the early PET image set;

[0135] Based on the early PET image set, a blood image region corresponding to a blood compartment is determined;

[0136] Determine, according to the blood image region, an average voxel value corresponding to each frame of the PET image in the dynamic PET image set;

[0137] A blood input function is constructed according to the scanning time and average voxel value of each frame of PET image.

[0138] In an optional embodiment, the device further comprises:

[0139] a classification label determination module, for determining, after determining the kinetic parameter image according to the model kinetic parameter, a tissue image region corresponding to the tissue compartment according to the dynamic PET image set when the kinetic parameter image includes a net uptake rate constant image;

[0140] According to the tissue image area, the net uptake rate constant image is cropped to obtain a tissue segmentation image;

[0141] According to the tissue segmentation image, a classification label of the tissue to be tested corresponding to the tissue segmentation image is determined.

[0142] The device for determining kinetic parameters based on PET imaging provided in the embodiment of the present invention can execute the method for determining kinetic parameters based on PET imaging provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0143] Figure 6A schematic diagram of the structure of an electronic device provided for one 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, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0144] like Figure 6 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor 11, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the ROM 12 or the computer program loaded from the storage unit 18 to the RAM 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0145] 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, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information or data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0146] The processor 11 may be a variety of general and / or special 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), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the method for determining the kinetic parameters based on PET imaging provided in the above embodiments.

[0147] In some embodiments, the method for determining the kinetic parameters based on PET imaging provided in the above embodiments may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, 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. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps in the method for determining the kinetic parameters based on PET imaging described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the method for determining the kinetic parameters based on PET imaging in any other appropriate manner (e.g., by means of firmware).

[0148] Various embodiments of the systems and techniques described above herein may be implemented in the following systems or combinations thereof: digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard parts (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs that can 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 that can 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.

[0149] The computer program for implementing the method for determining kinetic parameters based on PET imaging of the present invention can be written in any combination of one or more programming languages. These computer programs can 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 can be executed completely on the machine, partially on the machine, partially on the machine as an independent software package and partially on a remote machine, or completely on a remote machine or server.

[0150] In the context of the present application, 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, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer readable storage medium may be a machine readable storage medium. Examples of machine readable storage media may include electrical connections based on at least one line, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM), flash memories, optical fibers, portable compact disc read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0151] To provide interaction with a user, the systems and techniques described herein may be implemented on a terminal device having: a display device (e.g., a cathode ray tube (CRT) or a liquid crystal display (LCD) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball), through which the user can provide input to the terminal device. Other types of devices may also provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0152] 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.

[0153] 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 relationship between the client and the server 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 a 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 virtual private servers (VPS) services.

[0154] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0155] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for determining dynamic parameters based on PET imaging, characterized in that: include: Acquire a dynamic PET image set and obtain a blood input function; Determining a tracer output function according to the blood input function and the two-compartment dynamic model; Iteratively solving the two-compartment dynamic model according to the dynamic PET image set and the tracer output function to obtain model dynamic parameters of the two-compartment dynamic model; Determining a kinetic parameter image according to the model kinetic parameters; Among them, the dynamic PET image set represents a PET image set within a preset time after tracer injection, the preset time is greater than or equal to 30 minutes and less than 60 minutes, the two-compartment dynamic model corresponds to the blood compartment and the tissue compartment, the blood input function represents the activity value of the tracer in the blood compartment at different scanning times, and the tracer output function represents the sum of the activity value of the tracer in the blood compartment and the activity value of the tracer in the tissue compartment.

2. The method according to claim 1, characterized in that: Determining the tracer output function according to the blood input function and the two-compartment dynamic model comprises: determining a tissue activity function according to the blood input function and the model dynamic parameters; Performing weighted summation on the blood input function and the tissue activity function to obtain a tracer output function; The tissue activity function represents the activity value of the tracer in the tissue chamber at different scanning moments, and the weight corresponding to the blood input function is the blood volume ratio.

3. The method according to claim 2, characterized in that Determining the tissue activity function according to the blood input function and the model dynamic parameter comprises: Determining a transient response function according to the model dynamic parameters; taking the convolution result of the blood input function and the transient response function as the tissue activity function; Wherein, the tracer is an irreversible tracer, and accordingly, the model kinetic parameters do not include a dephosphorylation rate, wherein the dephosphorylation rate represents a rate constant of conversion of the phosphorylated tracer in the tissue compartment into a free tracer.

4. The method according to claim 1, characterized in that: The iteratively solving the two-compartment dynamic model according to the dynamic PET image set and the tracer output function to obtain the model dynamic parameters of the two-compartment dynamic model includes: determining a target function based on the dynamic PET image set and the tracer output function; Using the least squares fitting algorithm, the objective function is minimized to obtain the model dynamic parameters of the two-compartment dynamic model; The objective function represents the sum of square errors between the voxel values ​​of all pixels in the PET image corresponding to each scanning moment in the dynamic PET image set and the activity value of the tracer output function at the scanning moment.

5. The method according to claim 1, characterized in that The obtaining of the blood input function comprises: Acquire an early PET image set corresponding to the dynamic PET image set; wherein the scanning time corresponding to each PET image frame in the early PET image set is earlier than the scanning time of other PET images except in the early PET image set; determining, based on the early PET image set, a blood image region corresponding to the blood chamber; Determining, according to the blood image region, an average voxel value corresponding to each frame of the PET image in the dynamic PET image set; A blood input function is constructed according to the scanning time and average voxel value of each frame of PET image.

6. The method according to claim 1, characterized in that After determining the dynamic parameter image according to the model dynamic parameter, the method further comprises: When the kinetic parameter image includes a net uptake rate constant image, determining a tissue image region corresponding to the tissue compartment based on the dynamic PET image set; According to the tissue image area, the net uptake rate constant image is cropped to obtain a tissue segmentation image; According to the tissue segmentation image, a classification label of the tissue to be tested corresponding to the tissue segmentation image is determined.

7. A device for determining dynamic parameters based on PET imaging, characterized in that: include: A blood input function acquisition module, used to acquire a dynamic PET image set and obtain a blood input function; a tracer output function determination module, used to determine the tracer output function according to the blood input function and the two-chamber dynamic model; A model dynamic parameter solving module, used for iteratively solving the two-compartment dynamic model according to the dynamic PET image set and the tracer output function to obtain the model dynamic parameters of the two-compartment dynamic model; A kinetic parameter image determination module, used to determine a kinetic parameter image according to the model kinetic parameters; Among them, the dynamic PET image set represents a PET image set within a preset time after tracer injection, the preset time is greater than or equal to 30 minutes and less than 60 minutes, the two-compartment dynamic model corresponds to the blood compartment and the tissue compartment, the blood input function represents the activity value of the tracer in the blood compartment at different scanning times, and the tracer output function represents the sum of the activity value of the tracer in the blood compartment and the activity value of the tracer in the tissue compartment.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the method for determining a kinetic parameter based on PET imaging according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for determining a kinetic parameter based on PET imaging according to any one of claims 1 to 6 when executed.

10. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the method for determining a kinetic parameter based on PET imaging according to any one of claims 1 to 6.

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