Medical image imaging method, device and computer equipment

Abstract

The application relates to a medical image imaging method, device, equipment, readable storage medium and program product, relates to the technical field of medical images, and can balance the imaging quality and imaging efficiency of nuclear medical images. The method comprises the following steps: acquiring first double coincidence data and second double coincidence data of a scanning object; simultaneously iteratively obtaining a first tracer activity distribution and a second tracer activity distribution according to the first double coincidence data and the second double coincidence data; acquiring an evaluation parameter reflecting the accuracy of each iteration according to the first tracer activity distribution and the second tracer activity distribution obtained in each iteration; and in the case that an iteration stopping condition is met, determining a first tracer target activity distribution and a second tracer target activity distribution according to the evaluation parameters corresponding to the respective iterations.

Description

Technical Field

[0001] This application relates to the field of medical imaging technology, and in particular to a medical image imaging method, apparatus and computer equipment. Background Technology

[0002] With the development of medical technology, positron emission tomography (PET) imaging, as an advanced nuclear medicine examination technique, uses radioactive tracers to label biomolecules, detect metabolic activities in the body, and generate detailed three-dimensional images, providing important evidence for disease diagnosis.

[0003] In related technologies, a single PET scan can only perform functional imaging of one tracer, limiting its ability to characterize complex biological activities. While multiple PET scans can provide more comprehensive information on biological activities, they are costly in terms of both economy and time, and registration issues between multiple scans can also affect the final image quality. Therefore, related technologies suffer from a trade-off between imaging quality and imaging efficiency in nuclear medicine. Summary of the Invention

[0004] Therefore, it is necessary to provide a medical image imaging method, device, computer equipment, computer-readable storage medium, and computer program product to address the aforementioned technical problems.

[0005] In a first aspect, this application provides a medical image imaging method, comprising:

[0006] Acquire first double coincidence data and second double coincidence data of the scanned object; the first double coincidence data is determined based on double coincidence events generated by nuclides in the first tracer and double coincidence events generated by nuclides in the second tracer in the scanned object, and the second double coincidence data is determined based on double coincidence events among the triple coincidence events generated by nuclides in the second tracer;

[0007] Based on the first double coincidence data and the second double coincidence data, the first tracer activity distribution and the second tracer activity distribution are obtained simultaneously through iteration.

[0008] Based on the first tracer activity distribution and the second tracer activity distribution obtained in each iteration, evaluation parameters reflecting the accuracy of each iteration are obtained;

[0009] If the iteration stopping condition is met, the target activity distribution of the first tracer and the target activity distribution of the second tracer are determined based on the evaluation parameters corresponding to each of the multiple iterations.

[0010] In one embodiment, obtaining evaluation parameters reflecting the accuracy of each iteration based on the first tracer activity distribution and the second tracer activity distribution obtained in each iteration includes:

[0011] Based on the first tracer activity distribution and the second tracer activity distribution obtained through iteration, the first double coincidence data reconstruction result is determined, and based on the second tracer activity distribution obtained through iteration, the second double coincidence data reconstruction result is determined.

[0012] Based on the first difference between the reconstruction result of the first double-matching data and the first double-matching data, and the second difference between the reconstruction result of the second double-matching data and the second double-matching data, the fidelity item of the current iteration is determined;

[0013] Based on the first and second tracer activity distributions obtained through iteration, determine the corresponding regularization term and the positive constraint term used to constrain the range of values ​​for the tracer activity distribution;

[0014] Based on the summation of the fidelity term, the regularization term, and the positive constraint term corresponding to each iteration, an evaluation parameter reflecting the accuracy of each iteration is determined.

[0015] In one embodiment, determining the first double-matching data reconstruction result based on the iteratively obtained first and second tracer activity distributions, and determining the second double-matching data reconstruction result based on the iteratively obtained second tracer activity distribution, includes:

[0016] Based on the iteratively obtained first tracer activity distribution, the first preset influence factor of the first tracer activity distribution on the first double-matching data, and the iteratively obtained second tracer activity distribution and the second preset influence factor of the second tracer activity distribution on the first double-matching data, the reconstruction result of the first double-matching data is determined; and,

[0017] Based on the third preset influence factor of the second tracer activity distribution and the second tracer activity distribution obtained through iteration on the second double coincidence data, the reconstruction result of the second double coincidence data is determined.

[0018] In one embodiment, determining the reconstruction result of the first double-matching data based on the iteratively obtained first tracer activity distribution, a first preset influence factor of the first tracer activity distribution on the first double-matching data, and a second preset influence factor of the iteratively obtained second tracer activity distribution and the second tracer activity distribution on the first double-matching data includes:

[0019] Based on the product of the first tracer activity distribution obtained through iteration and the first preset influence factor of the first tracer activity distribution on the first double coincidence data, the double coincidence data reconstruction result of the double coincidence event generated by the nuclide in the first tracer is determined;

[0020] Based on the product of the second tracer activity distribution obtained through iteration and the second preset influence factor of the second tracer activity distribution on the first double coincidence data, the double coincidence data reconstruction result of the double coincidence event generated by the nuclide in the second tracer is determined;

[0021] The first double coincidence data reconstruction result is determined based on the double coincidence data reconstruction results of the double coincidence events generated by the nuclides in the first tracer and the double coincidence data reconstruction results of the double coincidence events generated by the nuclides in the second tracer.

[0022] In one embodiment, determining the first double coincidence data reconstruction result based on the double coincidence data reconstruction result of the double coincidence events generated by the nuclide in the first tracer and the double coincidence data reconstruction result of the double coincidence events generated by the nuclide in the second tracer includes:

[0023] Obtain the first scattering estimation information corresponding to the first double coincidence data;

[0024] The first double coincidence data reconstruction result is obtained based on the double coincidence data reconstruction results of the double coincidence events generated by the nuclides in the first tracer, the double coincidence data reconstruction results of the double coincidence events generated by the nuclides in the second tracer, and the summation result of the first scattering estimation information.

[0025] In one embodiment, determining the reconstruction result of the second double-matching data based on a third preset influence factor of the second tracer activity distribution and the second tracer activity distribution obtained through iteration on the second double-matching data includes:

[0026] Based on the product of the second tracer activity distribution obtained through iteration and the third preset influence factor of the second tracer activity distribution on the second double coincidence data, the reconstruction result of the double coincidence data corresponding to the double coincidence event in the second tracer triple coincidence event is determined;

[0027] The second double-match data reconstruction result is determined based on the double-match data reconstruction result corresponding to the double-match event in the second tracer triple-match event.

[0028] In one embodiment, determining the second double-matching data reconstruction result based on the double-matching data reconstruction result corresponding to the double-matching event in the second tracer triple-matching event includes:

[0029] Obtain the second scattering estimation information corresponding to the second double coincidence data;

[0030] The second double coincidence data reconstruction result is obtained based on the double coincidence data reconstruction result corresponding to the double coincidence event in the second tracer triple coincidence event and the summation result of the second scattering estimation information.

[0031] In one embodiment, acquiring the first and second double-matching data of the scanned object includes:

[0032] Acquire double coincidence event data corresponding to multiple double coincidence events of a scanned object injected with a first tracer and a second tracer; the nuclide of the first tracer generates double coincidence events; the nuclide of the second tracer generates double coincidence events and triple coincidence events, and each double coincidence event data includes two photon detection positions and the detection time difference between the two photon detection positions;

[0033] Based on each of the double-matching event data, a triple-matching event is determined from the plurality of double-matching events, and the double-matching event data corresponding to the double-matching data other than the triple-matching event is determined as the first double-matching data;

[0034] Based on the double coincidence event data corresponding to the triple coincidence event, determine the gamma transient photon among the three photons corresponding to the triple coincidence event, and obtain the second double coincidence data based on the double coincidence event data corresponding to the two photons other than the gamma transient photon.

[0035] Secondly, this application also provides a medical image imaging device, comprising:

[0036] The dual-match data acquisition module is used to acquire first dual-match data and second dual-match data of the scanned object; the first dual-match data is determined based on dual-match events generated by nuclides in the first tracer and dual-match events generated by nuclides in the second tracer in the scanned object, and the second dual-match data is determined based on dual-match events among the three-match events generated by nuclides in the second tracer;

[0037] The activity distribution iteration module is used to simultaneously iterate and obtain the first tracer activity distribution and the second tracer activity distribution based on the first double coincidence data and the second double coincidence data.

[0038] The evaluation module is used to obtain evaluation parameters reflecting the accuracy of each iteration based on the first tracer activity distribution and the second tracer activity distribution obtained in each iteration.

[0039] The iteration result determination module is used to determine the first tracer target activity distribution and the second tracer target activity distribution based on the evaluation parameters corresponding to each of the multiple iterations, provided that the iteration stopping condition is met.

[0040] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0041] Acquire first double coincidence data and second double coincidence data of the scanned object; the first double coincidence data is determined based on double coincidence events generated by nuclides in the first tracer and double coincidence events generated by nuclides in the second tracer in the scanned object, and the second double coincidence data is determined based on double coincidence events among the triple coincidence events generated by nuclides in the second tracer;

[0042] Based on the first double coincidence data and the second double coincidence data, the first tracer activity distribution and the second tracer activity distribution are obtained simultaneously through iteration.

[0043] Based on the first tracer activity distribution and the second tracer activity distribution obtained in each iteration, evaluation parameters reflecting the accuracy of each iteration are obtained;

[0044] If the iteration stopping condition is met, the target activity distribution of the first tracer and the target activity distribution of the second tracer are determined based on the evaluation parameters corresponding to each of the multiple iterations.

[0045] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0046] Acquire first double coincidence data and second double coincidence data of the scanned object; the first double coincidence data is determined based on double coincidence events generated by nuclides in the first tracer and double coincidence events generated by nuclides in the second tracer in the scanned object, and the second double coincidence data is determined based on double coincidence events among the triple coincidence events generated by nuclides in the second tracer;

[0047] Based on the first double coincidence data and the second double coincidence data, the first tracer activity distribution and the second tracer activity distribution are obtained simultaneously through iteration.

[0048] Based on the first tracer activity distribution and the second tracer activity distribution obtained in each iteration, evaluation parameters reflecting the accuracy of each iteration are obtained;

[0049] If the iteration stopping condition is met, the target activity distribution of the first tracer and the target activity distribution of the second tracer are determined based on the evaluation parameters corresponding to each of the multiple iterations.

[0050] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0051] Acquire first double coincidence data and second double coincidence data of the scanned object; the first double coincidence data is determined based on double coincidence events generated by nuclides in the first tracer and double coincidence events generated by nuclides in the second tracer in the scanned object, and the second double coincidence data is determined based on double coincidence events among the triple coincidence events generated by nuclides in the second tracer;

[0052] Based on the first double coincidence data and the second double coincidence data, the first tracer activity distribution and the second tracer activity distribution are obtained simultaneously through iteration.

[0053] Based on the first tracer activity distribution and the second tracer activity distribution obtained in each iteration, evaluation parameters reflecting the accuracy of each iteration are obtained;

[0054] If the iteration stopping condition is met, the target activity distribution of the first tracer and the target activity distribution of the second tracer are determined based on the evaluation parameters corresponding to each of the multiple iterations.

[0055] The aforementioned medical image imaging method, apparatus, computer equipment, computer-readable storage medium, and computer program product can acquire first and second double-matching data of the scanned object. The first double-matching data is determined based on double-matching events generated by nuclides in the first tracer and the second tracer in the scanned object, while the second double-matching data is determined based on double-matching events among the three-matching events generated by nuclides in the second tracer. Then, based on the first and second double-matching data, the activity distributions of the first and second tracer can be iteratively obtained simultaneously. Evaluation parameters reflecting the accuracy of each iteration are obtained based on the activity distributions of the first and second tracer obtained in each iteration. Furthermore, when the iteration stopping condition is met, the target activity distributions of the first and second tracers are determined based on the evaluation parameters corresponding to each of the multiple iterations. In this embodiment, by injecting dual tracers into the scanned object and simultaneously modeling it, the activity distribution of the dual tracers within the scanned object is determined. This reduces the number of scans while providing more vital activity information about the scanned object, effectively balancing the imaging quality and efficiency of nuclear medicine images. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 This is a flowchart illustrating a medical image imaging method in one embodiment;

[0058] Figure 2 This is a flowchart illustrating one step in obtaining an iteration result in one embodiment;

[0059] Figure 3 This is a flowchart illustrating another medical image imaging method in one embodiment;

[0060] Figure 4 This is a structural block diagram of a medical image imaging device in one embodiment;

[0061] Figure 5 This is an internal structural diagram of a computer device in one embodiment;

[0062] Figure 6 This is an internal structural diagram of another computer device in one embodiment. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0064] In one embodiment, such as Figure 1 As shown, a medical image imaging method is provided. This embodiment illustrates the method applied to a terminal. It is understood that this method can also be applied to a server, and further to a system including both a terminal and a server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0065] S101, acquire the first double coincidence data and the second double coincidence data of the scanned object; the first double coincidence data is determined based on the double coincidence events generated by the nuclide in the first tracer and the double coincidence events generated by the nuclide in the second tracer in the scanned object, and the second double coincidence data is determined based on the double coincidence events among the three coincidence events generated by the nuclide in the second tracer.

[0066] As mentioned earlier, in related technologies, a single PET scan can only perform functional imaging of one tracer, limiting its ability to characterize complex life activities. While multiple PET scans can provide more comprehensive information on life activities, they are costly in terms of both economy and time, and registration issues between multiple scans can also affect the final image quality. Based on this, the inventors discovered in practice that simultaneous imaging with multiple PET tracers in a single scan has significant implications for clinical applications.

[0067] In this embodiment, two different tracers can be injected into the scanned object. For ease of distinction, the two tracers are referred to as the first tracer and the second tracer, respectively. The scanned object can be a living organism with vital functions. In some optional embodiments, the first tracer and the second tracer can be injected into the scanned object simultaneously or sequentially. If sequential injection is used, it can be based on a predetermined injection interval (i.e., the time interval between injecting the first tracer and the second tracer) and / or injection order. For example, the injection interval and injection order can be determined according to clinical guidelines. The injection interval is less than a preset threshold, which can be determined based on the accuracy of the target activity distributions of the first tracer and the second tracer obtained under different injection intervals.

[0068] Because some tracers contain nuclides that undergo [the following process] During decay, prompt-gamma rays are emitted probabilistically, resulting in a triple coincidence event after positron annihilation. This allows for the separation of dual-nucleus signals during simultaneous imaging with dual tracers in PET. Therefore, the first tracer used in this application can be a tracer that only produces pure double coincidence events, such as... The tracer, the second tracer can be a tracer capable of simultaneously generating both double coincidence and triple coincidence events, such as... Tracer.

[0069] Furthermore, by collecting and extracting data from the double coincidence events generated by the first and second tracers, the first double coincidence data can be obtained. By collecting and extracting data from the triple coincidence events generated by the second tracer, the double coincidence events contained in the triple coincidence events can be determined, and the corresponding second double coincidence data can be obtained.

[0070] In this system, both the first and second double coincidence data originate from a pair of gamma photons generated after electron-electron pair annihilation. Two opposing detectors in the medical scanning system can detect these paired gamma photons, and the output electrical pulses, after energy discrimination, are sent to the coincidence circuit if they meet the requirements. The connection between the two detectors is a coincidence line, which contains positional information. Accordingly, the first double coincidence data characterizes the coincidence event detected by the detector after the decay of a nuclide in the first tracer of the scanned object; the second double coincidence data characterizes the coincidence event detected by the detector after the decay of a nuclide in the second tracer of the scanned object.

[0071] In some embodiments, the first and second double-matching data of the scanned object can be acquired simultaneously in a single scan. In other words, the first and second double-matching data can be obtained simultaneously based on the scan data obtained in a single scan. By acquiring two sets of double-matching data in this way, the matching degree between the first and second double-matching data can be improved, thereby enhancing the accuracy of the two tracer activity distributions obtained by iterating simultaneously based on the first and second double-matching data.

[0072] S102, based on the first double coincidence data and the second double coincidence data, simultaneously iterate to obtain the first tracer activity distribution and the second tracer activity distribution.

[0073] In this step, the activity distributions of the first tracer and the second tracer in the scanned object can be determined based on the first and second double-matching data. Specifically, the activity distributions of the first and second tracers injected into the same scanned object may be coupled, meaning the activity distribution of one tracer can influence the activity distribution of the other. To address this, this embodiment can simultaneously iterate through the first and second double-matching data to determine the activity distributions of both tracers. In other words, in each iteration, the potential mutual influence between the first and second tracers, as well as the data relationship between their activity distributions, can be considered, and then the activity distributions of both tracers can be solved simultaneously in each iteration.

[0074] In some exemplary embodiments, the first double coincidence data and the second double coincidence data can be input into a pre-built dual tracer simultaneous imaging model, and the dual tracer simultaneous imaging model can simultaneously iteratively reconstruct the activity distribution of the first tracer and the activity distribution of the second tracer.

[0075] In some exemplary embodiments, fixed-point iterative algorithms with convergence proofs can be used to iterate over the first and second tracer activity distributions, such as the Fixed-Point Proximity Algorithm with proximity operator association, the Douglas-Rachford splitting method, the Primal-Dual method, and the inexact Uzawa method. Of course, other iterative algorithms can also be used, such as the Alternating Direction Method of Multipliers (ADMM) or the Ordered Subsets Expectation Maximization (OSEM) algorithm.

[0076] S103. Based on the first tracer activity distribution and the second tracer activity distribution obtained in each iteration, obtain the evaluation parameters that reflect the accuracy of each iteration.

[0077] In practical applications, the accuracy of the iteration results can be tested by examining the first and second tracer activity distributions obtained in each iteration, thus obtaining evaluation parameters that reflect the accuracy of each iteration.

[0078] In some exemplary embodiments, the forward problem of the physical model can be solved based on the first and second tracer activity distributions obtained through iteration, and simulated measurement data can be calculated. Then, based on the difference between the simulated measurement data and the actual measurement data, evaluation parameters that can reflect the accuracy of the iteration can be obtained. Of course, in addition to comparing the difference between the simulated measurement data and the actual measurement data, statistical tests, such as t-tests and F-tests, can also be performed to determine whether there is no statistically significant difference between the simulated measurement data and the actual measurement data. If there is no significant difference, it indicates that the iteration results are correct and reasonable within the framework of the physical model and have high accuracy. In this context, solving the forward problem of the physical model can be understood as the process of predicting the output or behavior of a system by using physical laws and / or mathematical methods that conform to the laws of nature, given the physical characteristics and related parameters of the system. In this example, on the one hand, one or more measurable physical information associated with the activity distribution of the first tracer and the activity distribution of the second tracer can be determined, and the actual measurement data of the measurable physical information can be obtained. On the other hand, based on the first tracer, the second tracer, and the associated known parameters, the simulated measurement data of the measurable physical information can be calculated, and then the evaluation parameters corresponding to the iteration can be determined based on the simulated measurement data and the actual measurement data.

[0079] Since the first and second tracer activity distributions can be presented in image form, in some other possible embodiments, evaluation parameters for each iteration can be obtained based on image feature-based evaluation methods. For example, taking the structural similarity index (SSIM) as an example, the similarity of images can be comprehensively evaluated from three aspects: brightness, contrast, and structure. For tracer activity distribution images, the mean, variance, and covariance of the specified regions to be compared can be determined, and then the SSIM can be determined based on the statistics of different specified regions. The SSIM value is between [−1, 1]. The closer the value is to 1, the more structurally similar the activity distribution image obtained by the iteration is to the real image, and the higher the accuracy. Another example is edge detection and comparison, that is, edge information in the iterative result image and the reference image can be extracted through an edge detection algorithm, and then the position, intensity, and continuity of the edges can be compared. If the features match, the iterative result can be determined to have high accuracy.

[0080] S104, if the iteration stopping condition is met, determine the target activity distribution of the first tracer and the target activity distribution of the second tracer based on the evaluation parameters corresponding to each of the multiple iterations.

[0081] It is understandable that, in specific implementations, relevant iterative algorithms can be used to iterate the first and second tracer activity distributions multiple times simultaneously, obtaining the evaluation parameters corresponding to each iteration. Furthermore, when the iteration stopping condition is met (e.g., when the iteration result converges or the number of iterations reaches a preset number), the first and second tracer activity distributions with the highest iteration accuracy can be determined based on the evaluation parameters corresponding to each iteration. These distributions are then used as the first and second target tracer activity distributions. In some examples, the first and second tracer activity distributions with the highest iteration accuracy can be determined based on the evaluation parameters and used as the first and second target tracer activity distributions.

[0082] In the aforementioned medical image imaging method, first and second double-matching data of the scanned object can be acquired. The first double-matching data is determined based on double-matching events generated by nuclides in the first tracer and the second tracer, while the second double-matching data is determined based on double-matching events among the three-matching events generated by nuclides in the second tracer. Then, based on the first and second double-matching data, the activity distributions of the first and second tracers can be iteratively obtained simultaneously. Evaluation parameters reflecting the accuracy of each iteration are obtained based on the activity distributions of the first and second tracers obtained in each iteration. Furthermore, when the iteration stopping condition is met, the target activity distributions of the first and second tracers are determined based on the evaluation parameters corresponding to each iteration. In this embodiment, by injecting dual tracers into the scanned object and simultaneously modeling it, the activity distribution of the dual tracers within the scanned object is determined. This reduces the number of scans while providing more vital activity information about the scanned object, effectively balancing the imaging quality and efficiency of nuclear medicine images.

[0083] In one embodiment, such as Figure 2 As shown, in step S103, based on the first tracer activity distribution and the second tracer activity distribution obtained in each iteration, an evaluation parameter reflecting the accuracy of each iteration is obtained, which may include the following steps:

[0084] S201, based on the first tracer activity distribution and the second tracer activity distribution obtained through iteration, determine the first double coincidence data reconstruction result, and based on the second tracer activity distribution obtained through iteration, determine the second double coincidence data reconstruction result.

[0085] The first double coincidence data can be understood as including the double coincidence event data corresponding to the double coincidence events generated by the nuclides in the first tracer, and the double coincidence data corresponding to the double coincidence times included in the triple coincidence events generated by the nuclides in the second tracer. Therefore, both the activity distributions of the first and second tracers will affect the actual observed values ​​of the first double coincidence data. The first double coincidence data can be understood as the result of the combined effect of the activity distributions of the first and second tracers. Based on this, the first double coincidence data can be reconstructed according to the iteratively obtained activity distributions of the first and second tracers, and the reconstruction result of the first double coincidence data can be determined.

[0086] Furthermore, the activity distribution of the second tracer affects not only the first double coincidence data but also the second double coincidence data. Since the second double coincidence data is determined based on the double coincidence event among the three coincidence events generated by the nuclide in the second tracer, it can be assumed that the activity distribution of the first tracer does not affect the actual observed values ​​of the second double coincidence data. Based on this, the reconstruction result of the second double coincidence data can be determined according to the second tracer activity distribution obtained through iteration.

[0087] S202, based on the first difference between the reconstruction result of the first double-matching data and the first double-matching data, and the second difference between the reconstruction result of the second double-matching data and the second double-matching data, determine the fidelity term for the current iteration.

[0088] In practice, after obtaining the reconstruction results of the first and second pairs of identical data, the degree of deviation between the reconstructed values ​​and the observed values ​​can be determined. Specifically, the first difference between the reconstruction results of the first and second pairs of identical data and the first pair of identical data can be determined, as well as the second difference between the reconstruction results of the second and second pairs of identical data and the second pair of identical data. Then, based on the first and second differences, a fidelity term reflecting the degree of matching between the reconstruction results of the identical data and the actual identical data can be determined.

[0089] S203, based on the first tracer activity distribution and the second tracer activity distribution obtained by iteration, determine the corresponding regularization term and the positive constraint term used to constrain the range of values ​​of the tracer activity distribution.

[0090] In practice, since the activity distributions of the first and second tracer are positive, this step can obtain positive constraint terms to constrain the range of values ​​for the first and second tracer activity distributions. In one example, the positive constraint terms for the first and second tracer activity distributions are... Its function It can be defined as:

[0091]

[0092] in, The activity distribution of the first tracer. This represents the activity distribution of the second tracer.

[0093] On the other hand, since noise exists in the double-matching data, this embodiment can also determine the corresponding regularization term based on the first and second tracer activity distributions obtained through iteration. Adding a regularization term suppresses noise and improves the iteration effect. In some examples, the regularization term corresponding to the first and second tracer activity distributions... Can be taken -One or more of the following: TV total variation, smooth structure tensor total variation (STV), and relative difference penalty.

[0094] S204. Based on the summation of the fidelity term, regularization term, and positive constraint term corresponding to each iteration, determine the evaluation parameters that reflect the accuracy of each iteration.

[0095] Then, for the first tracer activity distribution and the second tracer activity distribution obtained in each iteration, the evaluation parameters reflecting the accuracy of each iteration can be determined based on the summation of the fidelity term, regularization term and positive constraint term corresponding to each iteration.

[0096] The evaluation parameters obtained through the above method are inversely proportional to the accuracy of the iteration. That is, the smaller the value of the evaluation parameter, the higher the accuracy of the iteration. By comparing the evaluation parameters of multiple iterations, the iteration result when the evaluation parameters reach their minimum (i.e., the first tracer activity distribution and the second tracer activity distribution) can be determined. The first tracer activity distribution and the second tracer activity distribution when the evaluation parameters reach their minimum are taken as the first tracer target activity distribution and the second tracer target activity distribution.

[0097] In this embodiment, by summing the fidelity term, regularization term, and positive constraint term corresponding to each iteration, evaluation parameters reflecting the accuracy of each iteration are determined. This enables effective physical modeling of simultaneous imaging with dual tracers and the construction of a corresponding mathematical model. It also allows for objective and accurate detection of the iteration accuracy of tracer activity distribution each time, thus enabling accurate and effective simultaneous imaging with dual tracers.

[0098] In one embodiment, step S201, determining the first double-matching data reconstruction result based on the iteratively obtained first tracer activity distribution and second tracer activity distribution, and determining the second double-matching data reconstruction result based on the iteratively obtained second tracer activity distribution, may include the following steps:

[0099] Based on the first tracer activity distribution obtained through iteration, the first preset influence factor of the first tracer activity distribution on the first double-matching data, and the second preset influence factor of the second tracer activity distribution obtained through iteration on the first double-matching data, the reconstruction result of the first double-matching data is determined; and based on the third preset influence factor of the second tracer activity distribution obtained through iteration on the second double-matching data, the reconstruction result of the second double-matching data is determined.

[0100] Specifically, the first double coincidence data can be understood as including the double coincidence event data corresponding to the double coincidence events generated by the nuclide in the first tracer, and the double coincidence data corresponding to the double coincidence times included in the triple coincidence events generated by the nuclide in the second tracer. Then, the activity distribution of the first tracer and the activity distribution of the second tracer will both affect the actual observed value of the first double coincidence data. The first double coincidence data can be understood as the result of the combined effect of the activity distribution of the first tracer and the activity distribution of the second tracer.

[0101] Based on this, a preset influence factor on the first tracer activity distribution and a preset influence factor on the first double coincidence data can be determined in advance. For ease of distinction, the former preset influence factor is referred to as the first preset influence factor, and the latter as the second preset influence factor. The first preset influence factor can characterize the contribution mode or degree of the first tracer activity distribution to the formation of the first double coincidence data. In some examples, the first preset influence factor can be a forward projection operator that maps the first tracer activity distribution to double coincidence event data of the first tracer. Similarly, the second preset influence factor can characterize the contribution of the second tracer activity distribution to the formation of the first double coincidence data. In some examples, the second preset influence factor can be a positive projection operator that maps the second tracer activity distribution to double coincidence event data generated by the second tracer nuclide (i.e., double coincidence event data corresponding to double coincidence events other than triple coincidence events). .

[0102] In addition, the activity distribution of the second tracer will affect not only the actual observed value of the first double coincidence data, but also the actual observed value of the second double coincidence data. Since the second double coincidence data is determined based on the double coincidence event among the triple coincidence events generated by the nuclide in the second tracer, it can be assumed that the activity distribution of the first tracer does not affect the actual observed value of the second double coincidence data.

[0103] Based on this, a preset influence factor on the second tracer activity distribution regarding the second double coincidence data can be determined in advance. For ease of distinction, this preset influence factor is referred to as the third preset influence factor. The third preset influence factor can characterize the contribution mode or degree of the second tracer activity distribution to the formation of the second double coincidence data. In some examples, the third preset influence factor can be a positive projection operator that maps the second tracer activity distribution to the second double coincidence data of the second tracer. .

[0104] Furthermore, based on the iteratively obtained first tracer activity distribution, first preset influence factor, iteratively obtained second tracer activity distribution, and second preset influence factor, the first double-consistent data can be reconstructed through calculation to obtain the first double-consistent data reconstruction result. On the other hand, based on the iteratively obtained second tracer activity distribution and third preset influence factor, the second double-consistent data can be reconstructed through calculation to obtain the second double-consistent data reconstruction result.

[0105] In this embodiment, a physical model can be constructed by combining the first preset influence factor, the second preset influence factor, and the third preset influence factor to connect the first tracer activity distribution, the second tracer activity distribution, the first double coincidence data, and the second double coincidence data. This helps to obtain accurate and reliable reconstruction results of the first double coincidence data and the second double coincidence data, thereby detecting the accuracy of each iteration.

[0106] In an exemplary embodiment, determining the reconstruction result of the first double-matching data based on the iteratively obtained first tracer activity distribution, a first preset influence factor of the first tracer activity distribution on the first double-matching data, and the iteratively obtained second tracer activity distribution and a second preset influence factor of the second tracer activity distribution on the first double-matching data may include the following steps:

[0107] Based on the product of the first tracer activity distribution obtained through iteration and the first preset influence factor of the first tracer activity distribution on the first double coincidence data, the reconstruction result of the double coincidence data of the double coincidence events generated by the nuclides in the first tracer is determined; based on the product of the second tracer activity distribution obtained through iteration and the second preset influence factor of the second tracer activity distribution on the first double coincidence data, the reconstruction result of the double coincidence data of the double coincidence events generated by the nuclides in the second tracer is determined; based on the reconstruction results of the double coincidence data of the double coincidence events generated by the nuclides in the first tracer and the reconstruction results of the double coincidence data of the double coincidence events generated by the nuclides in the second tracer, the reconstruction result of the first double coincidence data is determined.

[0108] In specific implementation, after obtaining the first and second preset influence factors, on the one hand, the product of the first tracer activity distribution and the first preset influence factor can be determined, and this product is used as the double-consistency data reconstruction result for the double-consistency events generated by the nuclides in the first tracer; on the other hand, the product of the second tracer activity distribution and the second preset influence factor can be determined, and this product is used as the double-consistency data reconstruction result for the double-consistency events generated by the nuclides in the second tracer. Furthermore, the first double-consistency data reconstruction result can be quickly determined by combining the two double-consistency data reconstruction results. This processing method has high interpretability and allows for rapid determination of the first double-consistency data reconstruction result through multiplication, making the process simple and effectively improving the iterative efficiency of the double tracer activity distribution.

[0109] In an exemplary embodiment, determining the first double coincidence data reconstruction result based on the double coincidence data reconstruction result of the double coincidence events generated by the nuclide in the first tracer and the double coincidence data reconstruction result of the double coincidence events generated by the nuclide in the second tracer may include:

[0110] Obtain the first scattering estimation information corresponding to the first double coincidence data; based on the reconstruction results of the double coincidence data of the double coincidence events generated by the nuclide in the first tracer, the reconstruction results of the double coincidence data of the double coincidence events generated by the nuclide in the second tracer, and the summation results of the first scattering estimation information, obtain the reconstruction result of the first double coincidence data.

[0111] In practice, scattering estimation can be performed on the first double coincidence data to obtain scattering estimation information. For ease of distinction, this scattering estimation information is referred to as the first scattering estimation information.

[0112] Specifically, when gamma rays pass through the scanned object, their direction is deflected due to interaction with atoms, resulting in scattering. In this embodiment, scattering estimation is performed on the first double coincidence data. The estimated first scattering information, the reconstructed double coincidence data of the double coincidence events generated by the nuclides in the first tracer, and the reconstructed double coincidence data of the double coincidence events generated by the nuclides in the second tracer are summed. This summation result is used as the reconstructed first double coincidence data result. This allows for the reconstruction of the first double coincidence data while considering scattering phenomena, making the reconstructed result closer to the actual situation and accelerating the iterative efficiency of the dual tracer activity distribution.

[0113] In an exemplary embodiment, determining the reconstruction result of the second double-matching data based on the third preset influence factor of the second tracer activity distribution obtained through iteration and the second tracer activity distribution on the second double-matching data may include the following steps:

[0114] Based on the product of the second tracer activity distribution obtained through iteration and the third preset influence factor of the second tracer activity distribution on the second double coincidence data, the reconstruction result of the double coincidence data corresponding to the double coincidence event in the second tracer triple coincidence event is determined; based on the reconstruction result of the double coincidence data corresponding to the double coincidence event in the second tracer triple coincidence event, the reconstruction result of the second double coincidence data is determined.

[0115] In practical applications, after obtaining the third preset influence factor, the product of the second tracer activity distribution and the third preset influence factor can be determined. This product is used as the reconstruction result of the double coincidence data corresponding to the double coincidence event in the three coincidence events of the second tracer, and the reconstruction result of the second double coincidence data is determined based on this result. This processing method has high interpretability on the one hand, and the reconstruction result of the second double coincidence data can be quickly determined by multiplication on the other hand. The processing is simple and effectively improves the iteration efficiency of the double tracer activity distribution.

[0116] In an exemplary embodiment, determining the second double-matching data reconstruction result based on the double-matching data reconstruction result corresponding to the double-matching event in the second tracer triple-matching event may include the following steps:

[0117] Obtain the second scattering estimation information corresponding to the second double coincidence data; based on the reconstruction result of the double coincidence data corresponding to the double coincidence event in the second tracer triple coincidence event, and the summation result of the second scattering estimation information, obtain the reconstruction result of the second double coincidence data.

[0118] In practical applications, scattering estimation can be performed on the second double coincidence data to obtain scattering estimation information. For ease of distinction, this scattering estimation information is referred to as the second scattering estimation information. In this embodiment, by performing scattering estimation on the second double coincidence data and summing the estimated second scattering estimation information with the reconstruction results of the double coincidence data corresponding to the double coincidence events in the second tracer triple coincidence events, the summation result is used as the reconstruction result of the second double coincidence data. This allows for the reconstruction of the second double coincidence data while considering scattering phenomena, making the reconstruction result closer to the actual situation and accelerating the iteration efficiency of the dual tracer activity distribution.

[0119] In an exemplary embodiment, obtaining the first and second double-matching data of the scanned object in step S101 may include the following steps:

[0120] S1011, acquire the double coincidence event data corresponding to each of the multiple double coincidence events of the scanned object injected with the first tracer and the second tracer; the nuclide of the first tracer generates double coincidence events; the nuclide of the second tracer generates double coincidence events and triple coincidence events, and each double coincidence event data includes two photon detection positions and the detection time difference between the two photon detection positions.

[0121] In some embodiments, the first tracer may be a tracer that produces only pure double coincidence events, such as... The tracer, the second tracer can be a tracer capable of simultaneously generating both double coincidence and triple coincidence events, such as... Tracer.

[0122] After injecting the first and second tracers into the scanned object and performing a PET scan, two types of double-matching data can be obtained by analyzing the scan results and extracting data. To facilitate the distinction between the two types of double-matching data, one type is called the first double-matching data and the other type is called the second double-matching data.

[0123] The first double coincidence data is the coincidence event data corresponding to the directly obtained double coincidence event, while the second double coincidence data is the coincidence event data corresponding to the double coincidence event obtained by filtering from the triple coincidence events. In other words, the first double coincidence data can be obtained without filtering the triple coincidence event data. It is mainly the data of a pair of γ photons with opposite directions and almost collinearity converted from a pair of annihilation radiation electron-positron pairs being detected simultaneously (or within the coincidence time) by two opposing detectors. The γ photons received by these two detectors come from the decay of the same nuclide. The second double coincidence data, on the other hand, needs to be obtained by removing irrelevant data from the coincidence event data corresponding to the triple coincidence events.

[0124] After performing a PET scan on the object, multiple double coincidence events can be obtained, each corresponding to a double coincidence event. The data format for each set of double coincidence event data is as follows: , and This represents the photon detection position of each of the two photons. For the two photon detection poses and The time difference of detection is also known as the time of flight (TOF).

[0125] S1012, Based on the data of each double-matching event, determine the triple-matching event from the multiple double-matching events, and determine the double-matching event data corresponding to the double-matching data other than the triple-matching event as the first double-matching data.

[0126] Then, based on each set of double-coincidence event data, the coincidence event data corresponding to the double-coincidence event and the coincidence event data corresponding to the triple-coincidence event can be separated. Specifically, a triple-coincidence event is composed of three double-coincidence events with repeated detection positions, and the format of a single triple-coincidence event can be represented as: And its detection time difference satisfies the relationship Based on this, triple coincidence events can be selected from multiple double coincidence events based on the data of each set of double coincidence events. The remaining double coincidence events are generated by multiple pairs of γ photons with opposite directions and almost collinearity. The double coincidence event data corresponding to the remaining double coincidence events can be determined as the first double coincidence data.

[0127] S1013. Based on the double coincidence event data corresponding to the triple coincidence event, determine the gamma instantaneous photon among the three photons corresponding to the triple coincidence event. Based on the double coincidence event data corresponding to the two photons other than the gamma instantaneous photon, obtain the second double coincidence data.

[0128] On the other hand, for the obtained triple coincidence events, since the multiple sets of double coincidence data corresponding to the triple coincidence events are mixed with emission prompts... In this step, the double coincidence data corresponding to the gamma transient luminescent particles generated by X-rays are determined based on the prompt-related data in the triple coincidence event of relevant techniques. The event involves removing the double coincidence event data containing gamma transient photons from the double coincidence event data corresponding to the triple coincidence event. Correspondingly, the double coincidence event data corresponding to the two photons other than the gamma transient photons can be used as the second double coincidence data.

[0129] In some embodiments, after the first and second double-matching data are obtained by screening, the data can be corrected (such as dead time correction) and then converted into a chord diagram before being input into the dual tracer simultaneous imaging model for subsequent processing according to the embodiments of this application.

[0130] In this embodiment, by separating triple coincidence events and double coincidence events, and removing the double coincidence data corresponding to gamma transient luminescent particles in the triple coincidence event data, it is possible to effectively reduce the interference data that interferes with nuclear medicine imaging and improve the accuracy and efficiency of subsequent model iterations.

[0131] To enable those skilled in the art to better understand the above steps, the following example illustrates the embodiments of this application, but it should be understood that the embodiments of this application are not limited thereto.

[0132] refer to Figure 3 In practical applications, the injection site can be obtained in a single scan. Tracer (i.e., the first tracer) and The data consists of multiple double coincidence events (list-mode data) obtained from PET scans of the target object using the tracer (i.e., the second tracer). The format of a single list-mode data point is as follows: ,in and This represents the photon detection position of each of the two photons. for and Detection time difference.

[0133] Then, double-matching and triple-matching data can be separated. Specifically, this can be achieved by extracting data from multiple list-mode datasets. Three matching events, the remaining list-mode data is The first double coincidence data (i.e., the first double coincidence data). A triple coincidence event consists of three double coincidence events with repeated probe positions. The format of a single triple coincidence event is as follows: And satisfy the relation .

[0134] For triple coincidence events, Time-of-Flight (TOF) information can be used to determine the event. , and Which one is the prompt? The photons produced by the event, and the prompt- Of the double coincidence events associated with the photons produced by the event, only one double coincidence event remains. Double-matching data (i.e., the second double-matching data).

[0135] Then, you can... Double-compliant data and The double-consistent data are corrected and converted into chord diagrams, denoted as follows: and ,in , Depend on The signal composition of the tracer, Depend on The signal composition of the tracer. Then... and The data is input into the dual-tracer simultaneous imaging model. In this embodiment, the dual-tracer simultaneous imaging model can be as follows:

[0136]

[0137] in, for tracer activity distribution for Tracer activity distribution; For fidelity, measure the reconstructed value. and observed values The degree of deviation is related to the noise distribution; for example, for Poisson noise, we can take... For Gaussian noise, we can take ; Positive constraint; and They are respectively for and Scattering estimation information obtained by performing scattering estimation.

[0138] In some exemplary embodiments, when dealing with and When performing iterations, different iteration algorithms can be selected depending on the specific circumstances. For example, when... When differentiable, iteration is performed using the Fixed-Point Proximity Algorithm:

[0139]

[0140] in, , , Linear operators Defined as positive real number The gradient step size, and For a symmetric positive definite operator, the function conjugate definition:

[0141]

[0142] function Proximity operator Defined as:

[0143]

[0144] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0145] Based on the same inventive concept, this application also provides a medical image imaging apparatus for implementing the medical image imaging method described above. The solution provided by this apparatus is similar to the implementation described in the above method; therefore, the specific limitations in one or more medical image imaging apparatus embodiments provided below can be found in the limitations of the medical image imaging method described above, and will not be repeated here.

[0146] In one exemplary embodiment, such as Figure 4 As shown, a medical imaging device is provided, comprising:

[0147] The dual-match data acquisition module 401 is used to acquire first dual-match data and second dual-match data of the scanned object; the first dual-match data is determined based on the dual-match events generated by the nuclide in the first tracer and the dual-match events generated by the nuclide in the second tracer in the scanned object, and the second dual-match data is determined based on the dual-match events among the three-match events generated by the nuclide in the second tracer;

[0148] The activity distribution iteration module 402 is used to simultaneously iterate and obtain the first tracer activity distribution and the second tracer activity distribution based on the first double coincidence data and the second double coincidence data.

[0149] Evaluation module 403 is used to obtain evaluation parameters reflecting the accuracy of each iteration based on the first tracer activity distribution and the second tracer activity distribution obtained in each iteration;

[0150] The iteration result determination module 404 is used to determine the first tracer target activity distribution and the second tracer target activity distribution based on the evaluation parameters corresponding to each of the multiple iterations, provided that the iteration stopping condition is met.

[0151] In one embodiment, the evaluation module 403 is used to:

[0152] Based on the first tracer activity distribution and the second tracer activity distribution obtained through iteration, the first double coincidence data reconstruction result is determined, and based on the second tracer activity distribution obtained through iteration, the second double coincidence data reconstruction result is determined.

[0153] Based on the first difference between the reconstruction result of the first double-matching data and the first double-matching data, and the second difference between the reconstruction result of the second double-matching data and the second double-matching data, the fidelity item of the current iteration is determined;

[0154] Based on the first and second tracer activity distributions obtained through iteration, determine the corresponding regularization term and the positive constraint term used to constrain the range of values ​​for the tracer activity distribution;

[0155] Based on the summation of the fidelity term, the regularization term, and the positive constraint term corresponding to each iteration, an evaluation parameter reflecting the accuracy of each iteration is determined.

[0156] In one embodiment, the evaluation module 403 is used to:

[0157] Based on the iteratively obtained first tracer activity distribution, the first preset influence factor of the first tracer activity distribution on the first double-matching data, and the iteratively obtained second tracer activity distribution and the second preset influence factor of the second tracer activity distribution on the first double-matching data, the reconstruction result of the first double-matching data is determined; and,

[0158] Based on the third preset influence factor of the second tracer activity distribution and the second tracer activity distribution obtained through iteration on the second double coincidence data, the reconstruction result of the second double coincidence data is determined.

[0159] In one embodiment, the evaluation module 403 is used to:

[0160] Based on the product of the first tracer activity distribution obtained through iteration and the first preset influence factor of the first tracer activity distribution on the first double coincidence data, the double coincidence data reconstruction result of the double coincidence event generated by the nuclide in the first tracer is determined;

[0161] Based on the product of the second tracer activity distribution obtained through iteration and the second preset influence factor of the second tracer activity distribution on the first double coincidence data, the double coincidence data reconstruction result of the double coincidence event generated by the nuclide in the second tracer is determined;

[0162] The first double coincidence data reconstruction result is determined based on the double coincidence data reconstruction results of the double coincidence events generated by the nuclides in the first tracer and the double coincidence data reconstruction results of the double coincidence events generated by the nuclides in the second tracer.

[0163] In one embodiment, the evaluation module 403 is used to:

[0164] Obtain the first scattering estimation information corresponding to the first double coincidence data;

[0165] The first double coincidence data reconstruction result is obtained based on the double coincidence data reconstruction results of the double coincidence events generated by the nuclides in the first tracer, the double coincidence data reconstruction results of the double coincidence events generated by the nuclides in the second tracer, and the summation result of the first scattering estimation information.

[0166] In one embodiment, the evaluation module 403 is used to:

[0167] Based on the product of the second tracer activity distribution obtained through iteration and the third preset influence factor of the second tracer activity distribution on the second double coincidence data, the reconstruction result of the double coincidence data corresponding to the double coincidence event in the second tracer triple coincidence event is determined;

[0168] The second double-match data reconstruction result is determined based on the double-match data reconstruction result corresponding to the double-match event in the second tracer triple-match event.

[0169] In one embodiment, the evaluation module 403 is used to:

[0170] Obtain the second scattering estimation information corresponding to the second double coincidence data;

[0171] The second double coincidence data reconstruction result is obtained based on the double coincidence data reconstruction result corresponding to the double coincidence event in the second tracer triple coincidence event and the summation result of the second scattering estimation information.

[0172] In one embodiment, the dual-compatibility data acquisition module 401 is used for:

[0173] Acquire double coincidence event data corresponding to multiple double coincidence events of a scanned object injected with a first tracer and a second tracer; the nuclide of the first tracer generates double coincidence events; the nuclide of the second tracer generates double coincidence events and triple coincidence events, and each double coincidence event data includes two photon detection positions and the detection time difference between the two photon detection positions;

[0174] Based on each of the double-matching event data, a triple-matching event is determined from the plurality of double-matching events, and the double-matching event data corresponding to the double-matching data other than the triple-matching event is determined as the first double-matching data;

[0175] Based on the double coincidence event data corresponding to the triple coincidence event, determine the gamma transient photon among the three photons corresponding to the triple coincidence event, and obtain the second double coincidence data based on the double coincidence event data corresponding to the two photons other than the gamma transient photon.

[0176] Each module in the aforementioned medical imaging device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0177] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 5As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores nuclear medicine imaging data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a medical image imaging method.

[0178] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 6 As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a medical image imaging method. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0179] Those skilled in the art will understand that Figure 5 and Figure 6The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0180] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0181] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0182] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0183] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0184] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0185] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0186] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A medical image imaging method, characterized in that, The method includes: Acquire first double coincidence data and second double coincidence data of the scanned object; the first double coincidence data is determined based on double coincidence events generated by nuclides in the first tracer and double coincidence events generated by nuclides in the second tracer in the scanned object, and the second double coincidence data is determined based on double coincidence events among the triple coincidence events generated by nuclides in the second tracer; Based on the first double coincidence data and the second double coincidence data, the first tracer activity distribution and the second tracer activity distribution are obtained simultaneously through iteration. Based on the first tracer activity distribution and the second tracer activity distribution obtained in each iteration, evaluation parameters reflecting the accuracy of each iteration are obtained; If the iteration stopping condition is met, the target activity distribution of the first tracer and the target activity distribution of the second tracer are determined based on the evaluation parameters corresponding to each of the multiple iterations.

2. The method according to claim 1, characterized in that, The step of obtaining evaluation parameters reflecting the accuracy of each iteration based on the first tracer activity distribution and the second tracer activity distribution obtained in each iteration includes: Based on the first tracer activity distribution and the second tracer activity distribution obtained through iteration, the first double coincidence data reconstruction result is determined, and based on the second tracer activity distribution obtained through iteration, the second double coincidence data reconstruction result is determined. Based on the first difference between the reconstruction result of the first double-matching data and the first double-matching data, and the second difference between the reconstruction result of the second double-matching data and the second double-matching data, the fidelity item of the current iteration is determined; Based on the first and second tracer activity distributions obtained through iteration, determine the corresponding regularization term and the positive constraint term used to constrain the range of values ​​for the tracer activity distribution; Based on the summation of the fidelity term, the regularization term, and the positive constraint term corresponding to each iteration, an evaluation parameter reflecting the accuracy of each iteration is determined.

3. The method according to claim 2, characterized in that, The step of determining the first double-matching data reconstruction result based on the first and second tracer activity distributions obtained through iteration, and determining the second double-matching data reconstruction result based on the second tracer activity distribution obtained through iteration, includes: Based on the iteratively obtained first tracer activity distribution, the first preset influence factor of the first tracer activity distribution on the first double-matching data, and the iteratively obtained second tracer activity distribution and the second preset influence factor of the second tracer activity distribution on the first double-matching data, the reconstruction result of the first double-matching data is determined; and, The reconstruction result of the second double coincidence data is determined based on the third preset influence factor of the second tracer activity distribution and the second tracer activity distribution obtained by iteration on the second double coincidence data.

4. The method according to claim 3, characterized in that, The step of determining the reconstruction result of the first double-matching data based on the iteratively obtained first tracer activity distribution, the first preset influence factor of the first tracer activity distribution on the first double-matching data, and the iteratively obtained second tracer activity distribution and the second preset influence factor of the second tracer activity distribution on the first double-matching data includes: Based on the product of the first tracer activity distribution obtained through iteration and the first preset influence factor of the first tracer activity distribution on the first double coincidence data, the double coincidence data reconstruction result of the double coincidence event generated by the nuclide in the first tracer is determined; Based on the product of the second tracer activity distribution obtained through iteration and the second preset influence factor of the second tracer activity distribution on the first double coincidence data, the double coincidence data reconstruction result of the double coincidence event generated by the nuclide in the second tracer is determined; The first double coincidence data reconstruction result is determined based on the double coincidence data reconstruction results of the double coincidence events generated by the nuclides in the first tracer and the double coincidence data reconstruction results of the double coincidence events generated by the nuclides in the second tracer.

5. The method according to claim 4, characterized in that, The step of determining the first double coincidence data reconstruction result based on the double coincidence data reconstruction result of the double coincidence events generated by the nuclides in the first tracer and the double coincidence data reconstruction result of the double coincidence events generated by the nuclides in the second tracer includes: Obtain the first scattering estimation information corresponding to the first double coincidence data; The first double coincidence data reconstruction result is obtained based on the double coincidence data reconstruction results of the double coincidence events generated by the nuclides in the first tracer, the double coincidence data reconstruction results of the double coincidence events generated by the nuclides in the second tracer, and the summation result of the first scattering estimation information.

6. The method according to claim 3, characterized in that, The step of determining the reconstruction result of the second double-matching data based on the third preset influence factor of the second tracer activity distribution and the second tracer activity distribution obtained through iteration on the second double-matching data includes: Based on the product of the second tracer activity distribution obtained through iteration and the third preset influence factor of the second tracer activity distribution on the second double coincidence data, the reconstruction result of the double coincidence data corresponding to the double coincidence event in the second tracer triple coincidence event is determined; The second double-match data reconstruction result is determined based on the double-match data reconstruction result corresponding to the double-match event in the second tracer triple-match event.

7. The method according to claim 6, characterized in that, The step of determining the second double-matching data reconstruction result based on the double-matching data reconstruction result corresponding to the double-matching event in the second tracer triple-matching event includes: Obtain the second scattering estimation information corresponding to the second double coincidence data; The second double coincidence data reconstruction result is obtained based on the double coincidence data reconstruction result corresponding to the double coincidence event in the second tracer triple coincidence event and the summation result of the second scattering estimation information.

8. The method according to any one of claims 1 to 7, characterized in that, The acquisition of the first and second double-matching data of the scanned object includes: Acquire double coincidence event data corresponding to multiple double coincidence events of a scanned object injected with a first tracer and a second tracer; the nuclide of the first tracer generates double coincidence events; the nuclide of the second tracer generates double coincidence events and triple coincidence events, and each double coincidence event data includes two photon detection positions and the detection time difference between the two photon detection positions; Based on each of the double-matching event data, a triple-matching event is determined from the plurality of double-matching events, and the double-matching event data corresponding to the double-matching data other than the triple-matching event is determined as the first double-matching data; Based on the double coincidence event data corresponding to the triple coincidence event, determine the gamma transient photon among the three photons corresponding to the triple coincidence event, and obtain the second double coincidence data based on the double coincidence event data corresponding to the two photons other than the gamma transient photon.

9. A medical image imaging device, characterized in that, The device includes: The dual-match data acquisition module is used to acquire first dual-match data and second dual-match data of the scanned object; the first dual-match data is determined based on dual-match events generated by nuclides in the first tracer and dual-match events generated by nuclides in the second tracer in the scanned object, and the second dual-match data is determined based on dual-match events among the three-match events generated by nuclides in the second tracer; The activity distribution iteration module is used to simultaneously iterate and obtain the first tracer activity distribution and the second tracer activity distribution based on the first double coincidence data and the second double coincidence data. The evaluation module is used to obtain evaluation parameters reflecting the accuracy of each iteration based on the first tracer activity distribution and the second tracer activity distribution obtained in each iteration. The iteration result determination module is used to determine the first tracer target activity distribution and the second tracer target activity distribution based on the evaluation parameters corresponding to each of the multiple iterations, provided that the iteration stopping condition is met.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 8.

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