PET image reconstruction method with self-attenuation correction, equipment, medium and product

Abstract

The invention discloses a PET image reconstruction method and device with a self-attenuation correction function, a medium and a product. The method comprises the steps that measurement data and the total tracer agent concentration corresponding to the measurement data are obtained, and the measurement data are obtained through a positron emission tomography imaging method based on flight time; on the basis of the measurement data, the total concentration, a pre-stored concentration map updating rule, a pre-stored attenuation sinogram updating rule and a pre-stored sensitivity matrix, the concentration map and the attenuation sinogram are alternately updated until iteration is finished, a target concentration map and a target attenuation sinogram are obtained, and the target concentration map serves as a target PET image; the concentration map update rule and the attenuation sinogram update rule are both determined based on a minimization model, the minimization model is used for minimizing a negative logarithm maximum likelihood function of a measurement data model, the minimization model comprises a concentration constraint term, and the measurement data model is used for constraining attenuation of measurement data in an exponential form. According to the embodiment of the invention, the PET image with high image quality can be reconstructed through self-attenuation correction.

Description

Technical Field

[0001] The present invention relates to the field of image processing technology, and in particular to a PET image reconstruction method, device, medium and product with self-attenuation correction. Background Art

[0002] The physical mechanism of PET imaging (positron emission tomography) is that after the radioactive isotope-labeled tracer enters the target object, it decays in the target object's body and releases positrons. These positrons instantly annihilate with the surrounding electrons and produce a pair of gamma photons with an energy of 511 keV that are emitted in opposite directions at an angle of approximately 180 degrees. The distribution of the tracer in the body is reconstructed by measuring the paired gamma photons, and then the metabolic functional characteristics of the organs and tissues inside the target are obtained based on the different tracer concentrations in different regions. However, since gamma rays interact with substances in the body, the gamma photons measured by the detector have undergone attenuation. In order to obtain quantitatively accurate and high-quality PET images, attenuation correction of the measurement data is essential.

[0003] Currently, the most commonly used correction method is to scan with additional imaging equipment, estimate the attenuation coefficient in the body in advance, and then perform attenuation correction on the measured data based on the attenuation coefficient. As early as the 1980s, time-of-flight (TOF) technology was proposed. As the name suggests, this technology can measure the time difference between pairs of photons arriving at the detector. In theory, the location of the annihilation point can be completely determined by measuring only the time difference, but in fact this technology is limited by the accuracy of the time resolution. Based on the TOF principle, in theory, quantitative image reconstruction can be achieved by directly relocating the location of the annihilation point only through PET acquisition data, but the existing technology is limited by the accuracy of the time resolution and cannot reconstruct high-quality PET images based on TOF. Summary of the invention

[0004] The present invention provides a PET image reconstruction method, device, medium and product with self-attenuation correction to solve the problem that the prior art cannot reconstruct high-quality PET images based on TOF.

[0005] According to one aspect of the present invention, there is provided a PET image reconstruction method with self-attenuation correction, comprising:

[0006] Acquire measurement data for reconstructing a PET image of a target object and a total tracer concentration corresponding to the measurement data, wherein the measurement data is acquired using a positron emission tomography imaging method based on time of flight;

[0007] Based on the measurement data, the total concentration, and the pre-stored concentration map update rule, attenuation sinusoidal map update rule, and sensitivity matrix, the concentration map and the attenuation sinusoidal map are updated alternately until the iteration ends, a target concentration map and a target attenuation sinusoidal map are obtained, and the target concentration map is used as a target PET image;

[0008] Among them, the concentration map update rule and the attenuation sinusoidal map update rule are both determined based on a minimization model, the minimization model is used to minimize the negative logarithmic maximum likelihood function of the measurement data model, the minimization model includes a concentration constraint term, the concentration constraint term is used to make the sum of the tracer concentrations corresponding to all concentration maps generated in each iteration equal to the total concentration, and the measurement data model is used to constrain the measurement data to decay exponentially.

[0009] According to another aspect of the present invention, there is provided a PET image reconstruction apparatus with self-attenuation correction, comprising:

[0010] an acquisition module, configured to acquire measurement data for reconstructing a PET image of a target object and a total concentration of a tracer corresponding to the measurement data, wherein the measurement data is acquired using a positron emission tomography imaging method based on time of flight;

[0011] An image reconstruction module, configured to alternately update the concentration map and the attenuation sinusoidal map based on the measurement data, the total concentration, and the pre-stored concentration map update rule, attenuation sinusoidal map update rule, and sensitivity matrix until the iteration is completed, to obtain a target concentration map and a target attenuation sinusoidal map, and to use the target concentration map as a target PET image;

[0012] Among them, the concentration map update rule and the attenuation sinusoidal map update rule are both determined based on a minimization model, the minimization model is used to minimize the negative logarithmic maximum likelihood function of the measurement data model, the minimization model includes a concentration constraint term, the concentration constraint term is used to make the sum of the tracer concentrations corresponding to all concentration maps generated in each iteration equal to the total concentration, and the measurement data model is used to constrain the measurement data to decay exponentially.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to the at least one processor; wherein,

[0016] 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 PET image reconstruction method with self-attenuation correction described in any embodiment of the present invention.

[0017] According to another aspect 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 PET image reconstruction method with self-attenuation correction described in any embodiment of the present invention when executed.

[0018] According to another aspect of the present invention, a computer program product is provided, the computer program product comprising a computer program, which, when executed by a processor, implements any of the PET image reconstruction methods with self-attenuation correction.

[0019] The technical solution of the embodiment of the present invention, on the one hand, uses the exponential form of self-attenuation correction as a priori to determine the measurement data model, thereby improving the accuracy of the photon attenuation description; on the other hand, a minimization model is determined based on the negative log-likelihood function of the measurement data model, and the concentration constraint term in the minimization model makes the alternating update process of the concentration map and the attenuation sinusoidal map more stable and easier to converge, so that with the same number of iterations, a concentration map and attenuation sinusoidal map with higher accuracy can be obtained; on the other hand, the update process of the concentration map and the attenuation sinusoidal map does not depend on the selection of parameters, so there is no need to adjust the parameters during the update process of the concentration map and the attenuation sinusoidal map, which simplifies the image reconstruction process; finally, the reconstruction of the concentration map is completed through self-attenuation correction, which simplifies the scanning process of PET imaging and helps to improve patient experience.

[0020] 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

[0021] 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.

[0022] Figure 1 is a flow chart of a PET image reconstruction method with self-attenuation correction provided according to an embodiment of the present invention;

[0023] Figure 2 is another flow chart of a PET image reconstruction method with self-attenuation correction provided according to an embodiment of the present invention;

[0024] Figure 3 is a first true concentration map of a phantom provided according to an embodiment of the present invention;

[0025] Figure 4 is a first attenuation CT (computed tomography) image of a phantom provided according to an embodiment of the present invention;

[0026] Figure 5 is a first true attenuation sinusoidal graph of a phantom provided according to an embodiment of the present invention;

[0027] Figure 6 is a first target concentration map of a phantom provided according to an embodiment of the present invention;

[0028] Figure 7 is a first target attenuation sinusoidal diagram of a phantom provided according to an embodiment of the present invention;

[0029] Figure 8 is a schematic diagram showing the variation of the relative error of the concentration map with the number of iterations according to an embodiment of the present invention;

[0030] Fig. 9 is a schematic diagram showing the variation of the relative error of the attenuated sinusoidal graph with the number of iterations according to an embodiment of the present invention;

[0031] Fig.10 is a schematic diagram showing the change of the relative error of data according to an embodiment of the present invention with the number of iterations;

[0032] Fig.11 is a schematic diagram showing the change of the function value corresponding to the minimization model with the number of iterations provided by an embodiment of the present invention;

[0033] Fig.12 is a second true concentration map of the phantom provided according to an embodiment of the present invention;

[0034] Fig.13 is a second attenuation CT image of the phantom provided according to an embodiment of the present invention;

[0035] Fig.14 is a second true attenuation sinusoidal graph of the phantom provided according to an embodiment of the present invention;

[0036] Fig.15 is a second target concentration map of the phantom provided according to an embodiment of the present invention;

[0037] Fig.16 is a third target concentration map of the phantom provided according to an embodiment of the present invention;

[0038] Fig.17 is a schematic structural diagram of a PET image reconstruction device with self-attenuation correction according to an embodiment of the present invention;

[0039] Fig.18 is another structural schematic diagram of a PET image reconstruction device with self-attenuation correction provided according to an embodiment of the present invention;

[0040] Fig.19 It is a structural schematic diagram of an electronic device for implementing the PET image reconstruction method with self-attenuation correction according to an embodiment of the present invention. DETAILED DESCRIPTION

[0041] 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.

[0042] It should be noted that the term "target" and the like in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0043] Figure 1 The flowchart of the PET image reconstruction method with self-attenuation correction provided by the embodiment of the present invention is applicable to the case of reconstructing a PET image corresponding to measurement data acquired based on a TOF-PET (positron emission tomography based on time of flight) scanning method. The method can be performed by a PET image reconstruction device with self-attenuation correction. The PET image reconstruction device with self-attenuation correction can be implemented in the form of hardware and / or software. The PET image reconstruction device with self-attenuation correction can be configured in an electronic device. Figure 1 As shown, the method includes:

[0044] S110 , obtaining measurement data for reconstructing a PET image of a target object and a total tracer concentration corresponding to the measurement data, wherein the measurement data is obtained using a positron emission tomography imaging method based on time of flight.

[0045] Tracers, also called contrast agents, are positron-emitting radionuclides that need to be injected before a PET scan. Currently, the most widely used is 18F-FDG, a type of glucose with positrons that can participate in human metabolism.

[0046] A tracer of a predetermined total concentration is injected into the target object, and after a set time, the PET imaging device is controlled to perform imaging scanning on the target object based on the TOF-PET method to obtain measurement data of the target object for the total concentration. It is understandable that different total concentrations correspond to different measurement data.

[0047] S120, based on the measurement data, total concentration, and pre-stored concentration map update rules, attenuation sinusoidal map update rules, and sensitivity matrix, alternately update the concentration map and the attenuation sinusoidal map until the iteration ends, obtain the target concentration map and the target attenuation sinusoidal map, and use the target concentration map as the target PET image; wherein, the concentration map update rule and the attenuation sinusoidal map update rule are both determined based on a minimization model, the minimization model is used to minimize the negative logarithmic maximum likelihood function of the measurement data model, the minimization model includes a concentration constraint term, the concentration constraint term is used to make the sum of the tracer concentrations corresponding to all concentration maps generated in each iteration equal to the total concentration, and the measurement data model is used to constrain the measurement data to decay exponentially.

[0048] The concentration map update rule can be understood as the concentration map update formula; the attenuated sine map update rule can be understood as the attenuated sine map update formula.

[0049] Each element in the sensitivity matrix can be expressed as a ijt , which represents the probability that the photon generated in the jth pixel block or voxel block is detected by the i-th response line in the t-th flight time window.

[0050] The measurement data model is used to simulate the attenuation form and attenuation result of the measurement data. The measurement data in the measurement data model of this embodiment is constrained to decay in an exponential form. Specifically: the first mean of the Poisson random variable corresponding to the number of photons collected by the t-th window of each response line is equal to the product of the second mean of the unattenuated measurement data of each response line in the t-th window and the attenuation sinusoid corresponding to each response line that decays in an exponential form; the second mean is equal to the sum of the corresponding measurement data of each pixel block under the t-th window, and the corresponding measurement data of each pixel block is equal to the product of the tracer concentration value in each pixel block and the probability that the photons generated by each pixel block are detected by the t-th window of each response line.

[0051] Measurement data model It can be expressed as:

[0052]

[0053] In this measurement data model, s i represents the line integral of the attenuation diagram on the i-th response line, i = 1, ..., I represents different response lines (line of response, LOR); is m it The mean value, m it represents the Poisson random variable corresponding to the number of photons collected by the t-th time-of-flight window (TOF) of the i-th LOR. In addition, a ijt represents the probability that the photon generated in the jth pixel block or voxel block is detected by the i-th response line in the t-th flight time window, λ j represents the tracer concentration value in the jth pixel block or voxel block, J represents the total number of pixel blocks or the total number of voxel blocks; t = 1, ..., T represents different flight time windows. It represents the mean of the measured data without attenuation for the ith response line in the tth flight time window.

[0054] It should be noted that the attenuation of the measured data can be simply constrained to It can also be constrained to f∶[0,+∞)→[0,+∞) is a one-to-one mapping, as well as other exponential decay forms.

[0055] In order to facilitate the description of the technical solution, The vector formed by λ is called the concentration diagram, and the vector formed by s is called the attenuation sinusoidal diagram.

[0056] In TOF-PET, according to the above modeling, recovering the unknown tracer distribution under attenuation correction is equivalent to solving λ and s simultaneously. Among them, the target concentration map is the target PET image, which can be used for clinical diagnosis and is the information that doctors are concerned about. s does not play a direct role in actual clinical diagnosis.

[0057] Let m it Represents the detection data corresponding to the i-th LOR under the t-th flight time window, and sets all random variables m it are independent of each other, then mathematically, the simultaneous reconstruction problem in TOF-PET can be equivalently converted into a minimization model, which includes multiple constraints and negative log-likelihood functions, such as:

[0058]

[0059] Among them, δ(·) is the indicator function, that is, if the constraint is satisfied, the function value is zero, otherwise, it takes an infinite value; 1 represents an all-one vector of length J; represents the sum of all components of λ, and N represents the total known concentration (total annihilation amount).

[0060] Specifically, the minimization model includes a negative log-likelihood term Non-negative concentration term (δ(λ≥0)), non-negative decaying sinusoidal term (δ(s≥0)), and concentration constraint term The concentration constraint term ensures that the sum of the tracer concentrations corresponding to all concentration maps determined in each iteration round is equal to the total concentration. In other words, the total concentration reconstructed in each iteration round is required to be consistent with the total tracer concentration injected into the target object. Therefore, the concentration constraint term can improve the speed and stability of iterative convergence.

[0061] In one embodiment, each component in the non-negative concentration map corresponding to the non-negative concentration map item is set to be greater than or equal to zero in a defined form, for example, directly defining λ≥0. Each component in the non-negative attenuation sinusoidal map corresponding to the non-negative attenuation sinusoidal map item is set to be greater than or equal to zero in a defined form, for example, directly defining s≥0. The concentration constraint term is directly configured as

[0062] It is understandable that it is not feasible to directly update λ and s at the same time. Therefore, this embodiment adopts an alternating update method, that is, to update λ and s alternately. Alternating update can be understood as keeping the concentration map unchanged and updating the attenuation sinusoidal map; then keeping the attenuation sinusoidal map unchanged and updating the concentration map; then keeping the concentration map unchanged and updating the attenuation sinusoidal map, and so on.

[0063] Based on formula (2), we can get the concentration map update rule, that is, the concentration map update formula. The details are as follows:

[0064]

[0065] Among them, k is the identifier of the current iteration round.

[0066] Let the initial concentration map λ 0 is a vector of all ones, let the initial decay sinusoidal graph s 0 The concentration map is updated based on formula (3) by keeping the attenuation sinusoidal map unchanged and adjusting the relative size of the components in the updated concentration map based on the total concentration defined by the concentration constraint and the principle that the sum of the components in the concentration map remains unchanged. The concentration map is updated again. The details are as follows:

[0067]

[0068] The updated concentration map is used as the current concentration map. The current concentration map remains unchanged and the attenuation sinusoidal map is updated based on the following formula:

[0069]

[0070] After the attenuation sine graph is updated, based on the non-negative principle of each element in the attenuation sine graph, each negative element in the updated attenuation sine graph is set to zero to update the attenuation sine graph again, as follows:

[0071]

[0072] In this way, the concentration map and the attenuation sinusoidal map are updated alternately until the updated concentration map, attenuation sinusoidal map or the number of iterations reaches the corresponding termination condition, and the iteration ends; the concentration map and attenuation sinusoidal map determined in the last iteration round are used as the target concentration map and target attenuation sinusoidal map.

[0073] The termination condition corresponding to the concentration map is that the first image evaluation index of the concentration map determined by adjacent iterations is within the first set range; the termination condition corresponding to the attenuation sinusoidal map is that the second image evaluation index of the attenuation sinusoidal map determined by adjacent iterations is within the second set range. The first image evaluation index and the second image evaluation index can be the same or different; the first set range and the second set range can be the same or different, and the sizes of the two can be determined by the user according to the quality requirements of the concentration map and the attenuation sinusoidal map. In one embodiment, the first image evaluation index and the second image evaluation index are the same, and both are relative errors or structural similarities, etc.

[0074] The termination condition corresponding to the number of iterations is the set number of iterations, such as 50, 500, 1000, 10000, etc.

[0075] During the alternating update process of the concentration graph and the attenuation sinusoidal graph, if the termination condition corresponding to the concentration graph is used to determine whether to continue the iteration, then when the concentration graph determined in any iteration round does not meet the corresponding termination condition, the concentration graph is kept unchanged and the attenuation sinusoidal graph is updated, and then the next iteration round is entered; when the concentration graph determined in any iteration round meets the corresponding termination condition, the iteration can be terminated, and the concentration graph determined in the iteration round is used as the target concentration graph, and the attenuation sinusoidal graph determined in the iteration round is used as the target attenuation sinusoidal graph.

[0076] Since the update process of the concentration map is related to the change of the attenuation sinusoid, after the attenuation sinusoid is determined, the concentration map based on the attenuation sinusoid is also determined. Based on this, in the process of alternating updates of the concentration map and the attenuation sinusoid, if the termination condition corresponding to the attenuation sinusoid is used to determine whether the iteration continues, then if the attenuation sinusoid determined in any iteration round does not meet the corresponding termination condition, enter the next iteration round; if the attenuation sinusoid determined in any iteration round meets the corresponding termination condition, the iteration can be terminated, and the concentration map determined in the iteration round is used as the target concentration map, and the attenuation sinusoid determined in the iteration round is used as the target attenuation sinusoid.

[0077] During the alternating update process of the concentration graph and the attenuation sinusoidal graph, if the termination condition corresponding to the number of iterations is used to determine whether the iteration continues, when the number of iterations corresponding to any iteration round reaches the maximum number of iterations, the attenuation sinusoidal graph determined in the iteration round is used as the target attenuation sinusoidal graph, and the concentration graph determined in the iteration round is used as the target concentration graph, and then the iteration is stopped; or the iteration is stopped directly, at which time the previous iteration round is the last iteration round, so the attenuation sinusoidal graph determined in the previous iteration round is used as the target attenuation sinusoidal graph, and the concentration graph determined in the previous iteration round is used as the target concentration graph.

[0078] It should be noted that, during the concentration map update process, the updates of the concentration maps corresponding to each pixel block or voxel block do not affect each other, so the concentration maps corresponding to different pixel blocks or voxel blocks can be updated serially or in parallel. Similarly, the updates of the attenuation sinusoidal graphs corresponding to each response line do not affect each other, so the attenuation sinusoidal graphs corresponding to different response lines can be updated serially or in parallel. In addition, in the initial iteration round, the attenuation sinusoidal graph can be kept unchanged as the starting attenuation sinusoidal graph, the concentration map can be updated, and then the updated concentration map can be kept unchanged and the attenuation sinusoidal graph can be updated; or the concentration map can be kept unchanged as the starting concentration map, the attenuation sinusoidal graph can be updated, and then the updated attenuation sinusoidal graph can be kept unchanged and the concentration map can be updated.

[0079] In one embodiment, after the target concentration map and the target attenuation sinusoidal map are determined, the target concentration map is displayed in a visualization interface, and the target concentration map is a PET image, so that the user can view the required PET image.

[0080] The technical solution provided by the embodiment of the present invention, on the one hand, uses the exponential form of self-attenuation correction as a priori to determine the measurement data model, thereby improving the accuracy of the photon attenuation description; on the other hand, a minimization model is determined based on the negative log-likelihood function of the measurement data model, and the concentration constraint term in the minimization model makes the alternating update process of the concentration map and the attenuation sinusoidal map more stable and easier to converge, so that with the same number of iterations, a concentration map and attenuation sinusoidal map with higher accuracy can be obtained; on the other hand, the update process of the concentration map and the attenuation sinusoidal map does not depend on the selection of parameters, so there is no need to adjust the parameters during the update process of the concentration map and the attenuation sinusoidal map, which simplifies the image reconstruction process; finally, the reconstruction of the concentration map is completed through self-attenuation correction, which simplifies the scanning process of PET imaging and helps to improve patient experience.

[0081] Figure 2 This is another flow chart of a PET image reconstruction method with self-attenuation correction provided by an embodiment of the present invention. This embodiment adds a termination condition configuration step based on the previous embodiment. Figure 2 As shown, the method includes:

[0082] S210. In response to a confirmation operation on the configuration parameters of the parameter configuration interface, the configuration parameters are used as corresponding termination conditions. The configuration parameters are at least one of a first boundary condition, a second boundary condition, and a maximum number of iterations. The first boundary condition corresponds to a first image evaluation index of a concentration graph, and the second boundary condition corresponds to a second image evaluation index of an attenuation sinusoidal graph.

[0083] In this embodiment, the termination condition used in the PET image reconstruction process is configured as a modifiable item. That is, the user can use the default termination condition to determine the timing of iterative interruption in the PET image reconstruction process, or can configure the termination condition according to actual conditions.

[0084] If the user wants to configure the termination condition by himself, he opens the parameter configuration interface and configures the corresponding configuration parameters in the parameter configuration interface, such as the first boundary condition corresponding to the first image evaluation index of the concentration map, the second boundary condition corresponding to the second image evaluation index of the attenuation sinusoidal map, or the maximum number of iterations. After the parameters are configured, click or touch the confirmation option in the parameter configuration interface, and the processor responds to the confirmation operation and uses the configuration parameters in the configuration interface as the corresponding termination conditions.

[0085] Among them, regarding the first image evaluation index and the second image evaluation index, refer to the above embodiment, and this embodiment will not be repeated here. The first boundary condition corresponds to the first setting range, for example, it is the upper boundary of the first setting range, and the lower boundary of the first setting range defaults to 0; the second boundary condition corresponds to the second setting range, for example, the upper boundary of the second setting range is the second boundary condition, and the lower boundary of the second setting range defaults to 0.

[0086] S220 , obtaining measurement data for reconstructing a PET image of the target object and a total tracer concentration corresponding to the measurement data, wherein the measurement data is obtained using a positron emission tomography imaging method based on time of flight.

[0087] S230, based on the measurement data, total concentration, and pre-stored concentration map update rules, attenuation sinusoidal map update rules, and sensitivity matrix, alternately update the concentration map and the attenuation sinusoidal map until the iteration ends, obtain the target concentration map and the target attenuation sinusoidal map, and use the target concentration map as the target PET image; wherein, the concentration map update rule and the attenuation sinusoidal map update rule are both determined based on a minimization model, the minimization model is used to minimize the negative logarithmic maximum likelihood function of the measurement data model, the minimization model includes a concentration constraint term, the concentration constraint term is used to make the sum of the tracer concentrations corresponding to all concentration maps generated in each iteration equal to the total concentration, and the measurement data model is used to constrain the measurement data to decay in an exponential form.

[0088] In one embodiment, the first evaluation index is a first relative error, and the first boundary condition includes a first relative error threshold. During the alternating update process of the concentration map and the attenuation sinusoidal map, if it is detected that the relative error between the concentration maps determined in adjacent iterations is less than or equal to the first relative error threshold, the iteration is terminated. The concentration map determined in the current iteration is used as the target concentration map, and the attenuation sinusoidal map determined in the current iteration is used as the target attenuation sinusoidal map.

[0089] In one embodiment, the second evaluation index is a second relative error, and the second boundary condition is a second relative error threshold. During the alternating update process of the concentration map and the attenuation sinusoidal map, if it is detected that the relative error between the attenuation sinusoidal maps determined in adjacent iterations is less than or equal to the second relative error threshold, the iteration is terminated. The concentration map determined in the current iteration is used as the target concentration map, and the attenuation sinusoidal map determined in the current iteration is used as the target attenuation sinusoidal map.

[0090] In one embodiment, the termination condition is a combination of the first relative error threshold, the second relative error threshold and the maximum number of iterations. At this time, during the alternating update process of the concentration map and the attenuation sinusoidal map, if it is detected that the relative error between the concentration maps determined in adjacent iterations is less than or equal to the first relative error threshold, and at the same time, it is detected that the relative error between the attenuation sinusoidal maps determined in adjacent iterations is greater than or equal to the second relative error, the iteration is terminated.

[0091] In the embodiment of the present invention, the parameter configuration function corresponding to the parameter configuration interface allows the user to set reasonable termination conditions based on actual needs, thereby improving the flexibility and universality of PET image reconstruction.

[0092] In one embodiment, the PET image reconstruction process is refined by taking the noise-free data of the reconstructed model as an example. The real image is determined by simulating the model. The real image corresponding to the model is distributed in an area of ​​[-15cm, 15cm] × [-15cm, 15cm], and the pixel size is 64 × 64. Figure 3 The first true concentration diagram is shown in Figure 4 The first attenuation CT image shown and Figure 5 The first real attenuation sinusoidal graph is shown in FIG. The grayscale window of the first real attenuation sinusoidal graph is [0, 0.115] cm -1 .

[0093] The first measurement data of the phantom is acquired by parallel beam scanning. Specifically, 64 scanning angles are evenly distributed from 0 to 180 degrees, and 64 detectors are evenly distributed in the range of [-15 cm, 15 cm]. The TOF time resolution considered is approximately 600 ps, ​​and a total of 10 TOF windows with a window width of 4.5 cm are set. The discrete X-ray transformation and the real image are substituted into the imaging model to simulate the generation of noise-free first measurement data, and then the PET image reconstruction method with self-attenuation correction described in the aforementioned embodiment is used to reconstruct the noise-free first measurement data to obtain a first target concentration map (see Figure 6 ) and the first target decay sinusoidal diagram (see Figure 7 ). It is obvious that the first target concentration diagram ( Figure 6 ) and the first true concentration map ( Figure 3 ) is almost the same, the first target attenuation sinusoidal diagram ( Figure 7 ) and the first true decay sinusoidal graph ( Figure 5 ), which indicates that the method described in the embodiment of the present invention can accurately reconstruct quantitative PET images.

[0094] In order to quantitatively measure the performance of the algorithm corresponding to the embodiment of the present invention, the number of iterations is set as large as possible, such as 10 million times, and a curve graph of the relative error of the concentration map versus the number of iterations is drawn ( Figure 8 ), the relative error of the decay sinusoidal graph versus the number of iterations ( Fig. 9 ), the curve of the relative error of the data versus the number of iterations ( Fig.10 ) and the graph of the objective function value versus the number of iterations ( Fig.11 ). The objective function value can be understood as the function value of the minimization model when the concentration map and attenuation sinusoidal map determined by each iteration are input into the minimization model; the data can be understood as the difference between the measured data inferred from the concentration map and attenuation sinusoidal map determined by each iteration and the actually collected measured data. Fig.11 It can be seen from the curve graph of the relative error of the objective function value versus the number of iterations that the method described in the embodiment of the present invention is convergent on the target value, and the curve of the relative error of the concentration graph versus the number of iterations, the curve of the relative error of the attenuation sine graph versus the number of iterations and the curve of the relative error of the data versus the number of iterations also fully illustrate that the method described in the embodiment of the present invention has high stability and convergence.

[0095] In one embodiment, the real concentration map is determined by simulating a phantom so that it is distributed in an area of ​​[-15cm, 15cm]×[-15cm, 15cm], with a pixel size of 128×128. Fig.12 The second true concentration diagram is shown in Fig.13The second real attenuation CT image shown and Fig.14 The second real attenuation sinusoidal diagram is shown. The scanning geometry used is the same as before. Specifically, the scanning angles are set to 128, evenly distributed from 0 to 180 degrees; there are 128 detectors, equidistantly distributed in the range of [-15cm, 15cm]. The TOF time resolution considered is still 600ps, and a total of 10 TOF windows with a window width of 4.5cm are set. The process of generating the noise-free second measurement data and implementing the algorithm is the same as before.

[0096] The PET image reconstruction method with self-attenuation correction described in the above embodiment is used to determine the target concentration map corresponding to the second measurement data, that is, the second target concentration map (such as Fig.15 ).

[0097] In order to quantitatively compare the quality of the reconstructed PET image (target concentration map), the following indicators are used to measure the quality of the PET image (target concentration map). Given two images, the vectors are and u * , their normalized root mean square error is defined as:

[0098]

[0099] The peak signal-to-noise ratio is defined as:

[0100]

[0101] Where I is the total number of image pixels.

[0102] The structural similarity is defined as:

[0103]

[0104] Among them, x i and i Corresponding to and u * The i-th 8×8 window (image block) of M represents the total number of windows.

[0105] The local similarity index is defined as:

[0106]

[0107] in, and Represent the mean and variance of x and y respectively, and c1=c2=0.05.

[0108] The second target concentration map determined by the scheme described in this embodiment ( Fig.15 ) and the second true concentration map ( Fig.12) is 0.00113, the peak signal-to-noise ratio is 76.02, and the result of subtracting the structural similarity is 5.97e-6. It can be seen that the method described in the embodiment of the present invention reconstructs the target PET image with high accuracy and image quality.

[0109] In one embodiment, the real concentration map is determined by simulating a phantom so that it is distributed in an area of ​​[-15cm, 15cm]×[-15cm, 15cm], with a pixel size of 128×128. Fig.12 The second true concentration diagram shown; the scanning geometry used is the same as before, specifically, the scanning angle is set to 128, evenly distributed from 0 to 180 degrees; there are 128 detectors, equidistantly distributed in the range of [-15cm, 15cm]. The TOF time resolution considered is still 600ps, and a total of 10 TOF windows with a window width of 4.5cm are set. The process of generating noise-free second measurement data and implementing the algorithm process are the same as before.

[0110] The second measurement data is put into a Poisson generator to obtain measurement data containing Poisson noise, i.e., the third measurement data. In the experiment, the noise level is set to 7.24 dB. The number of iterations is set to 50 steps, and the above-mentioned PET image reconstruction method with self-attenuation correction is used to reconstruct the third measurement data to obtain the third target concentration map ( Fig.16 ), it is found that the normalized root mean square error between the third target concentration map and the second real image (12) is 0.2520, the peak signal-to-noise ratio is 29.02, and the structural similarity is 0.8996. It can be seen that even for measurement data containing noise, the method of the present invention can still reconstruct a high-quality target concentration map, so the method of the present invention has high robustness.

[0111] Fig.17 The structure diagram of the PET image reconstruction device with self-attenuation correction provided by the embodiment of the present invention is shown in FIG. Fig.17 As shown, the device comprises:

[0112] An acquisition module 310, configured to acquire measurement data for reconstructing a PET image of a target object and a total tracer concentration corresponding to the measurement data, wherein the measurement data is acquired using a positron emission tomography imaging method based on time of flight;

[0113] An image reconstruction module 320 is used to alternately update the concentration map and the attenuation sinusoidal map based on the measurement data, the total concentration, and the pre-stored concentration map update rule, attenuation sinusoidal map update rule, and sensitivity matrix until the iteration ends, to obtain a target concentration map and a target attenuation sinusoidal map, and use the target concentration map as a target PET image;

[0114] Among them, the concentration map update rule and the attenuation sinusoidal map update rule are both determined based on a minimization model, the minimization model is used to minimize the negative logarithmic maximum likelihood function of the measurement data model, the minimization model includes a concentration constraint term, the concentration constraint term is used to make the sum of the tracer concentrations corresponding to all concentration maps generated in each iteration equal to the total concentration, and the measurement data model is used to constrain the measurement data to decay exponentially.

[0115] In one embodiment, the minimization model includes a negative log-likelihood function term, a non-negative concentration graph term, a non-negative decaying sinusoidal graph term, and a concentration constraint term.

[0116] In one embodiment, in the case where the concentration map is greater than or equal to zero, the function value of the non-negative concentration map term is equal to zero;

[0117] In the case where the decaying sinusoidal graph is greater than or equal to zero, the function value of the non-negative decaying sinusoidal graph term is equal to zero;

[0118] When the sum of the tracer concentrations corresponding to all the concentration maps is equal to the total concentration, the function value of the concentration constraint term is equal to zero, otherwise it is infinite.

[0119] In one embodiment, during the process of alternatingly updating the concentration map and the attenuation sinusoidal map, the image reconstruction module is specifically used to:

[0120] While keeping the attenuation sinusoidal graph unchanged and completing the updating of the concentration graph, based on the principle that the sum of the components in the concentration graph remains unchanged, the relative sizes of the components in the updated concentration graph are adjusted to update the concentration graph again;

[0121] While keeping the concentration map unchanged and completing the update of the attenuation sinusoidal map, based on the non-negative principle of each element in the attenuation sinusoidal map, each negative element in the updated attenuation sinusoidal map is set to zero to update the attenuation sinusoidal map again.

[0122] In one embodiment, the measurement data model is configured as:

[0123] The measurement data model is configured as follows:

[0124] The first mean of the Poisson random variable corresponding to the number of photons collected by the t-th window of each response line is equal to the product of the second mean of the unattenuated measurement data of each response line in the t-th window and the exponentially decaying attenuated sinusoid corresponding to each response line;

[0125] The second mean is equal to the sum of the corresponding measurement data of each pixel block under the tth window, and the corresponding measurement data of each pixel block is equal to the product of the tracer concentration value in each pixel block and the probability that the photons generated by each pixel block are detected by the tth window of each response line.

[0126] In one embodiment, Fig.18 As shown, the device further includes a configuration module 33003, and the configuration module 300 is used to:

[0127] In response to a confirmation operation on a configuration parameter of the parameter configuration interface, taking the configuration parameter as a corresponding termination condition;

[0128] The configuration parameter is at least one of a first boundary condition, a second boundary condition, and a maximum number of iterations, the first boundary condition corresponds to a first image evaluation index of the concentration map, and the second boundary condition corresponds to a second image evaluation index of the attenuation sinusoidal map.

[0129] In one embodiment, the image reconstruction module is specifically used for:

[0130] Alternately updating the concentration map and the attenuation sinusoidal map until the updated concentration map, the attenuation sinusoidal map or the number of iterations reaches a corresponding termination condition, thereby terminating the iteration;

[0131] The concentration map and attenuation sinusoidal map determined in the last iteration round are used as the target concentration map and target attenuation sinusoidal map.

[0132] In one embodiment, the device further comprises a display module, wherein the display module is used to:

[0133] The target concentration graph is displayed in a visual interface.

[0134] The technical solution provided by the embodiment of the present invention, on the one hand, uses the exponential form of self-attenuation correction as a priori to determine the measurement data model, thereby improving the accuracy of the photon attenuation description; on the other hand, a minimization model is determined based on the negative log-likelihood function of the measurement data model, and the concentration constraint term in the minimization model makes the alternating update process of the concentration map and the attenuation sinusoidal map more stable and easier to converge, so that with the same number of iterations, a concentration map and attenuation sinusoidal map with higher accuracy can be obtained; on the other hand, the update process of the concentration map and the attenuation sinusoidal map does not depend on the selection of parameters, so there is no need to adjust the parameters during the update process of the concentration map and the attenuation sinusoidal map, which simplifies the image reconstruction process; finally, the reconstruction of the concentration map is completed through self-attenuation correction, which simplifies the scanning process of PET imaging and helps to improve patient experience.

[0135] The PET image reconstruction device with self-attenuation correction provided by the embodiment of the present invention can execute the PET image reconstruction method with self-attenuation correction provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0136] Fig.19 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, 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.

[0137] like Fig.19 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, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (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. An input / output (I / O) interface 15 is also connected to the bus 14.

[0138] 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 / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0139] 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 performs the various methods and processes described above, such as a PET image reconstruction method with self-attenuation correction.

[0140] In some embodiments, the PET image reconstruction method with self-attenuation correction 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 of the PET image reconstruction method with self-attenuation correction described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the PET image reconstruction method with self-attenuation correction in any other appropriate manner (e.g., by means of firmware).

[0141] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can 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 can 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.

[0142] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0143] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination 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 signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0144] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to 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).

[0145] 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.

[0146] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0147] An embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements a PET image reconstruction method with self-attenuation correction as provided in any embodiment of the present application.

[0148] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present invention, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0149] 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.

[0150] 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 PET image reconstruction method with self-attenuation correction, characterized in that: include: Acquire measurement data for reconstructing a PET image of a target object and a total tracer concentration corresponding to the measurement data, wherein the measurement data is acquired using a positron emission tomography imaging method based on time of flight; Based on the measurement data, the total concentration, and the pre-stored concentration map update rule, attenuation sinusoidal map update rule, and sensitivity matrix, the concentration map and the attenuation sinusoidal map are updated alternately until the iteration ends, a target concentration map and a target attenuation sinusoidal map are obtained, and the target concentration map is used as a target PET image; Among them, the concentration map update rule and the attenuation sinusoidal map update rule are both determined based on a minimization model, the minimization model is used to minimize the negative logarithmic maximum likelihood function of the measurement data model, the minimization model includes a concentration constraint term, the concentration constraint term is used to make the sum of the tracer concentrations corresponding to all concentration maps generated in each iteration equal to the total concentration, and the measurement data model is used to constrain the measurement data to decay exponentially.

2. The method according to claim 1, characterized in that The minimization model includes a negative logarithmic maximum likelihood term, a non-negative concentration graph term, a non-negative attenuated sinusoidal graph term and a concentration constraint term.

3. The method according to claim 2, characterized in that In the case where the concentration map is greater than or equal to zero, the function value of the non-negative concentration map item is equal to zero; In the case where the decaying sinusoidal graph is greater than or equal to zero, the function value of the non-negative decaying sinusoidal graph term is equal to zero; When the sum of the tracer concentrations corresponding to all the concentration maps is equal to the total concentration, the function value of the concentration constraint term is equal to zero, otherwise it is infinite.

4. The method according to claim 1, characterized in that: During the alternating update of the concentration graph and the decay sine graph: While keeping the attenuation sinusoidal graph unchanged and completing the updating of the concentration graph, based on the principle that the sum of the components in the concentration graph remains unchanged, the relative sizes of the components in the updated concentration graph are adjusted to update the concentration graph again; While keeping the concentration map unchanged and completing the update of the attenuation sinusoidal map, based on the non-negative principle of each element in the attenuation sinusoidal map, each negative element in the updated attenuation sinusoidal map is set to zero to update the attenuation sinusoidal map again.

5. The method according to claim 1, characterized in that The measurement data model is configured as follows: The first mean of the Poisson random variable corresponding to the number of photons collected by the t-th window of each response line is equal to the product of the second mean of the unattenuated measurement data of each response line in the t-th window and the exponentially decaying attenuated sinusoid corresponding to each response line; The second mean is equal to the sum of the measurement data corresponding to each pixel block under the tth window, and the measurement data corresponding to each pixel block is equal to the product of the tracer concentration value in each pixel block and the probability that the photon generated by each pixel block is detected by the tth window of each response line.

6. The method according to claim 1, characterized in that Before acquiring the measurement data for reconstructing the PET image of the target object and the total concentration of the tracer corresponding to the measurement data, the method further includes: In response to a confirmation operation on a configuration parameter of the parameter configuration interface, taking the configuration parameter as a corresponding termination condition; The configuration parameter is at least one of a first boundary condition, a second boundary condition, and a maximum number of iterations, the first boundary condition corresponds to a first image evaluation index of the concentration map, and the second boundary condition corresponds to a second image evaluation index of the attenuation sinusoidal map.

7. The method according to claim 1, characterized in that The alternate updating of the concentration map and the attenuation sinusoidal map until the iteration ends to obtain the target concentration map and the target attenuation sinusoidal map includes: Alternately updating the concentration map and the attenuation sinusoidal map until the updated concentration map, the attenuation sinusoidal map or the number of iterations reaches a corresponding termination condition, thereby terminating the iteration; The concentration map and attenuation sinusoidal map determined in the last iteration round are used as the target concentration map and target attenuation sinusoidal map.

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 PET image reconstruction method with self-attenuation correction described in any one of claims 1-7.

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 PET image reconstruction method with self-attenuation correction described in any one of claims 1-7 when executed.

10. A computer program product, characterized in that The computer program product comprises a computer program, which, when executed by a processor, implements the PET image reconstruction method with self-attenuation correction according to any one of claims 1-7.

Images