Radioactive tracer agent distribution quantification optimization method and related equipment

Abstract

The invention belongs to the technical field of nuclear medicine imaging, and discloses a radioactive tracer agent distribution quantification optimization method and related equipment, and the method comprises the steps: obtaining scanning bed deformation information, moving track information and physiological motion dynamic information of a patient, and obtaining a radioactive tracer agent distribution quantification optimization result based on the scanning bed deformation information, the moving track information and the physiological motion dynamic information; the method comprises the steps of constructing a multi-source error integrated influence model, performing radioactive tracer distribution reconstruction on original detection data through an OSEM algorithm based on the multi-source error integrated influence model to obtain real distribution information of a radioactive tracer, and performing calculation according to the real distribution information to obtain a standardized uptake value. By means of the method, the accuracy of the radioactive tracer agent distribution quantification result is improved.

Description

Technical Field

[0001] This application relates to the technical field of nuclear medicine imaging, and more specifically, to a method and related equipment for the quantitative optimization of the distribution of radioactive tracers. Background Technology

[0002] In modern medical practice, nuclear medicine imaging technology, especially positron emission tomography / computed tomography (PET / CT) systems, has become a key tool for tumor diagnosis, staging, and treatment efficacy evaluation. The core of this imaging technology lies in accurately measuring the distribution of radioactive tracers within the body to assess the metabolic activity of lesions, which is crucial for disease diagnosis, staging, and evaluating treatment effectiveness.

[0003] Existing methods for quantifying the distribution of radiotracers typically calculate the standardized uptake value (SUV value) on a static, registered, and attenuation-corrected image. This approach assumes that the corrected image accurately reflects the true distribution of the tracer. However, this method ignores errors arising from the interaction of various complex factors and cannot effectively distinguish and compensate for these errors of different natures. Specifically, these errors include quasi-static spatial registration bias caused by the physical characteristics of the device and individual patient differences, as well as motion blur caused by physiological activities (such as breathing and heartbeat). These two types of errors overlap, making the final quantification result ambiguous. For example, when a physician observes a low standardized uptake value, it is impossible to clearly determine whether this is due to the tumor's true low metabolic activity or an artifact caused by insufficient attenuation correction combined with respiratory motion blur. This inherent ambiguity severely affects the clinical reliability of the quantification results, potentially leading to misdiagnosis or incorrect assessment of treatment effectiveness. Therefore, there is an urgent need for a method that can more accurately quantify the distribution of radiotracers to improve the accuracy and reliability of diagnosis.

[0004] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0005] The purpose of this application is to provide a method and related equipment for optimizing the distribution quantification of radiotracers. This method utilizes a multi-source error integrated influence model to reconstruct the distribution of radiotracers from the original detection data, thereby obtaining the true distribution information of the radiotracers. Furthermore, standardized uptake values ​​are calculated. This solves the technical problem in existing nuclear medicine imaging techniques where radiotracer distribution quantification methods neglect errors caused by the interaction of multiple complex factors when calculating standardized uptake values, failing to effectively distinguish and compensate for these errors of different natures, leading to unclear quantification results and affecting clinical reliability. This method improves the accuracy and clinical reliability of radiotracer distribution quantification results, enabling physicians to more accurately determine the metabolic activity of lesions, avoiding misdiagnosis or incorrect assessment of treatment effects. Therefore, it provides a more reliable basis for tumor diagnosis, staging, and treatment effect evaluation, and has significant clinical application value.

[0006] Firstly, this application provides a method for optimizing the distribution quantization of radioactive tracers, used to optimize the distribution quantization results of radioactive tracers, including the following steps: Acquire patient scanning bed deformation information, movement trajectory information, and physiological motion dynamic information; Based on the scanning bed deformation information, the movement trajectory information, and the physiological motion dynamic information, a multi-source error integrated influence model is constructed. Using the OSEM algorithm and based on the multi-source error integrated influence model, the distribution of radioactive tracers is reconstructed from the original detection data to obtain the true distribution information of radioactive tracers; Based on the actual distribution information, the standardized uptake value is calculated.

[0007] The radiotracer distribution quantification optimization method provided in this application can optimize the distribution quantification results of radiotracers. By using the multi-source error integrated influence model to reconstruct the radiotracer distribution from the original detection data, the true distribution information of the radiotracers can be obtained. Furthermore, the standardized uptake value can be calculated, which improves the accuracy and clinical reliability of the radiotracer distribution quantification results. This allows doctors to more accurately judge the metabolic activity of lesions, avoid misdiagnosis or incorrect assessment of treatment effects, and thus provide a more reliable basis for tumor diagnosis, staging, and treatment effect evaluation, with significant clinical application value.

[0008] Optionally, information on scanning bed deformation, movement trajectory, and physiological motion dynamics can be acquired, including: After the patient lies on the scanning bed, the geometry of the scanning bed is identified to obtain information on the patient's scanning bed deformation. The system uses multiple preset detection windows to detect the patient's movement information within key location areas in order to obtain the patient's movement trajectory information. By continuously acquiring scan data of the patient in multiple key locations, dynamic information about the patient's physiological movements can be obtained.

[0009] Optionally, based on the scanning bed deformation information, the movement trajectory information, and the physiological motion dynamic information, a multi-source error integrated influence model is constructed, including: Based on the scanning bed deformation information, determine the baseline offset information for each spatial location within the imaging area; By combining the baseline offset information with the movement trajectory information, the overall drift information of the spatial position at the corresponding data acquisition time point is obtained; By combining the overall drift information with the physiological motion dynamic information, the displacement influence information of the spatial position at the data acquisition time point is obtained; Based on the displacement influence information, a spatiotemporal mapping is established to characterize how the real radioactive source is presented in the original detection data, resulting in a multi-source error integrated influence model.

[0010] The radioactive tracer distribution quantification optimization method provided in this application can optimize the distribution quantification results of radioactive tracers. By integrating errors from different sources (scanning bed deformation, micro-movement, physiological motion) in a progressive manner, from baseline offset information to overall drift, and then to displacement influence information, a spatiotemporal mapping model that can accurately reflect the way the real radioactive source is presented in the detection data is finally constructed. This achieves refined modeling and integrated processing of multi-source errors, significantly improving the accuracy of error compensation.

[0011] Optionally, using the OSEM algorithm and based on the integrated multi-source error impact model, the distribution of radioactive tracers is reconstructed from the original detection data to obtain the true distribution information of the radioactive tracers, including: The initial radioactive tracer distribution assessment image of the original detection data was obtained by using the OSEM algorithm; Based on the initial radioactive tracer distribution assessment image and the integrated multi-source error impact model, the expected detection data is simulated and generated. Based on the difference between the expected detection data and the original detection data, the initial radioactive tracer distribution assessment image is adjusted to obtain the true distribution information of the radioactive tracer.

[0012] The radioactive tracer distribution quantification optimization method provided in this application can optimize the distribution quantification results of radioactive tracers. It uses the OSEM algorithm for iterative optimization and combines it with a multi-source error integrated influence model. By comparing the difference between the simulated expected detection data and the original detection data, the initial evaluation image is gradually corrected. This can effectively recover the true distribution information of radioactive tracers from the original data affected by errors, and significantly improve the accuracy and reliability of the reconstruction results.

[0013] Optionally, based on the difference between the expected detection data and the original detection data, the initial radioactive tracer distribution assessment image is adjusted to obtain the true distribution information of the radioactive tracer, including: The difference between the expected detection data and the original detection data is calculated. Based on the difference, the initial radioactive tracer distribution assessment image is adjusted to obtain the adjusted radioactive tracer distribution assessment image; Determine whether the adjusted radioactive tracer distribution assessment image meets the preset convergence condition; if yes, then determine the adjusted radioactive tracer distribution assessment image as the true distribution information of the radioactive tracer; if no, return to the step of calculating the initial radioactive tracer distribution assessment image, so that the adjusted radioactive tracer distribution assessment image, which is adjusted again based on the recalculated difference, meets the preset convergence condition.

[0014] Optionally, based on the true distribution information, a standardized uptake value is calculated, including: Based on the actual distribution information and combined with the preset standardized intake value calculation formula, the initial standardized intake value is calculated. Based on the actual distribution information, the confidence interval corresponding to the initial standardized intake value is calculated; The initial standardized ingestion value is optimized using the confidence interval to obtain a standardized ingestion value.

[0015] Optionally, based on the true distribution information, the confidence interval corresponding to the initial standardized ingestion value is calculated, including: Based on the true distribution information and using the covariance matrix, the confidence interval corresponding to the initial standardized ingestion value is calculated. Alternatively, the Monte Carlo method can be used to calculate the confidence interval corresponding to the initial standardized ingestion value based on the true distribution information.

[0016] Secondly, this application provides a radioactive tracer distribution quantization optimization device for optimizing the distribution quantization results of radioactive tracers, including: The acquisition module is used to acquire information on the patient's scanning bed deformation, movement trajectory, and physiological motion dynamics. The construction module is used to construct a multi-source error integrated influence model based on the scanning bed deformation information, the movement trajectory information, and the physiological motion dynamic information; The reconstruction module is used to reconstruct the distribution of radioactive tracers from the original detection data using the OSEM algorithm and based on the multi-source error integrated influence model, so as to obtain the true distribution information of the radioactive tracers. The calculation module is used to calculate the standardized uptake value based on the actual distribution information.

[0017] This radiotracer distribution quantification optimization device utilizes a multi-source error integrated influence model to reconstruct the radiotracer distribution from the original detection data, thereby obtaining the true distribution information of the radiotracer and further calculating the standardized uptake value. This improves the accuracy and clinical reliability of the radiotracer distribution quantification results, enabling doctors to more accurately determine the metabolic activity of lesions and avoid misdiagnosis or incorrect assessment of treatment effects. As a result, it provides a more reliable basis for tumor diagnosis, staging, and treatment effect evaluation, and has significant clinical application value.

[0018] Thirdly, this application provides an electronic device including a processor and a memory, the memory storing a computer program executable by the processor, wherein when the processor executes the computer program, it runs the steps in the radioactive tracer distribution quantification optimization method described above.

[0019] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps in the radioactive tracer distribution quantization optimization method described above.

[0020] Beneficial effects: The radiotracer distribution quantification optimization method and related equipment provided in this application utilize the multi-source error integrated influence model to reconstruct the radiotracer distribution from the original detection data, thereby obtaining the true distribution information of the radiotracer and further calculating the standardized uptake value. This improves the accuracy and clinical reliability of the radiotracer distribution quantification results, enabling doctors to more accurately determine the metabolic activity of lesions and avoid misdiagnosis or incorrect assessment of treatment effects. Thus, it provides a more reliable basis for tumor diagnosis, staging, and treatment effect evaluation, and has significant clinical application value. Attached Figure Description

[0021] Figure 1 A flowchart of the radioactive tracer distribution quantification optimization method provided in the embodiments of this application.

[0022] Figure 2 This is a schematic diagram of the structure of the radioactive tracer distribution quantification optimization device provided in the embodiments of this application.

[0023] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0024] Labeling Explanation: 1. Acquisition Module; 2. Construction Module; 3. Reconstruction Module; 4. Calculation Module; 301. Processor; 302. Memory; 303. Communication Bus. Detailed Implementation

[0025] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0026] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0027] Please refer to Figure 1 , Figure 1 This application discloses a method for optimizing the distribution quantization of radioactive tracers, as described in some embodiments, for optimizing the distribution quantization results of radioactive tracers, including: Step S101: Obtain the patient's scanning bed deformation information, movement trajectory information, and physiological motion dynamic information; Step S102: Based on the scanning bed deformation information, movement trajectory information, and physiological motion dynamic information, construct a multi-source error integrated influence model; Step S103: Using the OSEM algorithm and based on the multi-source error integrated influence model, the distribution of radioactive tracers is reconstructed from the original detection data to obtain the true distribution information of the radioactive tracers. Step S104: Calculate the standardized uptake value based on the actual distribution information.

[0028] This method for optimizing the distribution quantification of radiotracers utilizes a multi-source error integrated influence model to reconstruct the distribution of radiotracers from the original detection data, thereby obtaining the true distribution information of radiotracers. Furthermore, standardized uptake values ​​are calculated, improving the accuracy and clinical reliability of the radiotracer distribution quantification results. This allows physicians to more accurately assess the metabolic activity of lesions, avoiding misdiagnosis or incorrect evaluation of treatment effectiveness. Consequently, it provides a more reliable basis for tumor diagnosis, staging, and treatment efficacy evaluation, demonstrating significant clinical application value.

[0029] Specifically, in step S101, the scanning bed deformation information, movement trajectory information, and physiological motion dynamic information are acquired, including: After the patient lies on the scanning bed, the geometry of the scanning bed is identified to obtain information on the patient's scanning bed deformation. The system uses multiple preset detection windows to detect the patient's movement information within key location areas in order to obtain the patient's movement trajectory information. By continuously acquiring scan data of the patient in multiple key locations, dynamic information about the patient's physiological movements can be obtained.

[0030] In step S101, after the patient lies on the scanning bed, the surface contour of the scanning bed is precisely measured using devices such as a 3D scanner, structured light sensor, or pressure sensor array, thereby acquiring deformation data of the scanning bed caused by the patient's weight and posture. This deformation information characterizes the deviation of the scanning bed from its ideal geometry under actual use, and its purpose is to provide basic spatial deformation data for the subsequent construction of a multi-source error integrated influence model. For example, if the center of the bed board sinks by 2 millimeters, this displacement vector field will show a downward displacement in the corresponding area.

[0031] Multiple preset detection windows are used to detect the patient's movement within key areas to obtain the patient's movement trajectory information. Specifically, these detection windows can be configured on key parts of the patient's body, such as the head, chest, abdomen, or limbs. High-precision optical sensors, infrared sensors, or ultrasonic sensors, whether non-contact or contact sensors, are used to monitor minute displacements in these areas in real time. Movement trajectory information refers to the spatial changes in the patient's position caused by involuntary or voluntary slight movements during the scanning process. The purpose is to capture and quantify these subtle movements that significantly affect imaging accuracy.

[0032] By continuously acquiring scan data of patients at multiple key locations, dynamic physiological motion information of the patients can be obtained. This dynamic physiological motion information refers to the periodic or non-periodic movements caused by physiological activities such as breathing, heartbeat, and muscle tremors during the scanning process. For example, the patient's physiological activities can be continuously monitored at multiple key locations using respiratory sensors, electrocardiogram (ECG) devices, or real-time image sequence analysis, and converted into quantifiable motion data. The aim is to comprehensively capture and characterize the impact of the patient's physiological movements on the quantification of tracer distribution.

[0033] Specifically, in step S102, based on the scanning bed deformation information, movement trajectory information, and physiological motion dynamic information, a multi-source error integrated influence model is constructed, including: Based on the scanning bed deformation information, determine the baseline offset information for each spatial location within the imaging area; By combining baseline offset information with movement trajectory information, the overall drift information of spatial position at the corresponding data acquisition time point is obtained; By combining overall drift information with physiological motion dynamic information, we can obtain information on the displacement influence of spatial position at the data acquisition time point. Based on displacement influence information, a spatiotemporal mapping is established to characterize how the real radioactive source is presented in the original detection data, resulting in a multi-source error integrated influence model.

[0034] In step S102, based on the scanning bed deformation information, the baseline offset information for each spatial location within the imaging area is determined, i.e., x' = x + T. bed (x), x' represents the baseline offset information, x represents the actual transmission position, and T bed (x) represents the scanning bed deformation information. Baseline offset information refers to the fixed or slowly varying displacement of each spatial location within the imaging area relative to its ideal location due to scanning bed deformation. This step aims to quantify the fundamental impact of scanning bed deformation on the measurement of radioactive tracer distribution within the imaging area.

[0035] The baseline offset information is combined with the movement trajectory information, i.e., added together, to obtain the overall drift information of the spatial position at the corresponding data acquisition time point, i.e., x''=x'+T patient (x'), where x'' represents the overall drift information, and T patient (x') represents the movement position corresponding to point x' in the movement trajectory information, i.e., T. patient (x')=T patient (x+T bed (x)). Among them, the overall drift information takes into account the baseline offset information caused by the deformation of the scanning bed and the slight movement of the patient during the scanning process, thus reflecting the actual spatial displacement of the radiation source more comprehensively.

[0036] By combining the overall drift information with the physiological motion dynamic information (i.e., adding them together), we obtain the displacement influence information of spatial position at the data acquisition time point, x'''=x''+T physio (x'',t), where x''' represents displacement influence information, and T... physio (x'',t) represents the physiological movement position of point x'' at time t, i.e., T. physio (x'',t)=T physio (x'+T patient (x'),t)=T physio (x+T bed (x)+T patient (x+T bed (x)), t), where the displacement effect information further incorporates the dynamic displacement caused by the patient's physiological movements such as breathing and heartbeat, making the estimation of the actual location of the radiation source more accurate and complete.

[0037] Based on displacement influence information, a spatiotemporal mapping is established to characterize how the real radioactive source is presented in the raw detection data, resulting in a multi-source error integrated influence model. The spatiotemporal mapping is a mathematical model that describes how the distribution of the real radioactive source at different time points and spatial locations is transformed into raw detection data through the detection system, taking into account the combined influence of all the aforementioned error sources. This model can integrate various error factors into a unified framework, providing a precise correction basis for subsequent reconstruction of radioactive tracer distribution.

[0038] The multi-source error integrated impact model is specifically as follows: PSF(y,t|x)=P intrinsic ·(yT total (x,t))·A(x,t); Where PSF(y,t|x) is the probability density of a scan event recorded by the detector at position y at time t, given a real radiation source located at x; PSF is the point spread function; y is the detected position; t is time; x is the real emission position; P intrinsic The blurring is caused by the inherent resolution of the PET scanner itself; T total (x,t) is a comprehensive deformation field that combines the effects of scanning bed deformation, minute movements, and physiological motion on the displacement of point x at time t; A(x,t) is an attenuation correction factor applied to point x at time t, considering all spatial biases, and can be obtained experimentally. Where T... total (x,t) = x''' - x, i.e., T total (x,t)=x+T bed (x)+T patient (x')+Tphysio (x'',t)-x=T bed (x)+T patient (x+T bed (x))+T physio (x+T bed (x)+T patient (x+T bed (x)), t), where T patient (x+T bed (x) is the point x'.

[0039] Specifically, in step S103, the distribution of radioactive tracers is reconstructed from the original detection data using the OSEM algorithm based on the multi-source error integrated influence model, to obtain the true distribution information of the radioactive tracers, including: The initial radioactive tracer distribution assessment image of the original detection data was obtained by using the OSEM algorithm; Based on the initial radioactive tracer distribution assessment image and the integrated multi-source error impact model, the expected detection data is simulated and generated. Based on the difference between the expected detection data and the original detection data, the initial radioactive tracer distribution assessment image is adjusted to obtain the true distribution information of the radioactive tracer.

[0040] In step S103, an initial evaluation is performed by introducing the OSEM algorithm (Ordered Subset Expectation-Maximization), and simulation and correction are carried out in conjunction with a multi-source error integrated influence model. This effectively removes various error factors contained in the original detection data, thereby more accurately reconstructing the true distribution of the radioactive tracer. The OSEM algorithm provides an iterative framework, specifically employing a standard OSEM reconstruction process. Without considering multi-source errors, one or several iterations of reconstruction (i.e., one or more CT image reconstructions) are performed on the original detection data to obtain a preliminary tracer distribution image. The OSEM algorithm for obtaining the initial radioactive tracer distribution evaluation image is existing technology and will not be detailed here.

[0041] Based on the initial radioactive tracer distribution assessment image and the integrated multi-source error impact model, the expected detection data can be simulated and generated. This simulation process aims to predict what signal the detector should receive if the radioactive tracer distribution is as shown in the initial assessment image, considering multiple error sources such as scanning bed deformation, movement trajectory information, and physiological motion dynamics. The integrated multi-source error impact model plays a crucial role in this process, mapping the actual distribution of the radioactive source onto the raw detection data, thereby accurately simulating the detection data affected by various errors.

[0042] By comparing simulated data with actual data and making iterative adjustments, it is possible to gradually converge to a more accurate distribution of radioactive tracers.

[0043] Specifically, in step S103, the initial radioactive tracer distribution assessment image is adjusted based on the difference between the expected detection data and the original detection data to obtain the true distribution information of the radioactive tracer, including: The difference between the expected detection data and the original detection data is calculated. Based on the difference, the initial radioactive tracer distribution assessment image is adjusted to obtain the adjusted radioactive tracer distribution assessment image; Determine whether the adjusted radioactive tracer distribution assessment image meets the preset convergence condition; if yes, then determine the adjusted radioactive tracer distribution assessment image as the true distribution information of the radioactive tracer; if no, return to the step of calculating the initial radioactive tracer distribution assessment image so that the adjusted radioactive tracer distribution assessment image, which is adjusted again based on the recalculated difference, meets the preset convergence condition.

[0044] In step S103, the difference between the expected detection data and the original detection data is calculated. This involves using mathematical operations to quantify the inconsistency between the simulated data and the actual acquired data. This difference indicates the deviation between the current radioactive tracer distribution assessment image and the actual distribution, and its purpose is to provide a basis for direction and magnitude in subsequent image adjustments.

[0045] Adjusting the initial radioactive tracer distribution assessment image based on the difference refers to using this difference information to correct the current radioactive tracer distribution assessment image according to a specific iterative update rule. For example, gradient descent, expectation-maximization (EM) algorithms, or other optimization algorithms can be used to gradually approximate the true distribution. The goal is to gradually reduce the difference between the simulated data and the original detection data through iterative optimization.

[0046] Determining whether the adjusted radioactive tracer distribution assessment image meets the preset convergence criteria involves setting one or more standards to decide when the iterative process stops. For example, the algorithm can be considered converged when the change in image pixel values ​​is less than a certain threshold, the number of iterations reaches an upper limit, or the algorithm reaches its optimum through statistical criteria such as maximizing the Poisson likelihood function. The purpose is to ensure the stability and accuracy of the reconstruction results and avoid unnecessary computational overhead. If the convergence criteria are met, the radioactive tracer distribution assessment image at the time of convergence is determined to be the true distribution information of the radioactive tracer. If the convergence criteria are not met, the algorithm returns to the step of calculating the initial radioactive tracer distribution assessment image, so that the adjusted radioactive tracer distribution assessment image, based on the recalculated difference, meets the preset convergence criteria.

[0047] This scheme employs an iterative optimization strategy. In each iteration, new expected detection data is simulated based on the latest evaluation image and compared with the original detection data. The evaluation image is then adjusted again based on the new difference until the preset convergence condition is met. The aim is to gradually eliminate errors through continuous feedback and correction, ultimately obtaining high-precision information on the true distribution of radioactive tracers.

[0048] Specifically, in step S104, the standardized uptake value is calculated based on the actual distribution information, including: Based on the actual distribution information and combined with the preset standardized intake value calculation formula, the initial standardized intake value is calculated. Based on the real distribution information, the confidence intervals corresponding to the initial standardized intake values ​​are calculated; The initial standardized intake value is optimized using confidence intervals to obtain the standardized intake value.

[0049] In step S104, an initial standardized uptake value is calculated based on the actual distribution information and a preset standardized uptake value calculation formula. Specifically, the standardized uptake value calculation formula is: SUV = (radioactive concentration in the lesion area / injection dose) * patient weight. The radioactive concentration in the lesion area is the average radioactive concentration obtained by segmenting the lesion area (e.g., based on a threshold or region growing algorithm) and calculating its average value from the actual distribution information of the radioactive tracer. The injection dose is the amount of radioactive tracer injected.

[0050] In step S104, based on the true distribution information, the confidence intervals corresponding to the initial standardized intake values ​​are calculated, including: Based on the true distribution information, the confidence intervals corresponding to the initial standardized intake values ​​are calculated using the covariance matrix. Alternatively, the Monte Carlo method can be used to calculate the confidence interval corresponding to the initial standardized intake value based on the true distribution information.

[0051] In step S104, a confidence interval, in statistics, refers to the range of values ​​within which an unknown parameter is estimated, containing the true parameter value at a certain confidence level. Here, calculating the confidence interval aims to quantify the uncertainty or variability of the initial standardized intake values, providing a data foundation for subsequent optimization.

[0052] The covariance matrix is ​​a statistical tool used to quantify the variability and relationships among multiple random variables. In the context of radioactive tracer distribution reconstruction, the true distribution information is typically presented in the form of image pixels or voxels, and the radioactivity value of each pixel or voxel has a certain degree of statistical uncertainty. By calculating the covariance matrix of these pixel or voxel activity values, noise, artifacts, and correlations between activity values ​​in different regions introduced during the reconstruction process can be captured. Based on this covariance matrix, the statistical uncertainty of the initial standardized uptake value (SUV) can be further derived, thereby constructing its confidence interval. The aim is to provide a direct method based on statistical principles to quantify the uncertainty of the SUV.

[0053] Monte Carlo simulation is a method for solving complex problems using random sampling. When calculating the confidence intervals corresponding to the initial standardized uptake values, Monte Carlo simulation can be used to simulate the random errors and uncertainties present in the real distribution information. Specifically, based on the characteristics of the reconstruction algorithm and the statistical properties of the probe data, the real distribution information can be randomly perturbed or resampled multiple times, and the initial standardized uptake values ​​can be recalculated after each perturbation. By performing statistical analysis on a large number of simulation results, such as calculating the mean and standard deviation of the distribution of the initial standardized uptake values ​​obtained from the simulation, the confidence interval can be obtained. Its purpose is to provide a flexible and robust method, especially suitable for estimating the uncertainty of SUV through simulation when analytical solutions are difficult to obtain or the data distribution is complex.

[0054] On the one hand, calculating confidence intervals using the covariance matrix directly utilizes the statistical properties of the reconstructed image to quantify the uncertainty of each pixel or voxel and their interrelationships, thus accurately reflecting the uncertainty of the initial standardized ingestion value. This method, based on rigorous statistical principles, provides a theoretically reliable confidence interval. On the other hand, using Monte Carlo simulation to calculate confidence intervals, through extensive random sampling and repeated calculations, can simulate the propagation of various random errors in real-world distribution information, thereby robustly estimating the uncertainty of the initial standardized ingestion value without relying on specific distribution assumptions. Both methods provide a statistically significant range for subsequent optimization of the initial standardized ingestion value, ensuring the scientific rigor and accuracy of the optimization process.

[0055] In step S104, the initial standardized intake value is optimized using confidence intervals to obtain the final standardized intake value. In practical applications, the optimization process involves correcting or adjusting the initial calculation results based on the uncertainty range reflected by the confidence interval. For example, weighted averaging, Bayesian inference, or other statistical methods can be used to make the final standardized intake value more statistically significant and clinically reliable.

[0056] For example, assuming that after obtaining the true distribution information of the radioactive tracer, the initial standardized uptake value for a patient is calculated as 2.5 according to a pre-defined formula. Then, based on the true distribution information, the confidence interval corresponding to this initial standardized uptake value of 2.5 is calculated using the covariance matrix or the Monte Carlo method. This confidence interval is assumed to be [2.3, 2.7] with a confidence level of 95%. Finally, the initial standardized uptake value of 2.5 is optimized using this confidence interval. For example, a Bayesian method can be used, treating the initial value as prior information and combining it with the uncertainty reflected by the confidence interval, to iteratively calculate a more statistically significant standardized uptake value, such as 2.48, which is within the confidence interval and has higher reliability.

[0057] As shown above, this method for optimizing the quantification of radiotracer distribution acquires the patient's scanning bed deformation information, movement trajectory information, and physiological motion dynamic information. Based on these information, a multi-source error integrated influence model is constructed. Using the OSEM algorithm and based on this model, the radiotracer distribution is reconstructed from the original detection data to obtain the true distribution information of the radiotracer. Standardized uptake values ​​are then calculated based on this true distribution information. Therefore, by using this multi-source error integrated influence model to reconstruct the radiotracer distribution from the original detection data, the true distribution information of the radiotracer is obtained, and the standardized uptake values ​​are further calculated. This improves the accuracy and clinical reliability of the quantification results of radiotracer distribution, enabling doctors to more accurately determine the metabolic activity of lesions and avoid misdiagnosis or incorrect assessment of treatment effects. This provides a more reliable basis for tumor diagnosis, staging, and treatment effect evaluation, and has significant clinical application value.

[0058] refer to Figure 2 This application provides a radioactive tracer distribution quantization optimization device for optimizing the distribution quantization results of radioactive tracers, including: Acquisition module 1 is used to acquire the patient's scanning bed deformation information, movement trajectory information, and physiological motion dynamic information; Module 2 is used to construct a multi-source error integrated influence model based on scanning bed deformation information, movement trajectory information, and physiological motion dynamic information; Reconstruction module 3 is used to reconstruct the distribution of radioactive tracers from the original detection data using the OSEM algorithm and based on the multi-source error integrated influence model, so as to obtain the true distribution information of radioactive tracers. Calculation module 4 is used to calculate the standardized uptake value based on the actual distribution information.

[0059] This radiotracer distribution quantification optimization device utilizes a multi-source error integrated influence model to reconstruct the radiotracer distribution from the original detection data, thereby obtaining the true distribution information of the radiotracer and further calculating the standardized uptake value. This improves the accuracy and clinical reliability of the radiotracer distribution quantification results, enabling doctors to more accurately determine the metabolic activity of lesions and avoid misdiagnosis or incorrect assessment of treatment effects. As a result, it provides a more reliable basis for tumor diagnosis, staging, and treatment effect evaluation, and has significant clinical application value.

[0060] Specifically, when acquiring scanning bed deformation information, movement trajectory information, and physiological motion dynamic information, module 1 executes the following: After the patient lies on the scanning bed, the geometry of the scanning bed is identified to obtain information on the patient's scanning bed deformation. The system uses multiple preset detection windows to detect the patient's movement information within key location areas in order to obtain the patient's movement trajectory information. By continuously acquiring scan data of the patient in multiple key locations, dynamic information about the patient's physiological movements can be obtained.

[0061] During execution, module 1 uses devices such as a 3D scanner, structured light sensor, or pressure sensor array to precisely measure the surface contour of the scanning bed after the patient lies down, thereby acquiring deformation data of the scanning bed caused by the patient's weight and posture. This deformation information characterizes the deviation of the scanning bed from its ideal geometry under actual use, aiming to provide basic spatial deformation data for the subsequent construction of a multi-source error integrated influence model. For example, if the center of the bed sinks by 2 millimeters, this displacement vector field will show a downward displacement in the corresponding area.

[0062] Multiple preset detection windows are used to detect the patient's movement within key areas to obtain the patient's movement trajectory information. Specifically, these detection windows can be configured on key parts of the patient's body, such as the head, chest, abdomen, or limbs. High-precision optical sensors, infrared sensors, or ultrasonic sensors, whether non-contact or contact sensors, are used to monitor minute displacements in these areas in real time. Movement trajectory information refers to the spatial changes in the patient's position caused by involuntary or voluntary slight movements during the scanning process. The purpose is to capture and quantify these subtle movements that significantly affect imaging accuracy.

[0063] By continuously acquiring scan data of patients at multiple key locations, dynamic physiological motion information of the patients can be obtained. This dynamic physiological motion information refers to the periodic or non-periodic movements caused by physiological activities such as breathing, heartbeat, and muscle tremors during the scanning process. For example, the patient's physiological activities can be continuously monitored at multiple key locations using respiratory sensors, electrocardiogram (ECG) devices, or real-time image sequence analysis, and converted into quantifiable motion data. The aim is to comprehensively capture and characterize the impact of the patient's physiological movements on the quantification of tracer distribution.

[0064] Specifically, when constructing the multi-source error integrated influence model based on scanning bed deformation information, movement trajectory information, and physiological motion dynamic information, module 2 executes the following: Based on the scanning bed deformation information, determine the baseline offset information for each spatial location within the imaging area; By combining baseline offset information with movement trajectory information, the overall drift information of spatial position at the corresponding data acquisition time point is obtained; By combining overall drift information with physiological motion dynamic information, we can obtain information on the displacement influence of spatial position at the data acquisition time point. Based on displacement influence information, a spatiotemporal mapping is established to characterize how the real radioactive source is presented in the original detection data, resulting in a multi-source error integrated influence model.

[0065] When module 2 is executed, it determines the baseline offset information for each spatial location within the imaging area based on the scanning bed deformation information, i.e., x' = x + T. bed (x), x' represents the baseline offset information, x represents the actual transmission position, and T bed (x) represents the scanning bed deformation information. Baseline offset information refers to the fixed or slowly varying displacement of each spatial location within the imaging area relative to its ideal location due to scanning bed deformation. This step aims to quantify the fundamental impact of scanning bed deformation on the measurement of radioactive tracer distribution within the imaging area.

[0066] The baseline offset information is combined with the movement trajectory information, i.e., added together, to obtain the overall drift information of the spatial position at the corresponding data acquisition time point, i.e., x''=x'+T patient (x'), where x'' represents the overall drift information, and T patient (x') represents the movement position corresponding to point x' in the movement trajectory information, i.e., T. patient (x')=T patient (x+T bed (x)). Among them, the overall drift information takes into account the baseline offset information caused by the deformation of the scanning bed and the slight movement of the patient during the scanning process, thus reflecting the actual spatial displacement of the radiation source more comprehensively.

[0067] By combining the overall drift information with the physiological motion dynamic information (i.e., adding them together), we obtain the displacement influence information of spatial position at the data acquisition time point, x'''=x''+T physio (x'',t), where x''' represents displacement influence information, and T... physio (x'',t) represents the physiological movement position of point x'' at time t, i.e., T. physio (x'',t)=T physio (x'+T patient (x'),t)=T physio (x+T bed (x)+T patient (x+T bed (x)), t), where the displacement effect information further incorporates the dynamic displacement caused by the patient's physiological movements such as breathing and heartbeat, making the estimation of the actual location of the radiation source more accurate and complete.

[0068] Based on displacement influence information, a spatiotemporal mapping is established to characterize how the real radioactive source is presented in the raw detection data, resulting in a multi-source error integrated influence model. The spatiotemporal mapping is a mathematical model that describes how the distribution of the real radioactive source at different time points and spatial locations is transformed into raw detection data through the detection system, taking into account the combined influence of all the aforementioned error sources. This model can integrate various error factors into a unified framework, providing a precise correction basis for subsequent reconstruction of radioactive tracer distribution.

[0069] The multi-source error integrated impact model is specifically as follows: PSF(y,t|x)=P intrinsic ·(yT total (x,t))·A(x,t); Where PSF(y,t|x) is the probability density of a scan event recorded by the detector at position y at time t, given a real radiation source located at x; PSF is the point spread function; y is the detected position; t is time; x is the real emission position; P intrinsic The blurring is caused by the inherent resolution of the PET scanner itself; T total (x,t) is a comprehensive deformation field that combines the effects of scanning bed deformation, minute movements, and physiological motion on the displacement of point x at time t; A(x,t) is an attenuation correction factor applied to point x at time t, considering all spatial biases, and can be obtained experimentally. Where T... total (x,t) = x''' - x, i.e., T total (x,t)=x+T bed (x)+T patient (x')+Tphysio (x'',t)-x=T bed (x)+T patient (x+T bed (x))+T physio (x+T bed (x)+T patient (x+T bed (x)), t).

[0070] Specifically, when reconstruction module 3 reconstructs the distribution of radioactive tracers from the original detection data using the OSEM algorithm and based on the multi-source error integrated influence model to obtain the true distribution information of the radioactive tracers, it executes the following: The initial radioactive tracer distribution assessment image of the original detection data was obtained by using the OSEM algorithm; Based on the initial radioactive tracer distribution assessment image and the integrated multi-source error impact model, the expected detection data is simulated and generated. Based on the difference between the expected detection data and the original detection data, the initial radioactive tracer distribution assessment image is adjusted to obtain the true distribution information of the radioactive tracer.

[0071] During execution, reconstruction module 3 employs the OSEM algorithm (Ordered Subset Expectation-Maximization) for preliminary evaluation and combines it with a multi-source error integrated influence model for simulation and correction. This effectively removes various error factors from the original detection data, thereby more accurately reconstructing the true distribution of the radioactive tracer. The OSEM algorithm provides an iterative framework, specifically employing a standard OSEM reconstruction process. Without considering multi-source errors, it performs one or several iterations of reconstruction on the original detection data (i.e., one or more CT image reconstructions) to obtain a preliminary tracer distribution image. The acquisition of the initial radioactive tracer distribution evaluation image using the OSEM algorithm is existing technology and will not be detailed here.

[0072] Based on the initial radioactive tracer distribution assessment image and the integrated multi-source error impact model, the expected detection data can be simulated and generated. This simulation process aims to predict what signal the detector should receive if the radioactive tracer distribution is as shown in the initial assessment image, considering multiple error sources such as scanning bed deformation, movement trajectory information, and physiological motion dynamics. The integrated multi-source error impact model plays a crucial role in this process, mapping the actual distribution of the radioactive source onto the raw detection data, thereby accurately simulating the detection data affected by various errors.

[0073] By comparing simulated data with actual data and making iterative adjustments, it is possible to gradually converge to a more accurate distribution of radioactive tracers.

[0074] Specifically, when reconstruction module 3 adjusts the initial radioactive tracer distribution assessment image based on the differences between the expected detection data and the original detection data to obtain the true distribution information of the radioactive tracer, it performs the following: The difference between the expected detection data and the original detection data is calculated. Based on the difference, the initial radioactive tracer distribution assessment image is adjusted to obtain the adjusted radioactive tracer distribution assessment image; Determine whether the adjusted radioactive tracer distribution assessment image meets the preset convergence condition; if yes, then determine the adjusted radioactive tracer distribution assessment image as the true distribution information of the radioactive tracer; if no, return to the step of calculating the initial radioactive tracer distribution assessment image so that the adjusted radioactive tracer distribution assessment image, which is adjusted again based on the recalculated difference, meets the preset convergence condition.

[0075] During execution, Reconstruction Module 3 calculates the difference between the expected detection data and the original detection data. This difference, calculated mathematically, quantifies the inconsistency between the simulated data and the actual acquired data. It serves as an indicator of the deviation between the current radioactive tracer distribution assessment image and the actual distribution, providing direction and magnitude for subsequent image adjustments.

[0076] Adjusting the initial radioactive tracer distribution assessment image based on the difference refers to using this difference information to correct the current radioactive tracer distribution assessment image according to a specific iterative update rule. For example, gradient descent, expectation-maximization (EM) algorithms, or other optimization algorithms can be used to gradually approximate the true distribution. The goal is to gradually reduce the difference between the simulated data and the original detection data through iterative optimization.

[0077] Determining whether the adjusted radioactive tracer distribution assessment image meets the preset convergence criteria involves setting one or more standards to decide when the iterative process stops. For example, the algorithm can be considered converged when the change in image pixel values ​​is less than a certain threshold, the number of iterations reaches an upper limit, or the algorithm reaches its optimum through statistical criteria such as maximizing the Poisson likelihood function. The purpose is to ensure the stability and accuracy of the reconstruction results and avoid unnecessary computational overhead. If the convergence criteria are met, the radioactive tracer distribution assessment image at the time of convergence is determined to be the true distribution information of the radioactive tracer. If the convergence criteria are not met, the algorithm returns to the step of calculating the initial radioactive tracer distribution assessment image, so that the adjusted radioactive tracer distribution assessment image, based on the recalculated difference, meets the preset convergence criteria.

[0078] This scheme employs an iterative optimization strategy. In each iteration, new expected detection data is simulated based on the latest evaluation image and compared with the original detection data. The evaluation image is then adjusted again based on the new difference until the preset convergence condition is met. The aim is to gradually eliminate errors through continuous feedback and correction, ultimately obtaining high-precision information on the true distribution of radioactive tracers.

[0079] Specifically, when the calculation module 4 calculates the standardized ingestion value based on the actual distribution information, it executes the following: Based on the actual distribution information and combined with the preset standardized intake value calculation formula, the initial standardized intake value is calculated. Based on the real distribution information, the confidence intervals corresponding to the initial standardized intake values ​​are calculated; The initial standardized intake value is optimized using confidence intervals to obtain the standardized intake value.

[0080] During execution, calculation module 4 calculates the initial standardized uptake value based on the actual distribution information and a preset standardized uptake value calculation formula. Specifically, the standardized uptake value calculation formula is: SUV = (radioactivity concentration in the lesion area / injection dose) * patient weight. The radioactivity concentration in the lesion area is the average radioactivity concentration obtained by segmenting the lesion area (e.g., based on a threshold or region growing algorithm) and calculating its average value from the actual distribution information of the radioactive tracer. The injection dose is the amount of radioactive tracer injected.

[0081] When calculation module 4 calculates the confidence interval corresponding to the initial standardized ingestion value based on the true distribution information, it performs the following: Based on the true distribution information, the confidence intervals corresponding to the initial standardized intake values ​​are calculated using the covariance matrix. Alternatively, the Monte Carlo method can be used to calculate the confidence interval corresponding to the initial standardized intake value based on the true distribution information.

[0082] When calculation module 4 is executed, the confidence interval, in statistics, refers to the range of values ​​within which an unknown parameter is estimated, containing the true parameter value at a certain confidence level. Here, calculating the confidence interval aims to quantify the uncertainty or variability of the initial standardized ingestion values, providing a data foundation for subsequent optimization.

[0083] The covariance matrix is ​​a statistical tool used to quantify the variability and relationships among multiple random variables. In the context of radioactive tracer distribution reconstruction, the true distribution information is typically presented in the form of image pixels or voxels, and the radioactivity value of each pixel or voxel has a certain degree of statistical uncertainty. By calculating the covariance matrix of these pixel or voxel activity values, noise, artifacts, and correlations between activity values ​​in different regions introduced during the reconstruction process can be captured. Based on this covariance matrix, the statistical uncertainty of the initial standardized uptake value (SUV) can be further derived, thereby constructing its confidence interval. The aim is to provide a direct method based on statistical principles to quantify the uncertainty of the SUV.

[0084] Monte Carlo simulation is a method for solving complex problems using random sampling. When calculating the confidence intervals corresponding to the initial standardized uptake values, Monte Carlo simulation can be used to simulate the random errors and uncertainties present in the real distribution information. Specifically, based on the characteristics of the reconstruction algorithm and the statistical properties of the probe data, the real distribution information can be randomly perturbed or resampled multiple times, and the initial standardized uptake values ​​can be recalculated after each perturbation. By performing statistical analysis on a large number of simulation results, such as calculating the mean and standard deviation of the distribution of the initial standardized uptake values ​​obtained from the simulation, the confidence interval can be obtained. Its purpose is to provide a flexible and robust method, especially suitable for estimating the uncertainty of SUV through simulation when analytical solutions are difficult to obtain or the data distribution is complex.

[0085] On the one hand, calculating confidence intervals using the covariance matrix directly utilizes the statistical properties of the reconstructed image to quantify the uncertainty of each pixel or voxel and their interrelationships, thus accurately reflecting the uncertainty of the initial standardized ingestion value. This method, based on rigorous statistical principles, provides a theoretically reliable confidence interval. On the other hand, using Monte Carlo simulation to calculate confidence intervals, through extensive random sampling and repeated calculations, can simulate the propagation of various random errors in real-world distribution information, thereby robustly estimating the uncertainty of the initial standardized ingestion value without relying on specific distribution assumptions. Both methods provide a statistically significant range for subsequent optimization of the initial standardized ingestion value, ensuring the scientific rigor and accuracy of the optimization process.

[0086] During execution, calculation module 4 optimizes the initial standardized intake value using confidence intervals to obtain the final standardized intake value. In practical applications, the optimization process involves correcting or adjusting the initial calculation results based on the uncertainty range reflected by the confidence interval. For example, weighted averaging, Bayesian inference, or other statistical methods can be used to make the final standardized intake value more statistically significant and clinically reliable.

[0087] For example, assuming that after obtaining the true distribution information of the radioactive tracer, the initial standardized uptake value for a patient is calculated as 2.5 according to a pre-defined formula. Then, based on the true distribution information, the confidence interval corresponding to this initial standardized uptake value of 2.5 is calculated using the covariance matrix or the Monte Carlo method. This confidence interval is assumed to be [2.3, 2.7] with a confidence level of 95%. Finally, the initial standardized uptake value of 2.5 is optimized using this confidence interval. For example, a Bayesian method can be used, treating the initial value as prior information and combining it with the uncertainty reflected by the confidence interval, to iteratively calculate a more statistically significant standardized uptake value, such as 2.48, which is within the confidence interval and has higher reliability.

[0088] As shown above, this radiotracer distribution quantification optimization device acquires the patient's scanning bed deformation information, movement trajectory information, and physiological motion dynamic information. Based on these information, it constructs a multi-source error integrated influence model. Using the OSEM algorithm and based on this model, it reconstructs the radiotracer distribution from the original detection data, obtaining the true distribution information of the radiotracer. Based on this true distribution information, it calculates the standardized uptake value. Therefore, by using this multi-source error integrated influence model to reconstruct the radiotracer distribution from the original detection data, the device obtains the true distribution information of the radiotracer and further calculates the standardized uptake value. This improves the accuracy and clinical reliability of the radiotracer distribution quantification results, enabling doctors to more accurately determine the metabolic activity of lesions, avoiding misdiagnosis or incorrect assessment of treatment effects. This provides a more reliable basis for tumor diagnosis, staging, and treatment effect evaluation, and has significant clinical application value.

[0089] Please refer to Figure 3 , Figure 3This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device includes a processor 301 and a memory 302. The processor 301 and the memory 302 are interconnected and communicate with each other via a communication bus 303 and / or other forms of connection mechanisms (not shown). The memory 302 stores a computer program executable by the processor 301. When the electronic device is running, the processor 301 executes the computer program to perform the radioactive tracer distribution quantification optimization method in any optional implementation of the above embodiments, to achieve the following functions: acquiring the patient's scanning bed deformation information, movement trajectory information, and physiological motion dynamic information; constructing a multi-source error integrated influence model based on the scanning bed deformation information, movement trajectory information, and physiological motion dynamic information; reconstructing the radioactive tracer distribution from the original detection data using the OSEM algorithm and based on the multi-source error integrated influence model to obtain the true distribution information of the radioactive tracer; and calculating the standardized uptake value based on the true distribution information.

[0090] This application provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it performs the radiotracer distribution quantification optimization method in any optional implementation of the above embodiments to achieve the following functions: acquiring the patient's scanning bed deformation information, movement trajectory information, and physiological motion dynamic information; constructing a multi-source error integrated influence model based on the scanning bed deformation information, movement trajectory information, and physiological motion dynamic information; reconstructing the radiotracer distribution from the original detection data using the OSEM algorithm and based on the multi-source error integrated influence model to obtain the true distribution information of the radiotracer; and calculating the standardized uptake value based on the true distribution information. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0091] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0092] Furthermore, the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0093] Furthermore, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0094] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0095] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for optimizing the distribution quantization of radioactive tracers, used to optimize the distribution quantization results of radioactive tracers, characterized in that, Including the following steps: Acquire patient scanning bed deformation information, movement trajectory information, and physiological motion dynamic information; Based on the scanning bed deformation information, the movement trajectory information, and the physiological motion dynamic information, a multi-source error integrated influence model is constructed. Using the OSEM algorithm and based on the multi-source error integrated influence model, the distribution of radioactive tracers is reconstructed from the original detection data to obtain the true distribution information of radioactive tracers; Based on the actual distribution information, the standardized uptake value is calculated.

2. The method for quantitative optimization of radioactive tracer distribution according to claim 1, characterized in that, Acquire scanning bed deformation information, movement trajectory information, and physiological motion dynamic information, including: After the patient lies on the scanning bed, the geometry of the scanning bed is identified to obtain information on the patient's scanning bed deformation. The system detects the patient's movement within key location areas through multiple preset detection windows to obtain the patient's movement trajectory information. By continuously acquiring scans of the patient in multiple key locations, dynamic information about the patient's physiological movements can be obtained.

3. The method for quantitative optimization of radioactive tracer distribution according to claim 1, characterized in that, Based on the scanning bed deformation information, the movement trajectory information, and the physiological motion dynamic information, a multi-source error integrated influence model is constructed, including: Based on the scanning bed deformation information, determine the baseline offset information for each spatial location within the imaging area; By combining the baseline offset information with the movement trajectory information, the overall drift information of the spatial position at the corresponding data acquisition time point is obtained; By combining the overall drift information with the physiological motion dynamic information, the displacement influence information of the spatial position at the data acquisition time point is obtained; Based on the displacement influence information, a spatiotemporal mapping is established to characterize how the real radioactive source is presented in the original detection data, resulting in a multi-source error integrated influence model.

4. The method for quantitative optimization of radioactive tracer distribution according to claim 1, characterized in that, Using the OSEM algorithm and based on the integrated multi-source error impact model, the distribution of radioactive tracers is reconstructed from the original detection data to obtain the true distribution information of the radioactive tracers, including: The initial radioactive tracer distribution assessment image of the original detection data was obtained by using the OSEM algorithm; Based on the initial radioactive tracer distribution assessment image and the integrated multi-source error impact model, the expected detection data is simulated and generated. Based on the difference between the expected detection data and the original detection data, the initial radioactive tracer distribution assessment image is adjusted to obtain the true distribution information of the radioactive tracer.

5. The method for quantitative optimization of radioactive tracer distribution according to claim 4, characterized in that, Based on the difference between the expected detection data and the original detection data, the initial radioactive tracer distribution assessment image is adjusted to obtain the true distribution information of the radioactive tracer, including: The difference between the expected detection data and the original detection data is calculated. Based on the difference, the initial radioactive tracer distribution assessment image is adjusted to obtain the adjusted radioactive tracer distribution assessment image; Determine whether the adjusted radioactive tracer distribution assessment image meets the preset convergence condition; if yes, then determine the adjusted radioactive tracer distribution assessment image as the true distribution information of the radioactive tracer; if no, return to the step of calculating the initial radioactive tracer distribution assessment image, so that the adjusted radioactive tracer distribution assessment image, which is adjusted again based on the recalculated difference, meets the preset convergence condition.

6. The method for quantitative optimization of radioactive tracer distribution according to claim 1, characterized in that, Based on the actual distribution information, the standardized uptake value is calculated, including: Based on the actual distribution information and combined with the preset standardized intake value calculation formula, the initial standardized intake value is calculated. Based on the actual distribution information, the confidence interval corresponding to the initial standardized intake value is calculated; The initial standardized ingestion value is optimized using the confidence interval to obtain a standardized ingestion value.

7. The method for quantitative optimization of radioactive tracer distribution according to claim 6, characterized in that, Based on the actual distribution information, the confidence interval corresponding to the initial standardized ingestion value is calculated, including: Based on the true distribution information and using the covariance matrix, the confidence interval corresponding to the initial standardized ingestion value is calculated. Alternatively, the Monte Carlo method can be used to calculate the confidence interval corresponding to the initial standardized ingestion value based on the true distribution information.

8. A device for optimizing the distribution quantization of radioactive tracers, used to optimize the distribution quantization results of radioactive tracers, characterized in that, include: The acquisition module is used to acquire information on the patient's scanning bed deformation, movement trajectory, and physiological motion dynamics. The construction module is used to construct a multi-source error integrated influence model based on the scanning bed deformation information, the movement trajectory information, and the physiological motion dynamic information; The reconstruction module is used to reconstruct the distribution of radioactive tracers from the original detection data using the OSEM algorithm and based on the multi-source error integrated influence model, so as to obtain the true distribution information of the radioactive tracers. The calculation module is used to calculate the standardized uptake value based on the actual distribution information.

9. An electronic device, characterized in that, It includes a processor and a memory, the memory storing a computer program executable by the processor, which, when executing the computer program, performs the steps in the radioactive tracer distribution quantification optimization method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it performs the steps in the radioactive tracer distribution quantification optimization method as described in any one of claims 1-7.

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