An adaptive PET real-time imaging method based on fine system matrix

Abstract

The application discloses a kind of based on fine system matrix adaptive PET real-time imaging method, its steps include: 1) simulating generation PET system fine system matrix;2) based on the coincidence data and the fine system matrix that the PET system acquisition, using ordered subset maximum expectation value method carries out image reconstruction;Wherein in the iteration process of image reconstruction, the coincidence data is subsetted and iteratively updated, according to the relaxation factor α (t) that changes with time or the relaxation factor α (n) that changes with activity adaptively controls each iteration update step, according to each subset after each iteration update respectively carries out image reconstruction.The application can observe the distribution of radiopharmaceutical in the scanned object in real time, directly obtain PET real-time imaging image, and generate 4D imaging image after scanning.

Description

Technical Field

[0001] The present invention belongs to the fields of nuclear medicine imaging and high-performance parallel computing, and relates to a PET real-time imaging method, in particular to a fast adaptive PET real-time imaging method based on a fine system matrix. Background Art

[0002] With the development of nuclear medicine imaging technology, positron emission tomography (PET), as a functional molecular imaging technique, has provided advanced imaging tools for scientific research, clinical diagnosis, and treatment. Tracers labeled with positron-decay radioisotopes participate in metabolism. By detecting gamma photons produced by positron annihilation, the distribution of radionuclides within the tissues and organs of the scanned subject can be imaged, enabling the metabolism of radioactive drugs. This provides data supporting drug metabolism mechanisms and tissue metabolic levels. Unlike structural imaging methods, PET imaging has the ability to detect early, small lesions and non-organic lesions, making it crucial for the early detection and treatment of major diseases such as cancer. Therefore, PET imaging plays a vital role in preclinical research, clinical diagnosis, and treatment.

[0003] A PET imaging system includes a detector subsystem, an electronics subsystem, a data processing subsystem, and an image processing subsystem. The detector subsystem detects energy deposition in photons with a certain energy, while the electronics subsystem acquires valid signals and outputs event signals containing the deposited energy, deposition location, and deposition time to the data processing subsystem. The data processing subsystem organizes the detection events into Listmode data or Sinogram data using a specific format. The image processing subsystem performs data correction and image reconstruction based on the Listmode or Sinogram data to obtain an image of the radionuclide distribution. Current PET system designs typically consider positron annihilation as the primary pathway for generating photon pairs. Therefore, a valid PET detection event contains information about a pair of photons, also known as a line of response (LOR). A large number of detected LORs constitute Listmode data. Listmode data contains information such as the energy, time, and location of the annihilated photons, while Sinogram data organizes the detected LORs into a fixed-size data matrix based on the positional relationship of the LORs. Therefore, in PET data processing, Listmode data varies with the source activity and acquisition time, while Sinogram data is a fixed-size array. With the research and development of PET data processing algorithms, reconstruction methods for both Listmode and Sinogram data formats have been applied. Imaging algorithms primarily include analytical and iterative imaging.

[0004] PET imaging methods are generally categorized into static and dynamic imaging. Static imaging generally refers to data acquisition and reconstruction after the radiopharmaceutical distribution within the scanned subject has stabilized, resulting in 3D imaging. Dynamic imaging, on the other hand, involves acquisition and data processing that takes into account the temporal distribution of the radiopharmaceutical. Data acquisition begins before the subject is injected with the drug. After acquisition, the data is divided into time periods based on metabolic characteristics for 4D imaging, known as dynamic imaging.

[0005] Traditionally, both static and dynamic imaging involve data acquisition followed by offline analysis. However, in practical scientific research and clinical applications, online, real-time monitoring of radiopharmaceutical distribution is essential. High-precision, real-time imaging from PET systems is crucial for real-time adjustments to scanning protocols, real-time dose control during radiotherapy, and improved scanning throughput.

[0006] PET imaging systems process Listmode or Sinogram data, using analytical or iterative methods to image the radiopharmaceutical distribution. Existing methods are performed offline, requiring data processing after data acquisition. This prevents real-time imaging of the radiopharmaceutical distribution or only produces blurry images, failing to meet the needs of real-time imaging scenarios, such as preliminary assessment of drug metabolism model expectations and dose assessment during radiotherapy. Summary of the Invention

[0007] In response to the problems existing in the prior art, the purpose of the present invention is to provide a fast adaptive PET real-time imaging method based on a fine system matrix, so that during the PET scanning process, the distribution of radioactive drugs in the scanned object can be observed in real time, PET real-time imaging images can be directly obtained, and 4D imaging images can be generated after the scan is completed.

[0008] The present invention provides real-time dynamic data processing during PET scanning, with adaptive parameter adjustments based on the scan target. This enables millisecond-level image updates during the scan, providing real-time imaging of the current radiopharmaceutical distribution, and obtaining 4D dynamic images immediately after the scan. This significantly reduces imaging time and improves imaging efficiency and real-time performance. This technology introduces a new real-time reconstruction strategy to the field of PET imaging, thereby promoting real-time and efficient PET imaging applications.

[0009] The technical solution of the present invention is:

[0010] An adaptive PET real-time imaging method based on a fine system matrix comprises the following steps:

[0011] 1) Simulate and generate the detailed system matrix of the PET system;

[0012] 2) Based on the coincident data collected by the PET system and the refined system matrix, image reconstruction is performed using an ordered subset maximum expectation method; wherein during the iterative process of image reconstruction, the coincident data is subsetted and iteratively updated, the step size of each iterative update is controlled according to a relaxation factor α(t) that adaptively changes with time or a relaxation factor α(n) that adaptively changes with activity, and image reconstruction is performed separately for each subset after each iterative update.

[0013] Furthermore, the ordered subset maximum expected value method uses the formula Determine the distribution of the drug in the body in the image reconstructed using the jth subset at the k+1th iteration The system matrix of the PET system is obtained by simulation, and the element ρ in the system matrix is ij After ray driving, the corresponding element a in the fine system matrix is ​​obtained i,j ,ρ ij is the probability of the jth LOR of the data being detected by the i-th simulated voxel radiation source, I is the total number of pixels in the field of view, and J is the total number of LORs of the data being detected by the i-th simulated voxel radiation source.

[0014] Furthermore, the ordered subset maximum expected value method uses the formula Determine the distribution of the drug in the body in the image reconstructed using the jth subset at the k+1th iteration The system matrix of the PET system is obtained by simulation, and the element ρ in the system matrix is ij After ray driving, the corresponding element a in the fine system matrix is ​​obtained i,j ,ρ ij is the probability of the jth LOR of the data being detected by the i-th simulated voxel radiation source, I is the total number of pixels in the field of view, and J is the total number of LORs of the data being detected by the i-th simulated voxel radiation source.

[0015] Furthermore, a GPU parallel acceleration solution or a CPU multi-core parallel method is used to subset the conforming data and iteratively update the subsets.

[0016] Furthermore, a histogram equalization method is used to process each reconstructed image to improve the contrast of the reconstructed image.

[0017] Furthermore, a Monte Carlo simulation method is used to generate a fine system matrix of the PET system.

[0018] Furthermore, the fine system matrix is ​​a point drive system matrix or a ray drive system matrix; and the storage format of the conforming data is Listmode data or Sinogram data.

[0019] The advantages of the present invention are as follows:

[0020] (1) PET is used for radiotherapy dose monitoring, providing real-time drug distribution;

[0021] (2) Adaptively change the subset update mechanism based on the application scenario to better meet the imaging objectives;

[0022] (3) Realize online 4D dynamic reconstruction and improve the utilization rate of PET equipment;

[0023] (4) The method is universal for all equipment and does not depend on specific models. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is the overall flow chart of the present invention.

[0025] Figure 2 This is the matrix distribution diagram of the single simulation point system.

[0026] Figure 3 Generate a flow chart for the system matrix.

[0027] Figure 4 Comparison before and after histogram equalization processing;

[0028] (a) Before treatment, (b) After treatment.

[0029] Figure 5 The flowchart of real-time imaging based on time framing.

[0030] Figure 6 The flowchart of real-time imaging based on counting and framing. DETAILED DESCRIPTION

[0031] The present invention will be described in further detail below with reference to the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0032] The present invention designs a fast adaptive PET real-time imaging method based on a fine system matrix, which realizes simultaneous acquisition and imaging, and image reconstruction during data acquisition, thereby achieving real-time monitoring and evaluation of the distribution of radioactive drugs in the scanned object. The overall process of the method is as follows Figure 1 As shown, it mainly includes:

[0033] 1) Generate a detailed system matrix based on the Sinogram data storage format or Listmode data storage format;

[0034] 2) Adaptive subset update mechanism;

[0035] 3) GPU-based parallel acceleration processing;

[0036] 4) Adaptive image display based on image statistical histogram.

[0037] 1. Fine system matrix generation method

[0038] In PET imaging, the system matrix characterizes the physical relationship between the system's radiation source distribution and the projection data. The system matrix describes the probability of detecting the LOR formed by the uniform emission of Gamma rays from the pixels filled with radioactive drugs in the imaging space. There are analytical methods, simulation methods and experimental methods to generate the system matrix. In the present invention, the system matrix is ​​generated by Monte Carlo simulation, which has higher accuracy than the analytical method and higher generation efficiency than the experimental method. The simulation uses the axial translation symmetry and axial rotation symmetry of the PET detection system to generate a fine system matrix. Set the simulation core area, simulate and store the effective LOR event distribution point by point, such as Figure 2 As shown. This system matrix generation process uses pixels as the organizational structure, recorded as P-matrix, and can be used for pixel-driven image reconstruction. In ray-driven imaging applications, the response line LOR is used as the organizational structure, and the system matrix P-matrix obtained by simulation is used to drive the ray, generating the ray-driven system matrix R-matrix as the fine system matrix. The process is as follows Figure 3 As shown. The probability of the jth LOR detected by the i-th voxel radiation source in the simulated system matrix is ​​ρ ij , its expression is shown in 1. Where N i represents the simulated radioactive source activity at the i-th voxel, N ij It represents the detection count of the jth LOR detected by the radioactive source of the i-th voxel.

[0039]

[0040] Then the element ρ in the system matrix ij Element value α after ray driving ij The expression of is shown in formula 2, where represents the total count of LOR j in the full field of view range i∈(0,I), where I is the total number of pixels in the field of view of the PET system.

[0041]

[0042] The aforementioned system matrices are divided into a point-driven system matrix (matrix) and a ray-driven system matrix (R-matrix). Based on the characteristics of data storage, Sinogram has pixel attributes, while Listmode has ray attributes. Generally, the matrix is ​​used for Sinogram data organization and reconstruction, while the R-matrix is ​​used for Listmode data organization and reconstruction.

[0043] 2. Adaptive subset update mechanism (subset partitioning and relaxation factor, two modes)

[0044] The PET system acquires coincident data in real time and reconstructs the coincident data through a reconstruction algorithm. To meet the requirements of high-precision real-time imaging, this method uses the ordered subsets expectation maximum (OSEM) method for image reconstruction. During the iterative process, the coincident data is divided into subsets for iterative updates. During the update process, adaptive subset setting and update strategies are performed based on the count rate estimate and application objectives. Two update strategies are proposed: the time-based T-mode real-time imaging mode and the count-based N-mode real-time imaging mode. This method allows the algorithm to first capture rapid changes in drug distribution and then gradually transition to capturing slower changes, which helps to more accurately reconstruct the dynamic process of the drug in the body. The relaxation parameter is a parameter used in the iterative algorithm to control the update step size of each iteration. This study adopts two update methods, respectively introducing a relaxation factor α(t) that changes adaptively with time and a relaxation factor α(n) that changes adaptively with activity. A lookup table of the influence factor λ is developed based on the drug type and the metabolic sensitivity of the organ of interest to the drug. The expression of the influence factor λ is Equation (3).

[0045] α(t)=e -λt / T

[0046] α(n)=e -λn / N

[0047] λ=F(A,R)......(3)

[0048] Where A represents the radiopharmaceutical type, and R represents the estimated drug uptake in the region of interest. For single-injection drug metabolic imaging, which has a significant impact on time-dependent changes, the T-mode relaxation factor varies with the radiopharmaceutical metabolism. When drug distribution changes rapidly, α(t) will be larger, accelerating iterative convergence. As drug distribution changes more slowly over time, α(t) gradually decreases, ensuring algorithm stability.

[0049] During the metabolic plateau, subsets can be created based on the number of events or acquisition time, depending on temporal uniformity. Within each iteration, the image is updated once for each subset. Therefore, with the same amount of data and the same number of iterations, using subsets results in more image updates, accelerating image convergence and making the image closer to the true distribution.

[0050] 3. GPU-based parallel acceleration

[0051] This method uses a GPU parallel acceleration solution for timing subset or quantitative subset processing and CUDA multi-threaded parallel computing to improve image reconstruction speed and meet clinical application requirements.

[0052] 4. Adaptive contrast image display based on statistical histogram

[0053] After the image is reconstructed in real time, this study uses a method of timing image acquisition to refresh the reconstructed image in the software interface at regular intervals, allowing users to monitor the dynamic distribution of drugs in the scanned object in real time. Due to the uneven distribution of tracers in the body, the radioactivity in different areas varies greatly, resulting in a wide range between the maximum and minimum values ​​in the image. In order to obtain better visual effects when displaying the image, the contrast of the image needs to be adjusted. This study uses a histogram equalization method to improve the contrast of PET images, thereby improving the diagnostic value of the image, such as Figure 4 shown.

[0054] 5. Update the image

[0055] In order to view the dynamic process of radioactive drug distribution in the body, this study adopted a method of dynamic framing based on time, with the frame interval from small to large. This method allows the algorithm to first capture the rapid changes in drug distribution, and then gradually transition to capturing slower changes, which helps to more accurately reconstruct the dynamic process of drugs in the body. The relaxation coefficient (relaxation parameter) is a parameter used in the iterative algorithm to control the update step size of each iteration. This study adopted two updating methods, introducing a relaxation factor α(t) that changes adaptively with time and α(n) that changes adaptively with activity. This means that in the early stages of the acquisition and iteration process, when the drug distribution changes faster, α(t) will be larger, which will speed up the iterative convergence. As time goes by, the changes in drug distribution slow down, and α(t) gradually decreases to ensure the stability of the algorithm.

[0056]

[0057] In this method, the system matrix can be obtained by simulation, analysis or experiment. The acquisition of the system matrix only affects the accuracy of this method. The storage format of the collected conforming data can be Listmode data or based on Sinogram data.

[0058] For example:

[0059] The real-time reconstruction based on Listmode data proposed in this invention was tested on the animal panoramic PET independently developed by the Institute of High Energy Physics of the Chinese Academy of Sciences. Detector related parameters: crystal bar (1.58×1.58×10mm 3 ), detector effective field of view (143×143×213mm 3 ).

[0060] PET scans of mice were performed simultaneously with data acquisition and reconstruction based on the reconstruction method described in this study. The software interface updated the current reconstructed image in real time, reflecting the current distribution of the drug in the mouse. The results showed that the real-time reconstruction method of the present invention can monitor the distribution of drugs in mice in real time.

[0061] The reconstruction algorithm in this scheme can also be an extended ordered subset expectation maximization method or other reconstruction algorithms; in the adaptive subset update mechanism, specific nuclides can produce different parameters due to different markers; data acceleration is not limited to GPU methods or CPU multi-core parallel methods.

[0062] While specific embodiments of the present invention have been disclosed for illustrative purposes, intended to facilitate understanding and implementation of the present invention, those skilled in the art will appreciate that various substitutions, variations, and modifications are possible without departing from the spirit and scope of the present invention and the appended claims. Therefore, the present invention should not be limited to the disclosure of the preferred embodiments, and the scope of protection claimed in the present invention shall be determined by the scope of the claims.

Claims

1. A method for adaptive PET real-time imaging based on a fine system matrix, comprising the following steps: 1) Simulate and generate the detailed system matrix of the PET system; 2) reconstructing an image using an ordered subset maximum expectation method based on the coincident data acquired by the PET system and the refined system matrix; In the iterative process of image reconstruction, the conforming data is divided into subsets and iteratively updated, and the update step size of each iteration is controlled according to the relaxation factor α(t) that adaptively changes with time or the relaxation factor α(n) that adaptively changes with activity, and image reconstruction is performed on each subset after each iterative update.

2. The method according to claim 1, characterized in that The ordered subset maximum expected value method uses the formula Determine the distribution of the drug in the body in the image reconstructed using the jth subset at the k+1th iteration The system matrix of the PET system is obtained by simulation, and the element ρ in the system matrix is ij After ray driving, the corresponding element a in the fine system matrix is ​​obtained i,j ,ρ ij is the probability of the jth coincident data LOR detected by the ith simulated voxel radiation source, I is the total number of pixels in the field of view of the PET system, and J is the total number of coincident data LORs detected by the ith simulated voxel radiation source.

3. The method according to claim 1, characterized in that The ordered subset maximum expected value method uses the formula Determine the distribution of the drug in the body in the image reconstructed using the jth subset at the k+1th iteration The system matrix of the PET system is obtained by simulation, and the element ρ in the system matrix is ij After ray driving, the corresponding element a in the fine system matrix is ​​obtained i,j ,ρ ij is the probability of the jth coincident data LOR detected by the ith simulated voxel radiation source, I is the total number of pixels in the field of view of the PET system, and J is the total number of coincident data LORs detected by the ith simulated voxel radiation source.

4. The method according to claim 1, 2 or 3, characterized in that: A GPU parallel acceleration solution or a CPU multi-core parallel method is used to subset the conforming data and iteratively update the subsets.

5. The method according to claim 1, 2 or 3, characterized in that: The histogram equalization method is used to process each reconstructed image to improve the contrast of the reconstructed image.

6. The method according to claim 1, 2 or 3, characterized in that: The Monte Carlo simulation method is used to generate the detailed system matrix of the PET system.

7. The method according to claim 1, 2 or 3, characterized in that: The detailed system matrix is ​​a point-driven system matrix or a ray-driven system matrix; the storage format of the conforming data is Listmode data or Sinogram data.

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