Scattering correction method and device, digital device and computer readable storage medium

Abstract

The application provides a scattering correction method and device, a digital device and a computer readable storage medium. The method comprises: obtaining a down-sampling response line based on detection data; obtaining an estimated target function corresponding to each down-sampling response line by using a matrix estimation method based on a maximum likelihood-expectation maximization iterative algorithm; solving the estimated target function to obtain a number of scattering coincidence events corresponding to each down-sampling response line; performing up-sampling processing on the down-sampling response line to obtain an up-sampling response line; and calculating the number of scattering coincidence events corresponding to each up-sampling response line. When the embodiment of the application performs scattering correction, it does not depend on activity images and attenuation images, and the method does not require a hyperparameter, so that the hyperparameter no longer needs to be set in a determined range, and the method is simple and accurate.

Description

Technical Field

[0001] This application relates to the field of signal sampling technology, specifically to a scattering correction method, apparatus, digitization device, and computer-readable storage medium. Background Technology

[0002] Positron emission tomography (PET) works by labeling a compound with a radionuclide that can participate in blood flow or metabolic processes in living tissue. The compound is then injected into the organism. The positrons emitted by the radionuclide combine with electrons in the organism, resulting in electron-electron annihilation and producing two gamma photons of equal energy but opposite directions. Because these two gamma photons travel in different directions, the detector detects them at different times. If two scintillation crystals on the Line of Response (LOR) in the detector detect both gamma photons within a specified coincidence time window (e.g., 0–15 nanoseconds), the event of detecting these two gamma photons is called a coincidence event.

[0003] Coincidence events generally include true coincidence events, scattering coincidence events, and random coincidence events. A true coincidence event occurs when the time difference between the arrival of two gamma photons from the same annihilation event at two scintillation crystals located on the response line falls within the coincidence time window. A random coincidence event is a false coincidence event; in a random coincidence event, two detected gamma photons originate from different annihilation events but are mistakenly identified as occurring "simultaneously" within the coincidence time window. A scattering coincidence event refers to the following: for two gamma photons detected from the same annihilation event, one of the gamma photons changes its flight direction due to physical effects such as Compton scattering and / or Rayleigh scattering during its flight.

[0004] Of these three types of coincidence events, the data acquired for random coincidence events and scattering coincidence events may be erroneous, which could affect the resolution, contrast, and positioning accuracy of PET imaging. Therefore, correcting the acquired coincidence events is crucial.

[0005] Currently, commonly used scattering correction methods mainly include multi-window techniques, convolution / deconvolution techniques, and simulation-based techniques. Among these, simulation-based techniques are the most accurate and widely used.

[0006] Simulation-based techniques generally include single-scattering simulation, two-scattering simulation, and Monte Carlo simulation. Single-scattering simulation, for each response line, selects a scattering point from the input activity and decay images to obtain the photon path of a single scattering (i.e., a pair of gamma photons scatters only once). It calculates the single-scattering events along all photon paths corresponding to that response line. Finally, using tail data containing only scattering events, it fits the obtained single-scattering events using tail fitting (TF) to obtain a stretching factor for the scattering coincidence events, which is then applied to all data to achieve scattering correction. Two-scattering simulation is often used in conjunction with single-scattering simulation. The main difference is that two-scattering simulation uses two scattering points to determine the photon path. Monte Carlo simulation primarily determines the total scattering distribution by simulating the motion of each gamma photon pair determined based on the input activity and decay images.

[0007] In the process of developing this application, the inventors discovered that simulation-based technologies have at least the following problems:

[0008] (1) Single scattering simulation methods only consider the case of single scattering. However, in reality, multiple scattering may occur, which may lead to lower accuracy of scattering correction results and thus lower quality of reconstructed images. Although double scattering simulation methods and Monte Carlo simulation methods consider the case of multiple scattering, the calculation process is complex and computationally intensive, which will slow down the image reconstruction process and is not efficient.

[0009] (2) Before reconstructing the activity image, the scattering events need to be determined. However, the simulation-based method for determining the scattering events requires prior knowledge of the activity image, and existing technologies make it difficult to obtain accurate activity images.

[0010] (3) Since it is difficult to obtain activity images outside the field of view, existing technologies usually do not take into account external radiation (i.e., scattering events generated by gamma photon pairs located outside the field of view), which may lead to lower accuracy of the correction results and thus affect the quality of the reconstructed image.

[0011] (4) The currently used OSL-EDR and OT-EDR algorithms require the selection of appropriate hyperparameters. Only then can better results be achieved. Optimal Some preliminary experiments are needed for adjustments, and different sizes of prostheses require different approaches. This has brought many inconveniences to the use of OSL-EDR and OT-EDR algorithms.

[0012] (5) Currently, the moment estimation method is used to obtain the estimated objective function corresponding to each downsampled response line. The estimated objective function assumes that the probability density functions of unscattered photons and scattered photons are the same, which leads to the inaccuracy of the number of scattering coincidence events corresponding to the downsampled response line. In view of this, there is an urgent need to provide a scattering correction method that is different from simulation-based techniques and does not depend on activity images and attenuation images.

[0013] The content in the background section merely discloses technology known only to the inventors and is not intended to represent prior art in the field. Summary of the Invention

[0014] This application aims to provide a scattering correction method, apparatus, digitization device, and computer-readable storage medium to solve at least one problem existing in the prior art.

[0015] According to a first aspect of this application, a scattering correction method is provided, the scattering correction method comprising: obtaining downsampled response lines based on probe data; obtaining an estimated objective function corresponding to each downsampled response line using a moment estimation method based on a maximum likelihood-expectation-maximization iterative algorithm, wherein the estimated objective function is obtained based on a two-dimensional instantaneous coincidence energy histogram, a two-dimensional delayed coincidence energy histogram, a probability density function of scattered photons, and a probability density function of unscattered photons corresponding to each downsampled response line; solving the estimated objective function to obtain the number of scattering coincidence events corresponding to each downsampled response line; upsampling the downsampled response lines to obtain upsampled response lines; and calculating the number of scattering coincidence events corresponding to each upsampled response line.

[0016] In some embodiments, the downsampled response line is obtained based on probe data, including: obtaining a coincident response line based on the probe data; and performing downsampling processing on the coincident response line to obtain a downsampled response line.

[0017] In some embodiments, the detection data is acquired based on the target object.

[0018] In some embodiments, the estimated objective function corresponding to each downsampled response line is obtained using the moment estimation method based on the maximum likelihood-expectation-maximization iterative algorithm. This includes: obtaining a two-dimensional instantaneous coincidence energy histogram, a two-dimensional delayed coincidence energy histogram, and two one-dimensional probe energy spectra corresponding to each downsampled response line based on each coincidence event corresponding to each downsampled response line; and obtaining the probability density function of scattered photons and the probability density function of unscattered photons corresponding to the corresponding downsampled response line using the maximum likelihood-expectation-maximization iterative algorithm based on the two one-dimensional probe energy spectra.

[0019] In some embodiments, based on each coincidence event corresponding to each downsampled response line, two one-dimensional detector energy spectra are obtained, including: splitting all coincidence events corresponding to the downsampled response line into single events; dividing the single events into two parts according to the location information of the single events, each part corresponding to a one-dimensional detector energy spectrum; and obtaining two one-dimensional detector energy spectra based on the two parts of single events.

[0020] In some embodiments, based on the two one-dimensional detector energy spectra, a maximum likelihood-expectation-maximization iterative algorithm is used to obtain the probability density functions of scattered photons and unscattered photons corresponding to the corresponding downsampled response lines. This includes: obtaining a correspondence function between the one-dimensional detector energy spectrum and the one-dimensional gamma photon energy spectrum; based on the obtained two one-dimensional detector energy spectra and the correspondence function, using the maximum likelihood-expectation-maximization iterative algorithm and the obtained initial iteration values ​​corresponding to the corresponding downsampled response lines to obtain the two one-dimensional gamma photon energy spectra corresponding to the two one-dimensional detector energy spectra; and estimating the probability density functions of the two scattered photons and the two unscattered photons corresponding to the corresponding downsampled response lines based on the two one-dimensional gamma photon energy spectra.

[0021] In some embodiments, the correspondence function between the one-dimensional detector energy spectrum and the one-dimensional gamma photon energy spectrum is:

[0022] ,

[0023] in, This represents the expected value of the one-dimensional detection energy spectrum obtained from scanning the target object, corresponding to a single downsampling response line; Represents the one-dimensional gamma photon energy spectrum. The fuzzy response matrix characterizing a single downsampled response line. This represents the one-dimensional delayed energy spectrum corresponding to the random coincidence events included in a single response line.

[0024] In some embodiments, the one-dimensional delayed energy spectrum corresponding to the random coincidence event is obtained through delayed coincidence event estimation.

[0025] In some embodiments, obtaining a one-dimensional delayed energy spectrum includes: obtaining all delayed coincidence events in the downsampled response line based on a delayed coincidence window; splitting each delayed coincidence event into two corresponding delayed single events; dividing the delayed single event into two parts according to the location information of the delayed single event, each part corresponding to a one-dimensional delayed energy spectrum; and obtaining two one-dimensional delayed energy spectra based on the two parts of delayed single events; wherein, the location information of the delayed single event includes the location information of the downsampled detection module that detected the delayed single event.

[0026] In some embodiments, the fuzzy response matrix is ​​obtained. The process includes: acquiring prior detection data based on a prosthesis; acquiring a prior downsampled response line based on the prior detection data; dividing all prior coincidence events corresponding to the prior downsampled response line into prior instant coincidence events and prior delayed coincidence events; acquiring a prior one-dimensional instantaneous energy spectrum based on the prior instantaneous coincidence events corresponding to the downsampled response line; acquiring a prior one-dimensional delayed energy spectrum based on the prior delayed coincidence events corresponding to the downsampled response line; obtaining the difference between the prior one-dimensional instantaneous energy spectrum and the prior one-dimensional delayed energy spectrum, normalizing the difference to obtain a one-dimensional intermediate energy spectrum; and acquiring a fuzzy response matrix based on the one-dimensional intermediate energy spectrum.

[0027] In some embodiments, obtaining the two one-dimensional gamma photon spectra corresponding to the two one-dimensional detector energy spectra includes: obtaining the maximum likelihood-expectation-maximization iterative algorithm iterative formula based on the correspondence function between the one-dimensional detector energy spectrum and the one-dimensional gamma photon energy spectrum; setting the number of iterations, iterating through the maximum likelihood-expectation-maximization iterative algorithm iterative formula based on the obtained initial iteration value until the set number of iterations is reached, thereby obtaining the two one-dimensional gamma photon spectra corresponding to the two one-dimensional detector energy spectra.

[0028] In some embodiments, the iterative formula of the maximum likelihood-expectation maximization iterative algorithm is:

[0029] ,

[0030] in, In Characterizes the one-dimensional probe energy spectrum obtained from scanning the target object, corresponding to a single downsampling response line. Characterizing the first in the energy spectrum A vertical strip area, , Characterizing the first in the energy spectrum respectively The, the A vertical strip area, It is the number of iterations. and In This is the fuzzy response matrix corresponding to the downsampled response line. The fuzzy response matrix corresponding to this downsampled response line is the first... line, number The value of the column, The fuzzy response matrix corresponding to this downsampled response line is the first... line, number The value of the column; This indicates the one-dimensional delayed energy spectrum corresponding to the random coincidence events included in the downsampled response line; For the first The energy spectrum obtained in the +1 iteration is the first... The one-dimensional gamma photon energy spectrum corresponding to each vertical bar region; , Respectively characterize the first The energy spectrum is obtained in the nth iteration. The, the The vertical bar regions correspond to the one-dimensional gamma photon energy spectrum.

[0031] In some embodiments, obtaining the initial iteration value includes: obtaining the global initial scattered photon energy spectrum; performing deblurring on the global initial scattered photon energy spectrum to obtain the global intermediate initial iteration value; and stretching the global intermediate initial iteration value to obtain the initial iteration value corresponding to each downsampled response line.

[0032] In some embodiments, obtaining the global initial scattered photon energy spectrum includes: obtaining coincidence response lines based on detection data; dividing all coincidence events corresponding to all coincidence response lines into instantaneous coincidence events and random coincidence events; obtaining the global one-dimensional instantaneous energy spectrum based on instantaneous coincidence events; obtaining the global one-dimensional delayed energy spectrum based on delayed coincidence events; subtracting the global one-dimensional delayed energy spectrum corresponding to delayed coincidence events from the global one-dimensional instantaneous energy spectrum corresponding to instantaneous coincidence events on all response lines to obtain the global one-dimensional undelayed energy spectrum; and obtaining the objective function of the global initial scattered photon energy spectrum based on the global one-dimensional undelayed energy spectrum.

[0033] In some embodiments, the objective function of the global initial scattered photon energy spectrum is:

[0034] ,

[0035] Among them, among them, In The initial global scattered photon energy spectrum; Characterizing the first in the energy spectrum A vertical strip area, In The global one-dimensional undelayed energy spectrum is obtained based on the target object. In The global one-dimensional intermediate energy spectrum is obtained from the entire PET system scan, and the global one-dimensional intermediate energy spectrum is obtained based on the prosthesis; and The lower and upper thresholds of the high-energy window are set. The tensile coefficient is denoted as .

[0036] In some embodiments, obtain This includes: acquiring prior detection data based on the prosthesis; acquiring all prior coincidence response lines based on the prior detection data; classifying all prior coincidence events corresponding to all prior coincidence response lines into global prior instant coincidence events and global prior delayed coincidence events; acquiring the global prior one-dimensional instantaneous energy spectrum based on the global prior instantaneous coincidence events; acquiring the global prior one-dimensional delayed energy spectrum based on the global prior delayed coincidence events; and obtaining the difference between the global prior instantaneous energy spectrum and the global prior delayed energy spectrum. .

[0037] In some embodiments, the global initial scattered photon energy spectrum is deblurred to obtain a global intermediate initial iteration value, including: obtaining a deblurring iteration formula based on a maximum likelihood-expectation-maximization iteration algorithm; setting a prior initial iteration value and iteration conditions, and iterating through the deblurring iteration formula until the iteration conditions are met to obtain the scattered part of the global initial energy spectrum; estimating the unscattered part of the global initial energy spectrum based on the scattered part of the global initial energy spectrum; and obtaining a global intermediate initial iteration value based on the scattered part of the global initial energy spectrum and the unscattered part of the global initial energy spectrum.

[0038] In some embodiments, the deblurring iterative formula is:

[0039] ,

[0040] in, In The characterization is based on the global one-dimensional probe energy spectrum obtained by scanning the target object. Characterizing the first in the energy spectrum A vertical strip area, , Characterizing the first in the energy spectrum respectively The j-th vertical region, It is the number of iterations. , In The global fuzzy response matrix, The first fuzzy response matrix is ​​the global fuzzy response matrix. line, number The value of the column, The first fuzzy response matrix is ​​the global fuzzy response matrix. line, number The value of the column; This represents the global one-dimensional delayed energy spectrum corresponding to all random coincidence events; For the first The energy spectrum obtained in the +1 iteration is the first... The global one-dimensional gamma photon energy spectrum corresponding to each vertical bar region; , The energy spectrum obtained in the k-th iteration is represented by the following characters: The, the The global one-dimensional gamma photon energy spectrum corresponding to each vertical bar region;

[0041] ,

[0042] in, Characterization constant.

[0043] In some embodiments, the global fuzzy response matrix is ​​obtained. The process includes: acquiring prior detection data based on the spoof; acquiring all prior coincidence response lines based on the prior detection data; dividing all prior coincidence events corresponding to all prior coincidence response lines into global prior instant coincidence events and global prior delayed coincidence events; acquiring the global prior one-dimensional instantaneous energy spectrum based on the global prior instantaneous coincidence events; acquiring the global prior one-dimensional delayed energy spectrum based on the global prior delayed coincidence events; obtaining the difference between the global prior instantaneous energy spectrum and the global prior delayed energy spectrum; normalizing the difference to obtain the global prior intermediate energy spectrum; and acquiring the global fuzzy response matrix based on the global prior intermediate energy spectrum.

[0044] In some embodiments, the unscattered portion of the global initial energy spectrum is estimated using an estimation function, which is:

[0045] ,

[0046] in, This is the global fuzzy response matrix; For hyperparameters; This represents the ratio of unscattered photons to scattered photons.

[0047] ,

[0048] in, To express summation, and Corresponding to , .

[0049] In some embodiments, the representation function of the global intermediate initial iteration value is:

[0050] ,

[0051] in, It's a hyperparameter. The global fuzzy response matrix, This represents the ratio of unscattered photons to scattered photons.

[0052] In some embodiments, stretching the intermediate initial iteration values ​​to obtain the initial iteration values ​​corresponding to each downsampled response line includes:

[0053] The global intermediate initial iteration value is stretched using a stretching function to obtain the initial iteration value corresponding to each downsampled response line. The stretching function is:

[0054] ,

[0055] in, The initial iteration value, To express summation, The global fuzzy response matrix, This is the global intermediate initial iteration value; This represents the one-dimensional probe energy spectrum obtained from target object scanning, corresponding to a single downsampling response line. This represents the one-dimensional delayed energy spectrum corresponding to the random coincidence events included in a single response line.

[0056] In some embodiments, estimating the probability density functions of the two scattered photons and the two unscattered photons corresponding to the corresponding downsampled response line based on the two one-dimensional gamma photon energy spectra includes: dividing the one-dimensional gamma photon energy spectrum into a scattered part and an unscattered part; performing fuzzy response recovery processing on the scattered and unscattered parts of the two one-dimensional gamma photon energy spectra to obtain the two scattered parts and the two unscattered parts corresponding to the two one-dimensional probe energy spectra corresponding to the corresponding downsampled response line; performing normalization processing on the two scattered and two unscattered parts corresponding to the two one-dimensional probe energy spectra; and estimating the probability density functions of the two scattered photons and the two unscattered photons corresponding to the corresponding downsampled response line based on the obtained normalized values.

[0057] In some embodiments, the scattering portion is characterized as:

[0058]

[0059] The unscattered portion is characterized as follows:

[0060] ,

[0061] in, For the scattering part, This represents the unscattered portion.

[0062] In some embodiments, dividing the one-dimensional gamma photon energy spectrum into a scattering portion and an unscattered portion includes: dividing the one-dimensional gamma photon energy spectrum based on the energy value of the gamma photon when it does not scatter, with the portion less than the energy value being the scattering portion and the portion equal to the energy value being the unscattered portion.

[0063] In some embodiments, the estimated objective function is:

[0064] ,

[0065] in, These represent the number of true matching events, which do not require further processing. Indicates the number of scattering coincidence events. , is the unknown quantity to be determined; coefficient The calculation method is as follows:

[0066] ,

[0067] ,

[0068] in, and It is the probability density function of unscattered photons corresponding to the two detection modules in the downsampling response line. and It is the probability density function of scattered photons corresponding to the two detection modules in the downsampling response line;

[0069] Among them, the calculation obtained The value represents the number of scattering coincidence events corresponding to the downsampled response line.

[0070] In some embodiments, upsampling the downsampled response lines includes: processing the downsampled response lines using a bilinear interpolation method, a 4D linear interpolation method, or a 5D linear interpolation method to obtain the upsampled response lines corresponding to each downsampled response line.

[0071] In some embodiments, when processing the downsampled response line using a 4D linear interpolation method, the number of scattering coincidence events corresponding to the upsampled response line is calculated using the following formula:

[0072] ,

[0073] in, Characterized by the upsampling crystal after 4D linear interpolation and The number of scattering coincidence events that constitute the upsampled response line; Indicates upsampling crystal The set of the corresponding downsampling center crystal, downsampling axial adjacent crystal, downsampling radial adjacent crystal, and downsampling relative crystal; and This indicates the crystal number corresponding to the downsampled response line; Characterization of downsampling crystal and The number of scattering coincidence events on the downsampled response line; This indicates that for upsampling crystals Regarding downsampling crystals The weights; This indicates that for upsampling crystals Regarding downsampling crystals The weights; The normalization factor is characterized as follows:

[0074] ,

[0075] in The number of upsampled response lines contained in a downsampled response line.

[0076] In some embodiments, calculating the number of scattering coincidence events corresponding to each upsampled response line includes: calculating the ratio of the total number of scattering coincidence events on each downsampled response line to the total number of coincidence events; and calculating the number of scattering coincidence events corresponding to the corresponding upsampled response line based on the number of coincidence events on each upsampled response line included in each downsampled response line and the ratio.

[0077] According to a second aspect of this application, a scattering correction method is provided, the scattering correction method comprising: acquiring response lines based on probe data; acquiring an estimated objective function corresponding to each response line using a moment estimation method based on a maximum likelihood-expectation-maximization iterative algorithm, wherein the estimated objective function is acquired based on a two-dimensional instantaneous coincidence energy histogram, a two-dimensional delayed coincidence energy histogram, a probability density function of scattered photons, and a probability density function of unscattered photons corresponding to each response line; solving the estimated objective function to obtain the number of scattering coincidence events corresponding to each response line; and calculating the number of scattering coincidence events corresponding to each response line.

[0078] In some embodiments, the estimated objective function corresponding to each response line is obtained using the moment estimation method based on the maximum likelihood-expectation-maximization iterative algorithm, including: obtaining a two-dimensional instantaneous coincidence energy histogram, a two-dimensional delayed coincidence energy histogram, and two one-dimensional probe energy spectra corresponding to each response line based on each coincidence event corresponding to each response line; and obtaining the probability density function of scattered photons and the probability density function of unscattered photons corresponding to the corresponding response line using the maximum likelihood-expectation-maximization iterative algorithm based on the two one-dimensional probe energy spectra.

[0079] In some embodiments, obtaining two one-dimensional detector energy spectra based on each coincidence event corresponding to each response line includes: splitting all coincidence events corresponding to the response line into single events; dividing the single events into two parts according to the location information of the single events, with each part corresponding to a one-dimensional detector energy spectrum; and obtaining two one-dimensional detector energy spectra based on the two parts of single events.

[0080] In some embodiments, based on the two one-dimensional detector energy spectra, a maximum likelihood-expectation-maximization iterative algorithm is used to obtain the probability density functions of scattered photons and unscattered photons corresponding to the corresponding response lines. This includes: obtaining a correspondence function between the one-dimensional detector energy spectrum and the one-dimensional gamma photon energy spectrum; based on the obtained two one-dimensional detector energy spectra and the correspondence function, using the maximum likelihood-expectation-maximization iterative algorithm and the obtained initial iteration values ​​corresponding to the corresponding response lines to obtain the two one-dimensional gamma photon energy spectra corresponding to the two one-dimensional detector energy spectra; and estimating the probability density functions of the two scattered photons and the two unscattered photons corresponding to the corresponding response lines based on the two one-dimensional gamma photon energy spectra.

[0081] In some embodiments, obtaining the two one-dimensional gamma photon spectra corresponding to the two one-dimensional detector energy spectra includes: obtaining the maximum likelihood-expectation-maximization iterative algorithm iterative formula based on the correspondence function between the one-dimensional detector energy spectrum and the one-dimensional gamma photon energy spectrum; setting the number of iterations, iterating through the maximum likelihood-expectation-maximization iterative algorithm iterative formula based on the obtained initial iteration value until the set number of iterations is reached, thereby obtaining the two one-dimensional gamma photon spectra corresponding to the two one-dimensional detector energy spectra.

[0082] In some embodiments, obtaining the initial iteration value includes: obtaining the global initial scattered photon energy spectrum; performing deblurring processing on the global initial scattered photon energy spectrum to obtain the global intermediate initial iteration value; and stretching the global intermediate initial iteration value to obtain the initial iteration value corresponding to each response line.

[0083] In some embodiments, obtaining the global initial scattered photon energy spectrum includes: obtaining coincidence response lines based on detection data; dividing all coincidence events corresponding to all coincidence response lines into instantaneous coincidence events and random coincidence events; obtaining the global one-dimensional instantaneous energy spectrum based on instantaneous coincidence events; obtaining the global one-dimensional delayed energy spectrum based on delayed coincidence events; subtracting the global one-dimensional delayed energy spectrum corresponding to delayed coincidence events from the global one-dimensional instantaneous energy spectrum corresponding to instantaneous coincidence events on all response lines to obtain the global one-dimensional undelayed energy spectrum; and obtaining the objective function of the initial scattered photon energy spectrum based on the global one-dimensional undelayed energy spectrum.

[0084] In some embodiments, the global initial scattered photon energy spectrum is deblurred to obtain a global intermediate initial iteration value, including: obtaining a deblurring iteration formula based on a maximum likelihood-expectation-maximization iteration algorithm; setting a prior initial iteration value and the number of iterations, and iterating through the deblurring iteration formula until the number of iterations is reached to obtain the scattered part of the global initial energy spectrum; estimating the unscattered part of the global initial energy spectrum based on the scattered part of the global initial energy spectrum; and obtaining a global intermediate initial iteration value based on the scattered part of the global initial energy spectrum and the unscattered part of the global initial energy spectrum.

[0085] In some embodiments, estimating the probability density functions of the two scattered photons and the two unscattered photons corresponding to the corresponding response line based on the two one-dimensional gamma photon energy spectra includes: dividing the one-dimensional gamma photon energy spectrum into a scattered part and an unscattered part; performing fuzzy response recovery processing on the scattered and unscattered parts of the two one-dimensional gamma photon energy spectra to obtain the two scattered parts and the two unscattered parts corresponding to the two one-dimensional detector energy spectra corresponding to the corresponding response line; performing normalization processing on the two scattered and two unscattered parts corresponding to the two one-dimensional detector energy spectra; and estimating the probability density functions of the two scattered photons and the two unscattered photons corresponding to the corresponding response line based on the obtained normalized values.

[0086] In some embodiments, dividing the one-dimensional gamma photon energy spectrum into a scattering portion and an unscattered portion includes: dividing the one-dimensional gamma photon energy spectrum based on the energy value of the gamma photon when it does not scatter, with the portion less than the energy value being the scattering portion and the portion equal to the energy value being the unscattered portion.

[0087] According to a third aspect of this application, an image reconstruction method is provided, the image reconstruction method comprising: obtaining the number of scattering coincidence events corresponding to each response line based on the scattering correction method described in any of the above embodiments; correcting the scattering events based on the number of scattering coincidence events to obtain a reconstructed image.

[0088] According to a fourth aspect of this application, a scattering correction device is provided, the scattering correction device comprising: a downsampled response line acquisition module configured to acquire downsampled response lines based on probe data; an estimation module configured to acquire an estimated objective function corresponding to each downsampled response line using a moment estimation method based on a maximum likelihood-expectation-maximization iterative algorithm, the estimated objective function being acquired based on a two-dimensional instantaneous coincidence energy histogram, a two-dimensional delayed coincidence energy histogram, a probability density function of scattered photons, and a probability density function of unscattered photons corresponding to each downsampled response line; a scattering coincidence event number acquisition module configured to solve the estimated objective function to acquire the number of scattering coincidence events corresponding to each downsampled response line; an upsampling module configured to perform upsampling processing on the downsampled response lines to acquire upsampled response lines; and a scattering coincidence event number calculation module configured to calculate the number of scattering coincidence events corresponding to each upsampled response line.

[0089] According to a fifth aspect of this application, a scattering correction device is provided, the scattering correction device comprising: a response line acquisition module configured to acquire response lines based on probe data; an estimation module configured to acquire an estimated objective function corresponding to each response line using a moment estimation method based on a maximum likelihood-expectation-maximization iterative algorithm, the estimated objective function being acquired based on a two-dimensional instantaneous coincidence energy histogram, a two-dimensional delayed coincidence energy histogram, a probability density function of scattered photons, and a probability density function of unscattered photons corresponding to each response line; and a scattering coincidence event number acquisition module configured to solve the estimated objective function to acquire the number of scattering coincidence events corresponding to each response line.

[0090] According to a sixth aspect of this application, an image reconstruction apparatus is provided, the image reconstruction apparatus comprising: a scattering coincidence event number acquisition module configured to acquire the number of scattering coincidence events corresponding to a response line based on the scattering correction apparatus described in any of the above embodiments; and a reconstruction module configured to perform image reconstruction using an iterative image reconstruction algorithm based on the number of scattering coincidence events, and acquire a reconstructed image.

[0091] According to a seventh aspect of this application, a digital device is provided, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the method described in any of the above embodiments.

[0092] According to an eighth aspect of this application, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the method described in any one of the above embodiments.

[0093] Based on the above embodiments of this application, the beneficial effects of this application include one or more of the following effects in combination:

[0094] In some embodiments, a scheme combining the maximum likelihood-expectation-maximization iterative algorithm with the moment estimation method is adopted. Based on the maximum likelihood-expectation-maximization iterative algorithm, the moment estimation method is used to obtain the estimated objective function corresponding to each downsampled response line. On the one hand, the maximum likelihood-expectation-maximization iterative algorithm (MLEM) performs scattering correction without relying on the activity image and attenuation image, making scattering correction easy to implement, and the method does not require hyperparameters. Therefore, it is no longer necessary to adjust hyperparameters. Set within a defined range, i.e. subject to hyperparameters The impact is minimal, requiring almost no adjustment to hyperparameters. Adjustments are made to make scattering correction more convenient; on the other hand, the probability density functions of unscattered photons used in the estimated objective function are not necessarily the same, and the probability density functions of scattered photons are not necessarily the same either. Instead, they are determined specifically based on the two detection modules corresponding to the downsampled response line, and the number of scattering coincidence events corresponding to the downsampled response line is more accurate. Attached Figure Description

[0095] The embodiments of this application are described in detail below with reference to the accompanying drawings. These drawings, which form part of this application, are used to provide a further understanding of the application. The illustrative embodiments and descriptions of this application are used to explain the application and do not constitute an undue limitation of the application. In the drawings:

[0096] Figure 1 An exemplary flowchart of a scattering correction method according to an example embodiment of this application is shown;

[0097] Figure 2 This diagram illustrates the calculation of crystal weights in 4D linear interpolation according to an example embodiment of this application.

[0098] Figure 3 An exemplary flowchart of a scattering correction method according to another example embodiment of this application is shown;

[0099] Figure 4 An exemplary flowchart of an image reconstruction method according to an example embodiment of this application is shown;

[0100] Figure 5 An exemplary block diagram of a scattering correction method according to an example embodiment of this application is shown;

[0101] Figure 6 An exemplary block diagram of a scattering correction method according to another example embodiment of this application is shown;

[0102] Figure 7 An exemplary block diagram of an image reconstruction apparatus according to an example embodiment of this application is shown. Detailed Implementation

[0103] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of this application. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0104] In the description of this application, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicating orientation or positional relationships based on the orientation or positional relationships shown in the accompanying drawings, are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application. Furthermore, the terms "first" and "second" are used only for description and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more similar features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0105] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection, an electrical connection, or a communication connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0106] In this application, unless otherwise expressly specified and limited, "above" or "below" the second feature can include direct contact between the first and second features, or contact between the first and second features through another feature between them. Furthermore, "above," "over," and "on top" of the second feature includes the first feature being directly above or diagonally above the second feature, or simply indicates that the relative height of the first feature in a certain dimension is higher than that of the second feature. "Below," "below," and "under" of the second feature includes the first feature being directly above or diagonally above the second feature, or simply indicates that the relative position of the first feature in a certain dimension is smaller than that of the second feature.

[0107] Different embodiments or examples are provided below to implement different structures of this application. To simplify this application, the components and arrangements of specific examples are described below. Of course, these are merely examples and are not intended to limit this application. Reference numerals may be repeated in different examples; such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements described. Furthermore, this application provides examples of various specific processes and materials, but those skilled in the art can apply other processes and / or substitute other materials based on the teachings of this application.

[0108] The following description, with reference to the accompanying drawings, illustrates some preferred embodiments of the present application. It should be noted that the following description is for illustrative purposes only and is not intended to limit the scope of protection of this application.

[0109] Figure 1 This is an exemplary flowchart of a scattering correction method according to some embodiments of this application.

[0110] See also Figure 1 The scattering correction method 100 may include the following steps.

[0111] S110, obtains downsampling response line based on probe data.

[0112] In some embodiments of this application, step S110 may further include:

[0113] Step S0111: Obtain probe data based on the target object.

[0114] In some embodiments, the target object may be a living object, including but not limited to humans, animals, etc.

[0115] In some embodiments, the detection data may be sampling data acquired based on detector detection and subsequently based on multi-voltage threshold (MVT) sampling. Specifically, the detection data includes voltage threshold time pairs. The specific design of the detector can be found in existing technologies and will not be elaborated upon here.

[0116] In some embodiments of this application, step S110 may further include the following steps:

[0117] S111, Obtain the response line based on the probe data.

[0118] In some embodiments, the probe data is filtered based on time windows and energy windows to obtain coincident events, thereby obtaining coincident response lines. Specific details can be found in existing technologies, which will not be elaborated upon here.

[0119] S112. Perform downsampling processing on the conforming response line to obtain a downsampled response line.

[0120] Those skilled in the art will understand that a response line refers to the connection line between a pair of scintillation crystals in a PET system's detector module pair. Since the number of coincidence events on a single response line may be relatively small during normal scanning, it is difficult to obtain the corresponding energy spectrum based on the coincidence events corresponding to a single response line. Therefore, downsampling processing is required to obtain a downsampled response line. For example, if a PET system's detector module pair originally contains 3×3=9 coincidence response lines, downsampling these 9 response lines yields a single downsampled response line formed by merging them. Specifically, downsampling processing can merge response lines within a single detector module pair, or it can merge response lines from multiple adjacent detector module pairs (e.g., two axially adjacent, four axially and radially adjacent, etc.).

[0121] In some embodiments, downsampling processing includes merging multiple parallel or angularly aligned response lines that meet preset conditions into a single downsampling response line, but is not limited thereto. Specific details of downsampling processing can be found in existing technologies and will not be elaborated upon here.

[0122] S120, based on the maximum likelihood-expectation maximization iterative algorithm, uses the moment estimation method to obtain the estimated objective function corresponding to each downsampled response line. The estimated objective function is obtained based on the two-dimensional instantaneous coincidence energy histogram, the two-dimensional delayed coincidence energy histogram, the probability density function of scattered photons and the probability density function of unscattered photons corresponding to each downsampled response line.

[0123] In some embodiments, the maximum likelihood-expectation maximization (MLEM) iterative algorithm is obtained based on the standard maximum likelihood-expectation maximization algorithm in PET image reconstruction. For details, please refer to existing technologies, which will not be elaborated upon here.

[0124] In some embodiments, step S120 includes:

[0125] Based on each coincidence event corresponding to each downsampled response line, a two-dimensional instantaneous coincidence energy histogram, a two-dimensional delayed coincidence energy histogram, and two one-dimensional probe energy spectra are obtained for each downsampled response line. Based on the two one-dimensional probe energy spectra, the probability density function of scattered photons and the probability density function of unscattered photons corresponding to the corresponding downsampled response line are obtained using the maximum likelihood-expectation maximization iterative algorithm.

[0126] In some embodiments, the energy spectrum is characterized as a one-dimensional energy histogram, where the horizontal axis represents energy and the total axis represents the gamma photon count. The one-dimensional detection energy spectrum refers to the energy spectrum composed of the energies of gamma photons detected by the detector. It should be understood that the energy spectrum histogram includes multiple vertical bar regions, each of which is called a bin; specific details can be found in existing technologies and will not be elaborated here.

[0127] In some embodiments, based on each coincidence event corresponding to each downsampling response line, two one-dimensional probe energy spectra are obtained, including:

[0128] S1211 breaks down all coincidence events corresponding to the downsampled response line into single events.

[0129] It should be understood that a coincidence event contains two individual events. Each coincidence event in a downsampled response line is broken down into two corresponding individual events, each containing location information, energy information, and time information.

[0130] S1212 divides a single event into two parts based on its location information, with each part corresponding to a one-dimensional detection energy spectrum.

[0131] In some embodiments, the location information of a single event includes the location information of the downsampling detection module that detected the single event. For example, a downsampling response line corresponds to two detection modules A and B, and a portion of the single event included in the downsampling response line corresponds to detection module A, while the other portion corresponds to detection module B.

[0132] S1213, based on the two parts of single event partitioning, obtains two one-dimensional probe energy spectra.

[0133] In some specific examples, based on the detection module information corresponding to a single event, two one-dimensional detection energy spectra corresponding to the downsampled response line can be obtained. Continuing with the example above, one one-dimensional detection energy spectrum can be obtained based on the single event corresponding to detection module A, and the other one-dimensional detection energy spectrum corresponding to the downsampled response line can be obtained based on the single event corresponding to detection module B.

[0134] In some specific embodiments, based on the two one-dimensional detector energy spectra, a maximum likelihood-expectation-maximization iterative algorithm is used to obtain the probability density function of scattered photons and the probability density function of unscattered photons corresponding to the downsampled response lines, including:

[0135] S1221, the function that obtains the correspondence between the one-dimensional detector energy spectrum and the one-dimensional gamma photon energy spectrum.

[0136] In some embodiments, the correspondence function between the one-dimensional detector energy spectrum and the one-dimensional gamma photon energy spectrum is:

[0137] (1)

[0138] in, This represents the expected value of the one-dimensional detection energy spectrum obtained from scanning the target object, corresponding to a single downsampling response line; Represents the one-dimensional gamma photon energy spectrum. The fuzzy response matrix characterizing a single downsampled response line. This represents the one-dimensional delayed energy spectrum corresponding to the random coincidence events included in a single response line.

[0139] In some embodiments, the one-dimensional delayed energy spectrum corresponding to a random coincidence event is obtained through delayed coincidence event estimation.

[0140] In some specific embodiments, obtaining a one-dimensional delayed energy spectrum includes:

[0141] S1221a: Obtain all delayed coincidence events in the downsampled response line based on the delayed coincidence window; split each delayed coincidence event into two corresponding delayed single events.

[0142] In some embodiments, the delayed coincidence window includes a delayed coincidence time window. Specifically, when the signal is delayed, the corresponding coincidence event is called the delayed coincidence event, and the corresponding time window is called the delayed coincidence time window. The delayed coincidence time window is specifically obtained based on prior information, for example, it can be set to 2ns-10ns. For details on how the delayed coincidence event is obtained, please refer to the prior art, which will not be elaborated here.

[0143] It should be understood that a delayed coincidence event comprises two delayed single events. Each delayed coincidence event included in a downsampled response line is decomposed into two corresponding delayed single events, each containing location information, energy information, and time information.

[0144] S1221b divides a delayed single event into two parts based on the location information of the delayed single event, with each part corresponding to a one-dimensional delayed energy spectrum.

[0145] In some specific embodiments, corresponding to step S132, the location information of the delayed single event includes the location information of the downsampling detection module that detected the delayed single event.

[0146] S1221c, based on the two parts of delayed single events, two one-dimensional delayed energy spectra are obtained; wherein, the location information of the delayed single event includes the location information of the downsampling detection module that detected the delayed single event.

[0147] In some specific examples, based on the downsampling detection module information corresponding to the delayed single event, the two one-dimensional delayed energy spectra corresponding to the downsampling response line can be obtained.

[0148] In some specific embodiments, the fuzzy response matrix is ​​obtained. include:

[0149] S1221a', based on obtaining prior detection data from a prosthesis.

[0150] In some embodiments, the prosthesis may employ a point source or a line source, but is not limited thereto.

[0151] S1221b', obtains the a priori downsampling response line based on prior detection data.

[0152] In some embodiments, corresponding to step S110, step S1221b' includes:

[0153] Obtain the a priori coincidence response line based on prior detection data;

[0154] The prior conformance response line is downsampled to obtain the prior downsampled response line.

[0155] In some specific examples, how to obtain the prior coincidence response line and the prior downsampled response line can be referred to step S110, which will not be elaborated here.

[0156] S1221c' divides all prior coincidence events corresponding to the prior downsampled response line into prior immediate coincidence events and prior delayed coincidence events.

[0157] In some embodiments, prior instantaneous compliance events are obtained based on an instantaneous compliance time window.

[0158] In some specific examples, the instantaneous coincidence time window corresponding to the a priori instantaneous coincidence event is set to 2ns-10ns. When the instantaneous coincidence time window exceeds 10ns, the background signal in the subsequently obtained a priori one-dimensional instantaneous coincidence energy spectrum may be greater than the background signal in the delayed coincidence energy spectrum, resulting in a failure to cancel each other out and causing corresponding errors that interfere with the accuracy of the scattering correction results. Setting the instantaneous coincidence time window within the range of 2ns-10ns helps to eliminate the interference of the background signal in the subsequently obtained a priori one-dimensional instantaneous coincidence energy spectrum.

[0159] In some embodiments, corresponding to step S1221a, the delay coincidence time window for the priori delayed coincidence event is set to 2ns-10ns. Specifically, the range of the delayed coincidence time window and the immediate coincidence time window may be the same or different.

[0160] S1221d' obtains the prior one-dimensional instantaneous energy spectrum based on the prior instantaneous coincidence event corresponding to the downsampled response line; and obtains the prior one-dimensional delayed energy spectrum based on the prior delayed coincidence event corresponding to the downsampled response line.

[0161] For details on how to obtain the prior one-dimensional instantaneous energy spectrum and the prior one-dimensional delayed energy spectrum based on the downsampling response line, please refer to steps S1211-S1213 and S1221a-S1221c, which will not be repeated here.

[0162] S1221e': Obtain the difference between the prior one-dimensional instantaneous energy spectrum and the prior one-dimensional delayed energy spectrum, normalize the difference, and obtain the one-dimensional intermediate energy spectrum. .

[0163] In some embodiments, the difference between the prior one-dimensional instantaneous energy spectrum and the prior one-dimensional delayed energy spectrum is normalized, as detailed in the prior art, which will not be repeated here.

[0164] S1221f', based on a one-dimensional intermediate energy spectrum Obtaining the fuzzy response matrix .

[0165] In some embodiments, based on a one-dimensional intermediate energy spectrum Obtaining the fuzzy response matrix For details, please refer to existing technologies, which will not be elaborated here.

[0166] S1222, based on the two obtained one-dimensional detector energy spectra and their corresponding relational functions, the maximum likelihood-expectation maximization iterative algorithm and the initial iterative values ​​corresponding to the obtained downsampled response lines are used to obtain the two one-dimensional gamma photon energy spectra corresponding to the two one-dimensional detector energy spectra.

[0167] In some embodiments, a one-dimensional gamma photon energy spectrum refers to the energy spectrum composed of the energy of the gamma photons themselves.

[0168] In some embodiments, step S1222 includes:

[0169] S122210, based on the correspondence function between the one-dimensional detector energy spectrum and the one-dimensional gamma photon energy spectrum, obtains the iterative formula of the maximum likelihood-expectation maximization iterative algorithm.

[0170] In some embodiments, the iterative formula for the maximum likelihood-expectation maximization iterative algorithm is:

[0171] (2)

[0172] in, In Characterizes the one-dimensional probe energy spectrum obtained from scanning the target object, corresponding to a single downsampling response line. Characterizing the first in the energy spectrum A vertical bar area (i.e., "bin") , Characterizing the first in the energy spectrum respectively The, the A vertical strip area, It is the number of iterations. and In This is the fuzzy response matrix corresponding to the downsampled response line. The fuzzy response matrix corresponding to this downsampled response line is the first... line, number The value of the column, The fuzzy response matrix corresponding to this downsampled response line is the first... line, number The value of the column is the fuzzy response matrix corresponding to the downsampled response line; This indicates the one-dimensional delayed energy spectrum corresponding to the random coincidence events included in the downsampled response line; For the first The +1st iteration obtained the first The one-dimensional gamma photon energy spectrum corresponding to each vertical bar region; , Respectively characterize the first The energy spectrum is obtained in the nth iteration. The, the The vertical bar regions correspond to the one-dimensional gamma photon energy spectrum.

[0173] Since the MLEM iterative algorithm does not have a penalty term constraint in its iterative formula, the selection of the initial iteration value in equation (2) is very important.

[0174] In some embodiments, obtaining the initial iteration value includes:

[0175] S122211, obtain the global initial scattered photon energy spectrum.

[0176] In some embodiments, "global" refers to the object obtained using all the coincident response lines corresponding to the probe data. For example, the global initial scattered photon spectrum refers to the initial scattered photon spectrum obtained using all the coincident response lines corresponding to the probe data.

[0177] In some embodiments, step S132211 includes:

[0178] S122211a, Obtain the coincidence response line based on the probe data.

[0179] In some specific embodiments, step S122211a can refer to step S112, and will not be repeated here.

[0180] S122211b divides all coincident events corresponding to all coincident response lines into instant coincident events and random coincident events.

[0181] In some embodiments, the specific operation of step S122211b can refer to the prior art and will not be elaborated herein.

[0182] S122211c, obtaining a global one-dimensional prompt energy spectrum based on prompt coincidence events; obtaining a global one-dimensional delayed energy spectrum based on delayed coincidence events.

[0183] In some embodiments, the specific operation of S122211c can be analogously referred to step 1221d’ and will not be elaborated herein.

[0184] S122211d, subtracting the global one-dimensional delayed energy spectrum corresponding to the delayed coincidence events from the global one-dimensional prompt energy spectrum corresponding to all prompt coincidence events on the response lines to obtain a global one-dimensional undelayed energy spectrum.

[0185] In some embodiments, the global one-dimensional prompt energy spectrum refers to a one-dimensional prompt energy spectrum obtained by using all coincidence response lines corresponding to the detection data, and the global one-dimensional delayed energy spectrum refers to a one-dimensional delayed energy spectrum obtained by using all coincidence response lines corresponding to the detection data.

[0186] S122211e, obtaining an objective function of the global initial scattered photon energy spectrum based on the global one-dimensional undelayed energy spectrum.

[0187] In some specific embodiments, the objective function of the global initial scattered photon energy spectrum is:

[0188] (3)

[0189] where, where, in is the global initial scattered photon energy spectrum; represents the th vertical bar region in the energy spectrum; in is the global one-dimensional undelayed energy spectrum, obtained based on the target object, in is the global one-dimensional intermediate energy spectrum obtained by scanning the entire PET system ; the global one-dimensional intermediate energy spectrum is obtained based on the phantom; and are the lower threshold and upper threshold of a set high-energy window (specifically set based on experience, usually 511 < hel < heu, and heu generally takes values such as 600, 650 or 700, etc.), is the stretching coefficient.

[0190] In some embodiments, the global one-dimensional intermediate energy spectrum The acquisition can be referred to in step S1221e'. Specifically, the one-dimensional intermediate energy spectrum obtained by using all response lines corresponding to the detection data obtained based on the target object is the global one-dimensional intermediate energy spectrum. .

[0191] It should be understood that, The high-energy window contains almost entirely unscattered photons, with very few scattered photons. The total number of photons passing through the high-energy window is related to... Stretching allows estimation of the unscattered photon energy spectrum within the complete energy window. Subtracting the estimated unscattered photon energy spectrum from the global one-dimensional undelayed energy spectrum yields the global initial scattered photon energy spectrum.

[0192] It should be understood that the prerequisite for the success of equation (3) is The system contains a sufficient number of photons. Based on prior information, the number of photons on all coincident response lines is sufficient. The number of photons on a single response line or a single downsampled response line is generally insufficient. If only the number of photons on a single response line or a single downsampled response line is used... It will be affected by statistical noise. If the energy spectrum of a single response line or a single downsampled response line is obtained directly based on equation (3), it will be severely affected by statistical noise, resulting in a lower energy spectrum. The accuracy is very poor. In the embodiments of this application, due to the global one-dimensional undelayed energy spectrum... Based on all the corresponding response lines of the probe data. The number of photons in the sample is usually sufficient to improve the acquisition rate. The accuracy.

[0193] In some specific examples, as can be seen from the above, Global one-dimensional intermediate energy spectrum obtained from scanning the entire PET system , obtain For obtaining the energy spectrum, please refer to the steps below. The acquisition of.

[0194] In some embodiments, the instantaneous energy spectrum obtained based on all response lines corresponding to the prior detection data can be called the global prior instantaneous energy spectrum, and the obtained delayed energy spectrum can be called the global prior delayed energy spectrum.

[0195] S122212 performs deblurring on the global initial scattered photon energy spectrum to obtain the global intermediate initial iteration value.

[0196] In some embodiments, the global intermediate initial iteration value refers to the intermediate initial iteration value obtained based on all response lines corresponding to the probe data.

[0197] In some embodiments, step S122212 includes:

[0198] S122212a, based on the maximum likelihood-expectation maximization iterative algorithm, obtains the defuzzing iterative formula.

[0199] In some specific embodiments, the deblurring iterative formula is as follows:

[0200] (4)

[0201] in, In The characterization is based on the global one-dimensional probe energy spectrum obtained by scanning the target object. Characterizing the first in the energy spectrum A vertical bar area (i.e., "bin") , Characterizing the first in the energy spectrum respectively The, the A vertical strip area, It is the number of iterations. , In The global fuzzy response matrix, The first fuzzy response matrix is ​​the global fuzzy response matrix. line, number The value of the column, The first fuzzy response matrix is ​​the global fuzzy response matrix. line, number The value of the column; This represents the global one-dimensional delayed energy spectrum corresponding to all random coincidence events; For the first The +1st iteration obtained the first The global one-dimensional gamma photon energy spectrum corresponding to each vertical bar region; , Respectively characterize the first The energy spectrum obtained in the nth iteration is the first The, the The global one-dimensional gamma photon energy spectrum corresponding to each vertical bar region;

[0202] (5)

[0203] in, Characterized by very large constants, such as, but not limited to, 1,000,000.

[0204] In this embodiment of the application, equation (4) is introduced by... This can make the formula It only includes scattered photons, meaning that even after blurring, only the scattered portion has a value. The part corresponding to time is the scattering part.

[0205] In some embodiments, the global fuzzy response matrix refers to the fuzzy response matrix based on all response lines corresponding to the probe data.

[0206] In some specific examples, the global fuzzy response matrix is ​​obtained. include:

[0207] Prior detection data is obtained based on spurs (such as point sources or line sources);

[0208] All prior coincident response lines are obtained based on prior detection data;

[0209] All prior compliance events corresponding to all prior compliance response lines are divided into global prior immediate compliance events and global prior delayed compliance events.

[0210] Obtain the global prior one-dimensional instantaneous energy spectrum based on global prior instantaneous coincidence events; obtain the global prior one-dimensional delayed energy spectrum based on global prior delayed coincidence events;

[0211] Obtain the difference between the global prior instantaneous energy spectrum and the global prior delayed energy spectrum;

[0212] The difference is normalized to obtain the global prior intermediate energy spectrum. ;

[0213] Based on global prior intermediate energy spectrum Obtain the global fuzzy response matrix .

[0214] In some embodiments, the global prior intermediate energy spectrum The acquisition can be referenced in S1221e'. Specifically, the one-dimensional intermediate energy spectrum obtained by using all response lines corresponding to the prior probe data acquired based on the spur is the global prior intermediate energy spectrum. .

[0215] In some specific embodiments, how to normalize this difference to obtain the global prior intermediate energy spectrum? ; and how to base on global prior intermediate energy spectrum Obtain the global fuzzy response matrix For details, please refer to existing technologies, which will not be elaborated here.

[0216] S122212b sets the prior initial iteration value and the number of iterations. Iterates through the defuzzification iterative formula until the number of iterations is reached, and obtains the scattering part of the global initial energy spectrum.

[0217] In some specific examples, it is possible to let Substitute into equation (4) and iterate several times, for example, including but not limited to 50-500 times, to obtain the scattering part of the global initial energy spectrum. .

[0218] S122212c estimates the unscattered portion of the global initial energy spectrum based on the scattered portion of the global initial energy spectrum.

[0219] In some embodiments, after obtaining In the case of estimating the unscattered portion of the global initial energy spectrum, i.e., in order to obtain The value of the global initial energy spectrum can be estimated based on an estimation function, which is:

[0220] (6)

[0221] in, Characterization The estimated function segment of the global intermediate initial iterative value function corresponding to the time. This represents the ratio of unscattered photons to scattered photons; here... The global fuzzy response matrix, The first fuzzy response matrix is ​​the global fuzzy response matrix. line, number The value of the column; This is a hyperparameter.

[0222] In some specific examples, the ratio of unscattered photons to scattered photons Specifically, it can be estimated using the following formula:

[0223] (7)

[0224] in, To express summation, and As mentioned above , .

[0225] S122212d obtains the global intermediate initial iteration value based on the scattered part of the global initial energy spectrum and the unscattered part of the global initial energy spectrum.

[0226] In some specific embodiments, since the PET system mainly uses gamma photons, and the energy of gamma photons is 511 keV, when the energy is greater than 511 keV, the corresponding global initial energy spectrum value is 0. Based on this, the global intermediate initial iterative value function is: :

[0227] (8)

[0228] in, It's a hyperparameter. Compared with the above They have the same meaning; both refer to the global fuzzy response matrix. This represents the ratio of unscattered photons to scattered photons. This is a hyperparameter.

[0229] In this embodiment of the application, the result obtained by equation (3) is... There may be errors because equation (3) assumes... There are no scattered photons in the high-energy window, in fact Scattered photons may still exist in the high-energy window, leading to... Increased by the elongation factor Decrease, then based on equation (7) obtained It might be overestimated; this can be addressed through hyperparameters. Balance. Specifically, by... Set a number less than 1 to balance. The impact was overestimated. Experiments revealed... A value between 0.3 and 0.5 generally yields good results and requires minimal adjustment. On the other hand, The initial global scattered photon energy spectrum is obtained based on all response lines acquired from the target object detection. Response lines that do not cross the target object are considered all scattering events. Response lines that cross the target object contain both scattering events and true events (i.e., non-scattering events). Therefore, the proportion of scattering events crossing the target object's response lines is lower than that of scattering events across all response lines. This is determined through hyperparameters. Balancing can also yield a more accurate picture of the unscattered portion.

[0230] S122213 stretches the global intermediate initial iteration value to obtain the initial iteration value corresponding to each downsampled response line.

[0231] In some embodiments, step S122212 includes:

[0232] The global intermediate initial iteration value is stretched using a stretching function to obtain the initial iteration value corresponding to each downsampled response line.

[0233] In some specific examples, the stretching function is:

[0234] (9)

[0235] in, The initial iteration value, To express summation, The global fuzzy response matrix, This is the global intermediate initial iteration value; This represents the one-dimensional probe energy spectrum obtained from target object scanning, corresponding to a single downsampling response line. This represents the one-dimensional delayed energy spectrum corresponding to the random coincidence events included in a single response line.

[0236] In this embodiment, by stretching, an initial iteration value matching the number of single events included in the downsampling response line can be obtained. .

[0237] S122220, Set the number of iterations, and iterate according to the obtained initial iteration value using the maximum likelihood-expectation maximization iteration algorithm formula until the set number of iterations is reached, to obtain the two one-dimensional gamma photon energy spectra corresponding to the two one-dimensional detector energy spectra.

[0238] In some specific embodiments, the number of iterations can be set according to specific needs, such as 20-60 iterations. Generally, a good one-dimensional gamma photon spectrum can be obtained within 60 iterations, while the effect will deteriorate if the number of iterations is too high.

[0239] S1223, based on the two one-dimensional gamma photon energy spectra, estimate the probability density functions of the two scattered photons and the probability density functions of the two unscattered photons corresponding to the downsampled response lines.

[0240] In some embodiments, step S1223 includes:

[0241] Step S12231: Divide the one-dimensional gamma photon energy spectrum into a scattered part and an unscattered part.

[0242] In some embodiments, the obtained one-dimensional gamma photon energy spectrum can be divided based on the energy value of the gamma photon when it is not scattered, with the portion below the energy value being the scattered portion and the portion equal to the energy value being the unscattered portion.

[0243] In some specific examples, it can be based on the energy of the gamma photons that have not undergone scattering. The obtained one-dimensional gamma photon energy spectrum is Divided into scattering parts and unscattered portion As shown below:

[0244] (10)

[0245] Step S12232: Scattering portion of the two one-dimensional gamma photon energy spectra and unscattered portion Perform recovery fuzzy response processing to obtain the two scattered parts and two unscattered parts of the two one-dimensional probe energy spectra corresponding to the downsampled response lines.

[0246] In some embodiments, the information obtained based on equation (7) and Calculate the scattered and unscattered portions of the one-dimensional detector energy spectrum:

[0247] ;

[0248] ;

[0249] in, Characterizing the scattering component of a one-dimensional probe energy spectrum, Characterizing the unscattered portion of the one-dimensional probe energy spectrum, This is the fuzzy response matrix corresponding to the downsampled response line.

[0250] Step S12233: Normalize the two scattered parts and two unscattered parts of the two one-dimensional probe energy spectra. The obtained normalized values ​​are used to estimate the probability density functions of the two scattered photons and the two unscattered photons corresponding to the downsampled response lines.

[0251] In some embodiments, the two-dimensional instantaneous conformance energy histogram and the two-dimensional delayed conformance energy histogram corresponding to each downsampling response line can be obtained by referring to the prior art, and will not be elaborated here.

[0252] In some embodiments, after obtaining the two-dimensional instantaneous coincidence energy histogram, the two-dimensional delayed coincidence energy histogram, the probability density function of scattered photons, and the probability density function of unscattered photons corresponding to each downsampled response line, the estimated target function can be obtained.

[0253] In some specific embodiments, the objective function is estimated as follows:

[0254] (11)

[0255] in, These represent the number of true matching events, which do not require further processing. Indicates the number of scattering coincidence events. , is the unknown quantity to be determined; coefficient The calculation method is as follows:

[0256]

[0257] (12)

[0258] in, and It is the probability density function of unscattered photons corresponding to the two detection modules in the downsampling response line. and It is the probability density function of scattered photons corresponding to the two detection modules in the downsampling response line.

[0259] S130, Solve the estimated objective function to obtain the number of scattering coincidence events corresponding to each downsampled response line.

[0260] In some embodiments, the calculation formula (11) obtains The value of is the number of scattering coincidence events corresponding to the downsampled response line.

[0261] In the embodiments of this application, the unscattered photon probability density function in the objective function is estimated. and Not necessarily the same, scattered photon probability density function and They are not necessarily the same, but are determined specifically based on the two detection modules corresponding to the downsampled response line. Based on this, the number of scattering coincidence events corresponding to the downsampled response line is more accurate.

[0262] S140, perform upsampling processing on the downsampled response line to obtain the upsampled response line.

[0263] In some embodiments, upsampling the downsampled response line includes:

[0264] The downsampled response lines are processed using bilinear interpolation, 4D linear interpolation, or 5D linear interpolation to obtain the corresponding upsampled response lines. Specific procedures can be found in existing techniques and will not be elaborated upon here.

[0265] S150, calculate the number of scattering coincidence events corresponding to each upsampled response line.

[0266] In some embodiments, the scintillation crystals included in the detection module of the PET system are referred to as upsampling crystals, and the set of scintillation crystals corresponding to the downsampling response lines obtained by downsampling processing is referred to as downsampling crystals, for example, such as Figure 2 As shown, the downsampling crystal to which the upsampling crystal belongs is denoted as the "downsampling center crystal". The downsampling crystal that is closest to the upsampling crystal along its axis is denoted as the "downsampling axis adjacent crystal". The downsampling crystal that is radially closest to the upsampling crystal is denoted as the "downsampling radially adjacent crystal". The downsampling crystal that is diagonally closest to the upsampling crystal is denoted as the "downsampling relative crystal". ".

[0267] like Figure 2 As shown, let the axial length of the downsampling crystal be... The radial length is An upsampling response line has two upsampling crystals, distinguished by the superscripts "i" and "j". Taking the scintillation crystal labeled "i" as an example, the center distance of this upsampling crystal is denoted as "center crystal". The radial and axial distances of the center of “” are respectively , The weights of the four downsampling crystals corresponding to one are as follows:

[0268] Downsampling center crystal ": ;

[0269] Downsampling Axial Adjacent Crystal ": ;

[0270] Downsampling radial adjacent crystal ": ;

[0271] Downsampling relative crystal ": ;

[0272] Similarly, for the four downsampling crystals corresponding to the upsampling crystal with the superscript "j", we also have:

[0273] ;

[0274] ;

[0275] ;

[0276] .

[0277] In some specific embodiments, when processing the downsampled response line using the 4D linear interpolation method, the following formula is used to calculate the number of scattering coincidence events corresponding to the upsampled response line:

[0278]

[0279] in, Characterized by the upsampling crystal after 4D linear interpolation and The number of scattering coincidence events that constitute the upsampled response line; Indicates upsampling crystal The set of the corresponding downsampling center crystal, downsampling axial adjacent crystal, downsampling radial adjacent crystal, and downsampling relative crystal; and This indicates the crystal number corresponding to the downsampled response line; Characterization of downsampling crystal and The number of scattering coincidence events on the downsampled response line; This indicates that for upsampling crystals Regarding downsampling crystals The weights; This indicates that for upsampling crystals Regarding downsampling crystals The weights; The normalization factor is characterized as follows:

[0280]

[0281] in The number of upsampled response lines contained in a downsampled response line.

[0282] In some other embodiments, step S150 includes:

[0283] Calculate the ratio of the total number of scattering coincidence events to the total number of coincidence events on each downsampled response line;

[0284] The number of scattering coincidence events corresponding to each upsampled response line is calculated based on the number of coincidence events on each upsampled response line contained in each downsampled response line and the aforementioned proportion.

[0285] In some embodiments, a scheme combining the maximum likelihood-expectation-maximization iterative algorithm with the moment estimation method is adopted. Based on the maximum likelihood-expectation-maximization iterative algorithm, the moment estimation method is used to obtain the estimated objective function corresponding to each downsampled response line. On the one hand, the maximum likelihood-expectation-maximization iterative algorithm (MLEM) performs scattering correction without relying on the activity image and attenuation image, making scattering correction easy to implement, and the method does not require hyperparameters. Therefore, it is no longer necessary to adjust hyperparameters. Set within a defined range, i.e. subject to hyperparameters The impact is minimal. Compared to existing one-step-later iterative algorithms like OSL-EDR (One Step Later eliminate detector response) and Optimization transfer eliminate detector response (OT-EDR), both OSL-EDR and OT-EDR require the selection of appropriate hyperparameters. Only then can better results be achieved, and the optimal Some preliminary experiments are needed for adjustments, and different sizes of prostheses require different approaches. The embodiments of this application use the MLEM algorithm for scattering correction, which requires almost no adjustment to hyperparameters. Adjustments are made to make scattering correction more convenient; on the other hand, the probability density functions of unscattered photons used in the estimated objective function are not necessarily the same, and the probability density functions of scattered photons are not necessarily the same either. Instead, they are determined specifically based on the two detection modules corresponding to the downsampled response line, and the number of scattering coincidence events corresponding to the downsampled response line is more accurate.

[0286] As can be seen from the above, some embodiments of this application consider various scattering scenarios, such as random scattering and external scattering events that do not penetrate the target object, resulting in higher accuracy of the scattering correction results and thus improving the quality of the subsequent reconstructed image. Furthermore, compared with existing double scattering simulation methods and Monte Carlo simulation methods, the scattering correction method according to this application has the advantages of simpler calculation process and lower computational load.

[0287] Figure 3 This is an exemplary flowchart of a scattering correction method 200 according to some embodiments of this application.

[0288] See also Figure 3 The scattering correction method 200 may include the following steps:

[0289] S210, acquire response lines based on probe data;

[0290] S220, based on the maximum likelihood-expectation-maximization iterative algorithm, uses the moment estimation method to obtain the estimated objective function corresponding to each response line. The estimated objective function is obtained based on the two-dimensional instantaneous coincidence energy histogram, the two-dimensional delayed coincidence energy histogram, the probability density function of scattered photons and the probability density function of unscattered photons corresponding to each response line.

[0291] S230, Solve the estimated objective function to obtain the number of scattering coincidence events corresponding to each response line.

[0292] Unlike the scattering correction method 100, which obtains a response line based on probe data, downsamples the response line to obtain a downsampled response line, uses a maximum likelihood-expectation-maximization iterative algorithm combined with a matrix estimation method to obtain the number of scattering coincidence events corresponding to the downsampled response line, and then upsamples the downsampled response line to obtain the number of scattering coincidence events for each response line, this embodiment uses a maximum likelihood-expectation-maximization iterative algorithm combined with a matrix estimation method to directly process the response line obtained based on the probe data to obtain the number of scattering coincidence events corresponding to each response line.

[0293] In the embodiments of this application, the scattering correction method 200 may selectively combine features of the scattering correction method 100 or other methods, or vice versa.

[0294] Figure 4 This is an exemplary flowchart of an image reconstruction method according to some embodiments of this application.

[0295] See also Figure 4 The image reconstruction method 300 may include the following steps:

[0296] S310, the scattering correction method described in the above embodiments obtains the number of scattering coincidence events corresponding to each response line;

[0297] S320 corrects the scattering events based on the number of scattering coincidence events to obtain a reconstructed image.

[0298] In some specific embodiments, an iterative image reconstruction algorithm can be used to reconstruct the image based on the number of scattering coincidence events, thereby obtaining the reconstructed image.

[0299] Step S320 can be found in existing technology and will not be described in detail here.

[0300] Figure 5 This is an exemplary block diagram of a scattering correction device according to some embodiments of this application. Figure 5 As shown, the scattering correction device 400 may include:

[0301] The downsampling response line acquisition module 410 is configured to acquire the downsampling response line based on the probe data;

[0302] The estimation module 420 is configured to obtain the estimation objective function corresponding to each downsampled response line based on the maximum likelihood-expectation-maximization iterative algorithm and the moment estimation method. The estimation objective function is obtained based on the two-dimensional instantaneous coincidence energy histogram, the two-dimensional delayed coincidence energy histogram, the probability density function of scattered photons and the probability density function of unscattered photons corresponding to each downsampled response line.

[0303] The scattering coincidence event acquisition module 430 is configured to solve the estimated objective function and acquire the number of scattering coincidence events corresponding to each downsampled response line.

[0304] Upsampling module 440 is configured to perform upsampling processing on downsampling response line to obtain upsampling response line;

[0305] The scattering coincidence event count calculation module 450 is configured to calculate the number of scattering coincidence events corresponding to each upsampled response line.

[0306] In some embodiments, the estimation module 420 includes:

[0307] The energy spectrum acquisition module is configured to acquire a two-dimensional instantaneous coincidence energy histogram, a two-dimensional delayed coincidence energy histogram, and two one-dimensional probe energy spectra for each downsampled response line based on each coincidence event corresponding to each downsampled response line.

[0308] The probability density acquisition module is configured to obtain the probability density function of scattered photons and the probability density function of unscattered photons corresponding to the downsampled response lines based on two one-dimensional probe energy spectra and using the maximum likelihood-expectation-maximization iterative algorithm.

[0309] In some embodiments, the estimation module 420 further includes:

[0310] The correspondence acquisition module is configured to acquire the correspondence function between the one-dimensional detector energy spectrum and the one-dimensional gamma photon energy spectrum;

[0311] The gamma photon energy spectrum acquisition module is configured to acquire two one-dimensional gamma photon energy spectra corresponding to the two one-dimensional detector energy spectra based on the acquired two one-dimensional detector energy spectra and the corresponding relational function, using the maximum likelihood-expectation-maximization iterative algorithm and the initial iterative value corresponding to the acquired downsampled response line.

[0312] The probability density estimation module is configured to estimate the probability density functions of the two scattered photons and the two unscattered photons corresponding to the downsampled response lines based on the two one-dimensional gamma photon energy spectra.

[0313] In some specific embodiments, the probability density estimation module includes:

[0314] The partitioning module is configured to divide the one-dimensional gamma photon energy spectrum into a scattering part and an unscattered part.

[0315] The fuzzy response recovery module is configured to perform fuzzy response recovery processing on the scattered and unscattered parts of the two one-dimensional gamma photon energy spectra to obtain the two scattered and two unscattered parts corresponding to the two one-dimensional probe energy spectra of the corresponding downsampled response lines;

[0316] The density estimation module is configured to normalize the two scattered and two unscattered portions corresponding to the two one-dimensional probe energy spectra, and estimate the probability density functions of the two scattered photons and the two unscattered photons corresponding to the downsampled response lines based on the obtained normalized values.

[0317] In some specific embodiments, the gamma photon energy spectrum acquisition module includes a maximum likelihood-expectation-maximization iterative algorithm iterative formula acquisition module, which is configured to acquire the maximum likelihood-expectation-maximization iterative algorithm iterative formula based on the correspondence function between the one-dimensional probe energy spectrum and the one-dimensional gamma photon energy spectrum.

[0318] The initial iteration value acquisition module is configured to acquire the initial iteration value.

[0319] The iteration module is configured to set iteration conditions and iterate through the maximum likelihood-expectation-maximization iteration algorithm formula based on the obtained initial iteration value until the set iteration conditions are met, thereby obtaining the two one-dimensional gamma photon energy spectra corresponding to the two one-dimensional detector energy spectra.

[0320] In some specific examples, the initial iteration value acquisition module includes a global initial scattered photon energy spectrum, which is configured to acquire the global initial scattered photon energy spectrum; an intermediate initial iteration value acquisition module, which is configured to deblur the global initial scattered photon energy spectrum to acquire the global intermediate initial iteration value; and a stretching module, which stretches the global intermediate initial iteration value to acquire the initial iteration value corresponding to each downsampled response line.

[0321] In the embodiments of this application, the scattering correction device 400 may selectively incorporate features of the scattering correction method 100 or 200 or other methods, or vice versa.

[0322] Figure 6 This is an exemplary block diagram of a scattering correction device according to some embodiments of this application. Figure 6 As shown, the scattering correction device 500 may include:

[0323] The response line acquisition module 510 is configured to acquire the response line based on the probe data;

[0324] The estimation module 520 is configured to obtain the estimation objective function corresponding to each response line based on the maximum likelihood-expectation maximization iterative algorithm and the moment estimation method. The estimation objective function is obtained based on the two-dimensional instantaneous coincidence energy histogram, the two-dimensional delayed coincidence energy histogram, the probability density function of scattered photons and the probability density function of unscattered photons corresponding to each response line.

[0325] The scattering coincidence event acquisition module 530 is configured to solve the estimated objective function and acquire the number of scattering coincidence events corresponding to each response line.

[0326] Unlike the embodiment of scattering correction device 400, which obtains a response line based on probe data, downsamples the response line to obtain a downsampled response line, obtains the number of scattering coincidence events corresponding to the downsampled response line based on the maximum likelihood-expectation-maximization iterative algorithm combined with a matrix estimation method, and then upsamples the downsampled response line to obtain the number of scattering coincidence events for each response line, this embodiment directly processes the response line obtained based on probe data using the maximum likelihood-expectation-maximization iterative algorithm combined with a matrix estimation method to obtain the number of scattering coincidence events corresponding to each response line.

[0327] In the embodiments of this application, the scattering correction device 500 can be used to implement the scattering correction method 200 or the method described in other embodiments herein, and can be selectively combined with the features of the scattering correction device 400 or other devices, or selectively combined with the features of the scattering correction method 100 or 200 or other methods, and vice versa.

[0328] Figure 7 These are exemplary block diagrams of an image reconstruction apparatus according to some embodiments of this application. Figure 7 As shown, the image reconstruction apparatus 600 may include:

[0329] The scattering coincidence event acquisition module 610 is configured to acquire the number of scattering coincidence events corresponding to each response line based on the scattering correction method described in any of the above embodiments.

[0330] The reconstruction module 620 is configured to correct scattering events based on the number of scattering coincidence events and obtain a reconstructed image.

[0331] In the embodiments of this application, the image reconstruction apparatus 600 can be used to implement the image reconstruction method 300 or the methods described in other embodiments herein, and can selectively combine features of the image reconstruction method 300 or other methods, or vice versa.

[0332] In some embodiments of this application, the image reconstruction apparatus 600 may also include components or features of the scattering correction apparatus 400, 500 in a manner that is not contradictory, and vice versa.

[0333] In some embodiments, this application also provides a digitizing device, which includes the apparatus described in any one of the above embodiments.

[0334] In some embodiments, this application also provides a digital device that may include a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, can implement the steps of the method described in any of the above embodiments.

[0335] Although not shown, some embodiments also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method described in any of the above embodiments. The computer program includes various program modules / units constituting the apparatus according to embodiments of this application, and when executed, the computer program comprised of these program modules / units is capable of performing functions corresponding to the various steps in the methods described in the above embodiments. The computer program can also run on electronic devices as described in embodiments of this application.

[0336] The basic concepts have been described herein. It is obvious that the detailed disclosure above is merely illustrative and does not constitute a limitation of this application. Although not explicitly stated herein, various modifications, improvements, and corrections may be made to this application by those skilled in the art. Such modifications, improvements, and corrections are suggested in this application and therefore remain within the spirit and scope of the exemplary embodiments of this application.

[0337] Furthermore, this application uses specific terms to describe its embodiments. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic related to at least one embodiment of this application. Therefore, it should be emphasized and noted that "an embodiment," "one embodiment," or "an alternative embodiment" mentioned twice or more in different locations in this application do not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this application can be appropriately combined.

[0338] Furthermore, those skilled in the art will understand that aspects of this application can be described and illustrated through several patentable types or situations, including any new and useful combination of processes, machines, products, or substances, or any new and useful improvements thereof. Accordingly, aspects of this application can be implemented entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. All of the above hardware or software may be referred to as a “data block,” “module,” “engine,” “unit,” “component,” or “system.” Furthermore, aspects of this application may manifest as a computer product located on one or more computer-readable media, the product including computer-readable program code.

[0339] Computer storage media may contain a propagated data signal containing computer program code, for example, on baseband or as part of a carrier wave. This propagated signal may take various forms, including electromagnetic, optical, and suitable combinations thereof. Computer storage media can be any computer-readable medium other than a computer-readable storage medium, which can be connected to an instruction execution system, apparatus, or device to enable communication, propagation, or transmission of a program for use. The program code located on the computer storage medium can be propagated through any suitable medium, including radio, cable, fiber optic cable, RF, or similar media, or any combination of the above media.

[0340] The computer program code required for the operation of each part of this application can be written in any one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc., conventional procedural programming languages ​​such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages ​​such as Python, Ruby, and Groovy, or other programming languages. This program code can run entirely on the user's computer, or as a standalone software package on the user's computer, or partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any network, such as a local area network (LAN) or wide area network (WAN), or connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or used as a service such as Software as a Service (SaaS).

[0341] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this application are not intended to limit the order of the processes and methods of this application. Although the foregoing disclosure has discussed some currently considered useful embodiments of the invention through various examples, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the substance and scope of the embodiments of this application. For example, while the system components described above can be implemented using hardware devices, they can also be implemented solely through software solutions, such as installing the described system on existing servers or mobile devices.

[0342] Similarly, it should be noted that, in order to simplify the description of the present application and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of the embodiments of the present application sometimes combines multiple features into a single embodiment, drawing, or description thereof. However, this disclosure method does not imply that the subject matter of the application requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of the single embodiments disclosed above.

[0343] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters should take into account specified significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of scope in some embodiments of this application are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

[0344] For each patent, patent application, patent application publication, and other material such as articles, books, specifications, publications, and documents referenced in this application, the entire contents of that material are incorporated herein by reference. This excludes historical application documents that are inconsistent with or conflict with the content of this application, as well as documents that limit the broadest scope of the claims in this application (currently or subsequently appended to this application). It should be noted that if there is any inconsistency or conflict between the descriptions, definitions, and / or terminology used in the supplementary materials of this application and the content of this application, the descriptions, definitions, and / or terminology used in this application shall prevail.

[0345] Finally, it should be noted that the above descriptions are merely exemplary embodiments of this application and are not intended to limit this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A scattering correction method, characterized in that, The scattering correction method includes: Obtain the downsampling response line based on the probe data; Based on the maximum likelihood-expectation-maximization iterative algorithm, the moment estimation method is used to obtain the estimated objective function corresponding to each downsampled response line. The estimated objective function is obtained based on the two-dimensional instantaneous coincidence energy histogram, the two-dimensional delayed coincidence energy histogram, the probability density function of scattered photons and the probability density function of unscattered photons corresponding to each downsampled response line. Solve the estimated objective function to obtain the number of scattering coincidence events corresponding to each downsampled response line; The downsampled response line is upsampled to obtain the upsampled response line; Calculate the number of scattering coincidence events corresponding to each upsampled response line; The estimated objective function is: ， in, These represent the number of true matching events, which do not require further processing. Indicates the number of scattering coincidence events. , is the unknown quantity to be determined; coefficient The calculation method is as follows: ， ， in, and It is the probability density function of unscattered photons corresponding to the two detection modules in the downsampling response line. and It is the probability density function of scattered photons corresponding to the two detection modules in the downsampling response line; Among them, the calculation obtained The value represents the number of scattering coincidence events corresponding to the downsampled response line.

2. The scattering correction method according to claim 1, characterized in that, The downsampling response line is acquired based on the probe data and includes: Obtain the response line based on the probe data; The coincident response line is downsampled to obtain a downsampled response line.

3. The scattering correction method according to claim 2, characterized in that, The detection data is obtained based on the target object.

4. The scattering correction method according to claim 1, characterized in that, Based on the maximum likelihood-expectation-maximization iterative algorithm, the estimated objective function corresponding to each downsampled response line is obtained using the moment estimation method, including: Based on each coincidence event corresponding to each downsampled response line, a two-dimensional instantaneous coincidence energy histogram, a two-dimensional delayed coincidence energy histogram, and two one-dimensional probe energy spectra are obtained for each downsampled response line. Based on the two one-dimensional probe energy spectra, the probability density function of scattered photons and the probability density function of unscattered photons corresponding to the corresponding downsampled response line are obtained using the maximum likelihood-expectation maximization iterative algorithm.

5. The scattering correction method according to claim 4, characterized in that, Based on the coincidence events corresponding to each downsampling response line, two one-dimensional probe energy spectra are obtained, including: Break down all coincidence events corresponding to the downsampling response line into single events; Based on the location information of a single event, the single event is divided into two parts, and each part corresponds to a one-dimensional detection energy spectrum. Based on the two parts of single events, two one-dimensional probe energy spectra are obtained.

6. The scattering correction method according to claim 4, characterized in that, Based on the two one-dimensional detector energy spectra, the probability density functions of scattered photons and unscattered photons corresponding to the downsampled response lines are obtained using the maximum likelihood-expectation-maximization iterative algorithm, including: Obtain the correspondence function between the one-dimensional detector energy spectrum and the one-dimensional gamma photon energy spectrum; Based on the two obtained one-dimensional detector energy spectra and their corresponding relational functions, the maximum likelihood-expectation-maximization iterative algorithm and the initial iterative values ​​corresponding to the obtained downsampled response lines are used to obtain the two one-dimensional gamma photon energy spectra corresponding to the two one-dimensional detector energy spectra. Based on the two one-dimensional gamma photon energy spectra, the probability density functions of the two scattered photons and the two unscattered photons corresponding to the downsampled response lines are estimated.

7. The scattering correction method according to claim 6, characterized in that, The correspondence function between the one-dimensional detector energy spectrum and the one-dimensional gamma photon energy spectrum is as follows: ， in, This represents the expected value of the one-dimensional detection energy spectrum obtained from scanning the target object, corresponding to a single downsampling response line; Represents the one-dimensional gamma photon energy spectrum. The fuzzy response matrix characterizing a single downsampled response line. This represents the one-dimensional delayed energy spectrum corresponding to the random coincidence events included in a single response line.

8. The scattering correction method according to claim 7, characterized in that, The one-dimensional delayed energy spectrum corresponding to the random coincidence event is obtained through delayed coincidence event estimation.

9. The scattering correction method according to claim 8, characterized in that, Obtaining a one-dimensional delayed energy spectrum includes: All delayed coincidence events in the downsampled response line are obtained based on the delayed coincidence window; each delayed coincidence event is split into two corresponding delayed single events. Based on the location information of the delayed single event, the delayed single event is divided into two parts, each part corresponding to a one-dimensional delayed energy spectrum; Based on the two-part delayed single event, two one-dimensional delayed energy spectra are obtained; The location information of the delayed single event includes the location information of the downsampling detection module that detected the delayed single event.

10. The scattering correction method according to claim 7, characterized in that, Obtaining the fuzzy response matrix include: Acquiring prior detection data based on prostheses; Obtain the a priori downsampling response line based on prior detection data; All prior compliance events corresponding to the prior downsampling response line are divided into prior immediate compliance events and prior delayed compliance events. A prior one-dimensional instantaneous energy spectrum is obtained based on the prior instantaneous coincidence event corresponding to the downsampled response line; a prior one-dimensional delayed energy spectrum is obtained based on the prior delayed coincidence event corresponding to the downsampled response line. Obtain the difference between the prior one-dimensional instantaneous energy spectrum and the prior one-dimensional delayed energy spectrum, and normalize the difference to obtain the one-dimensional intermediate energy spectrum. The fuzzy response matrix is ​​obtained based on the one-dimensional intermediate energy spectrum.

11. The scattering correction method according to claim 6, characterized in that, Obtaining the two one-dimensional gamma photon energy spectra corresponding to the two one-dimensional detector energy spectra includes: Based on the correspondence function between the one-dimensional detector energy spectrum and the one-dimensional gamma photon energy spectrum, the iterative formula of the maximum likelihood-expectation maximization iterative algorithm is obtained. Set the number of iterations, and iterate using the maximum likelihood-expectation-maximization iteration algorithm formula based on the obtained initial iteration value until the set number of iterations is reached, to obtain the two one-dimensional gamma photon energy spectra corresponding to the two one-dimensional detector energy spectra.

12. The scattering correction method according to claim 11, characterized in that, The iterative formula for the maximum likelihood-expectation maximization iterative algorithm is: ， in, In Characterizes the one-dimensional probe energy spectrum obtained from scanning the target object, corresponding to a single downsampling response line. Characterizing the first in the energy spectrum A vertical strip area, , Characterizing the first in the energy spectrum respectively The, the A vertical strip area, It is the number of iterations. and In This is the fuzzy response matrix corresponding to the downsampled response line. The fuzzy response matrix corresponding to this downsampled response line is the first... line, number The value of the column, The fuzzy response matrix corresponding to this downsampled response line is the first... line, number The value of the column; This indicates the one-dimensional delayed energy spectrum corresponding to the random coincidence events included in the downsampled response line; For the first The energy spectrum obtained in the +1 iteration is the first... The one-dimensional gamma photon energy spectrum corresponding to each vertical bar region; , Respectively characterize the first The energy spectrum is obtained in the nth iteration. The, the The vertical bar regions correspond to the one-dimensional gamma photon energy spectrum.

13. The scattering correction method according to claim 11, characterized in that, Obtain the initial iteration values, including: Obtain the global initial scattered photon energy spectrum; The global initial scattered photon energy spectrum is deblurred to obtain the global intermediate initial iteration value; Stretch the global intermediate initial iteration value to obtain the initial iteration value corresponding to each downsampled response line.

14. The scattering correction method according to claim 13, characterized in that, Obtain the global initial scattered photon energy spectrum, including: Obtain the response line based on the probe data; All matching events corresponding to all matching response lines are divided into immediate matching events and random matching events; Obtain the global one-dimensional instantaneous energy spectrum based on instantaneous coincidence events; obtain the global one-dimensional delayed energy spectrum based on delayed coincidence events; Subtract the global one-dimensional delayed energy spectrum corresponding to the delayed coincidence event from the global one-dimensional instant energy spectrum corresponding to the instant coincidence event of all responses online to obtain the global one-dimensional undelayed energy spectrum; The objective function for obtaining the global initial scattered photon energy spectrum is based on the global one-dimensional undelayed energy spectrum.

15. The scattering correction method according to claim 14, characterized in that, The objective function for the global initial scattered photon energy spectrum is: ， Among them, among them, In The initial global scattered photon energy spectrum; Characterizing the first in the energy spectrum A vertical strip area, In The global one-dimensional undelayed energy spectrum is obtained based on the target object. In The global one-dimensional intermediate energy spectrum is obtained from the entire PET system scan, and the global one-dimensional intermediate energy spectrum is obtained based on the prosthesis; and The lower and upper thresholds of the high-energy window are set. The tensile coefficient is denoted as .

16. The scattering correction method according to claim 15, characterized in that, Get include: Acquiring prior detection data based on prostheses; All prior coincident response lines are obtained based on prior detection data; All prior compliance events corresponding to all prior compliance response lines are divided into global prior immediate compliance events and global prior delayed compliance events. Obtain the global prior one-dimensional instantaneous energy spectrum based on global prior instantaneous coincidence events; obtain the global prior one-dimensional delayed energy spectrum based on global prior delayed coincidence events; Obtain the difference between the global prior instantaneous energy spectrum and the global prior delayed energy spectrum. .

17. The scattering correction method according to claim 13, characterized in that, The global initial scattered photon energy spectrum is deblurred to obtain global intermediate initial iteration values, including: The defuzzing iterative formula is obtained based on the maximum likelihood-expectation maximization iterative algorithm; Set prior initial iteration values ​​and iteration conditions, and iterate through the iterative formula by defuzzification until the iteration conditions are met to obtain the scattering part of the global initial energy spectrum; Estimate the unscattered portion of the global initial energy spectrum based on the scattered portion of the global initial energy spectrum; Based on the scattered portion and the unscattered portion of the global initial energy spectrum, the global intermediate initial iteration value is obtained.

18. The scattering correction method according to claim 17, characterized in that, The iterative formula for deblurring is: ， in, In The characterization is based on the global one-dimensional probe energy spectrum obtained by scanning the target object. Characterizing the first in the energy spectrum A vertical strip area, , Characterizing the first in the energy spectrum respectively The j-th vertical region, It is the number of iterations. , In The global fuzzy response matrix, The first fuzzy response matrix is ​​the global fuzzy response matrix. line, number The value of the column, The first fuzzy response matrix is ​​the global fuzzy response matrix. line, number The value of the column; This represents the global one-dimensional delayed energy spectrum corresponding to all random coincidence events; For the first The energy spectrum obtained in the +1 iteration is the first... The global one-dimensional gamma photon energy spectrum corresponding to each vertical bar region; , The energy spectrum obtained in the k-th iteration is represented by the following characters: The, the The global one-dimensional gamma photon energy spectrum corresponding to each vertical bar region; ， in, Characterization constant.

19. The scattering correction method according to claim 18, characterized in that, Obtain the global fuzzy response matrix include: Acquiring prior detection data based on prostheses; All prior coincident response lines are obtained based on prior detection data; All prior compliance events corresponding to all prior compliance response lines are divided into global prior immediate compliance events and global prior delayed compliance events. Obtain the global prior one-dimensional instantaneous energy spectrum based on global prior instantaneous coincidence events; obtain the global prior one-dimensional delayed energy spectrum based on global prior delayed coincidence events; Obtain the difference between the global prior instantaneous energy spectrum and the global prior delayed energy spectrum; The difference is normalized to obtain the global prior intermediate energy spectrum; The global fuzzy response matrix is ​​obtained based on the global prior intermediate energy spectrum.

20. The scattering correction method according to claim 17, characterized in that, The unscattered portion of the global initial energy spectrum is estimated using an estimation function, which is: ， in, This is the global fuzzy response matrix; For hyperparameters; This represents the ratio of unscattered photons to scattered photons. ， in, To express summation, and Corresponding to , .

21. The scattering correction method according to claim 17, characterized in that, The representation function of the global intermediate initial iteration value is: ， in, It's a hyperparameter. The global fuzzy response matrix, This represents the ratio of unscattered photons to scattered photons.

22. The energy screening method according to claim 13, characterized in that, Stretch the intermediate initial iteration values ​​to obtain the initial iteration values ​​corresponding to each downsampled response line, including: The global intermediate initial iteration value is stretched using a stretching function to obtain the initial iteration value corresponding to each downsampled response line. The stretching function is: ， in, The initial iteration value, To express summation, The global fuzzy response matrix, This is the global intermediate initial iteration value; This represents the one-dimensional probe energy spectrum obtained from target object scanning, corresponding to a single downsampling response line. This represents the one-dimensional delayed energy spectrum corresponding to the random coincidence events included in a single response line.

23. The scattering correction method according to claim 6, characterized in that, Based on the two one-dimensional gamma photon energy spectra, the probability density functions of the two scattered photons and the two unscattered photons corresponding to the downsampled response lines are estimated, including: The one-dimensional gamma photon energy spectrum is divided into a scattered part and an unscattered part; By performing fuzzy response recovery processing on the scattered and unscattered portions of two one-dimensional gamma photon energy spectra, the two scattered and two unscattered portions corresponding to the two one-dimensional detector energy spectra corresponding to the downsampled response lines are obtained; The two scattered and two unscattered portions corresponding to the two one-dimensional detector energy spectra are normalized, and the probability density functions of the two scattered photons and the two unscattered photons corresponding to the downsampled response lines are estimated based on the obtained normalized values.

24. The scattering correction method according to claim 23, characterized in that, The scattering component is characterized as follows: The unscattered portion is characterized as follows: ， in, The scattering part, This represents the unscattered portion.

25. The scattering correction method according to claim 23, characterized in that, The one-dimensional gamma photon energy spectrum is divided into a scattered part and an unscattered part, including: The one-dimensional gamma photon energy spectrum is divided based on the energy value of the gamma photon when it is not scattered. The part with energy value less than the energy value is the scattered part, and the part with energy value equal to the energy value is the unscattered part.

26. The scattering correction method according to claim 1, characterized in that, Upsampling is performed on the downsampled response line, including: The downsampled response lines are processed using bilinear interpolation, 4D linear interpolation, or 5D linear interpolation to obtain the upsampled response lines corresponding to each downsampled response line.

27. The scattering correction method according to claim 26, characterized in that, When processing the downsampled response line using the 4D linear interpolation method, the number of scattering coincidence events corresponding to the upsampled response line is calculated using the following formula: ， in, Characterized by 4D linear interpolation by upsampling crystal and The number of scattering coincidence events that constitute the upsampled response line; Indicates upsampling crystal The set of the corresponding downsampling center crystal, downsampling axial adjacent crystal, downsampling radial adjacent crystal, and downsampling relative crystal; and This indicates the crystal number corresponding to the downsampled response line; Characterization of downsampling crystal and The number of scattering coincidence events on the downsampled response line; This indicates that for upsampling crystals Regarding downsampling crystals The weights; This indicates that for upsampling crystals Regarding downsampling crystals The weights; The normalization factor is characterized as follows: ， in The number of upsampled response lines contained in a downsampled response line.

28. The scattering correction method according to claim 1, characterized in that, Calculate the number of scattering coincidence events corresponding to each upsampled response line, including: Calculate the ratio of the total number of scattering coincidence events to the total number of coincidence events on each of the downsampled response lines; The number of scattering coincidence events corresponding to each upsampled response line is calculated based on the number of coincidence events on each upsampled response line contained in each downsampled response line and the proportion.

29. A scattering correction method, characterized in that, The scattering correction method includes: Response lines are obtained based on probe data; Based on the maximum likelihood-expectation-maximization iterative algorithm, the moment estimation method is used to obtain the estimated objective function corresponding to each response line. The estimated objective function is obtained based on the two-dimensional instantaneous coincidence energy histogram, the two-dimensional delayed coincidence energy histogram, the probability density function of scattered photons and the probability density function of unscattered photons corresponding to each response line. Solve the estimated objective function to obtain the number of scattering coincidence events corresponding to each response line; Calculate the number of scattering coincidence events corresponding to each response line; The estimated objective function is: ， in, These represent the number of true matching events, which do not require further processing. Indicates the number of scattering coincidence events. , is the unknown quantity to be determined; coefficient The calculation method is as follows: ， ， in, and It is the probability density function of unscattered photons corresponding to the two detection modules in the response line. and It is the probability density function of scattered photons corresponding to the two detection modules in the response line; Among them, the calculation obtained The value represents the number of scattering coincidence events corresponding to the corresponding response line.

30. The scattering correction method according to claim 29, characterized in that, Based on the maximum likelihood-expectation-maximization iterative algorithm, the estimated objective function corresponding to each response line is obtained using the moment estimation method, including: Based on each coincidence event corresponding to each response line, a two-dimensional instantaneous coincidence energy histogram, a two-dimensional delayed coincidence energy histogram, and two one-dimensional probe energy spectra are obtained for each response line. Based on the two one-dimensional probe energy spectra, the probability density function of the scattered photon and the probability density function of the unscattered photon corresponding to the corresponding response line are obtained by the maximum likelihood-expectation maximization iterative algorithm.

31. The scattering correction method according to claim 30, characterized in that, Based on the coincidence events corresponding to each response line, two one-dimensional probe energy spectra are obtained, including: Break down all matching events corresponding to the response line into single events; Based on the location information of a single event, the single event is divided into two parts, and each part corresponds to a one-dimensional detection energy spectrum. Based on the two parts of single events, two one-dimensional probe energy spectra are obtained.

32. The scattering correction method according to claim 30, characterized in that, Based on the two one-dimensional detector energy spectra, the probability density functions of scattered photons and unscattered photons corresponding to the corresponding response lines are obtained using a maximum likelihood-expectation-maximization iterative algorithm, including: Obtain the correspondence function between the one-dimensional detector energy spectrum and the one-dimensional gamma photon energy spectrum; Based on the two obtained one-dimensional detector energy spectra and their corresponding relational functions, the maximum likelihood-expectation maximization iterative algorithm and the initial iterative values ​​corresponding to the obtained response lines are used to obtain the two one-dimensional gamma photon energy spectra corresponding to the two one-dimensional detector energy spectra. Based on the two one-dimensional gamma photon energy spectra, the probability density functions of the two scattered photons and the two unscattered photons corresponding to the corresponding response lines are estimated.

33. The scattering correction method according to claim 32, characterized in that, Obtaining the two one-dimensional gamma photon energy spectra corresponding to the two one-dimensional detector energy spectra includes: Based on the correspondence function between the one-dimensional detector energy spectrum and the one-dimensional gamma photon energy spectrum, the iterative formula of the maximum likelihood-expectation maximization iterative algorithm is obtained. Set the number of iterations, and iterate using the maximum likelihood-expectation-maximization iteration algorithm formula based on the obtained initial iteration value until the set number of iterations is reached, to obtain the two one-dimensional gamma photon energy spectra corresponding to the two one-dimensional detector energy spectra.

34. The scattering correction method according to claim 32 or 33, characterized in that, Obtain the initial iteration values, including: Obtain the global initial scattered photon energy spectrum; The global initial scattered photon energy spectrum is deblurred to obtain the global intermediate initial iteration value; Stretch the global intermediate initial iteration value to obtain the initial iteration value corresponding to each response line.

35. The scattering correction method according to claim 34, characterized in that, Obtain the global initial scattered photon energy spectrum, including: Obtain the response line based on the probe data; All matching events corresponding to all matching response lines are divided into immediate matching events and random matching events; Obtain the global one-dimensional instantaneous energy spectrum based on instantaneous coincidence events; obtain the global one-dimensional delayed energy spectrum based on delayed coincidence events; Subtract the global one-dimensional delayed energy spectrum corresponding to the delayed coincidence event from the global one-dimensional instant energy spectrum corresponding to the instant coincidence event of all responses online to obtain the global one-dimensional undelayed energy spectrum; The objective function for obtaining the initial scattered photon energy spectrum is based on the global one-dimensional undelayed energy spectrum.

36. The scattering correction method according to claim 34, characterized in that, The global initial scattered photon energy spectrum is deblurred to obtain global intermediate initial iteration values, including: The defuzzing iterative formula is obtained based on the maximum likelihood-expectation maximization iterative algorithm; Set the prior initial iteration value and the number of iterations, and iterate through the defuzzification iterative formula until the number of iterations is reached to obtain the scattering part of the global initial energy spectrum; Estimate the unscattered portion of the global initial energy spectrum based on the scattered portion of the global initial energy spectrum; Based on the scattered portion and the unscattered portion of the global initial energy spectrum, the global intermediate initial iteration value is obtained.

37. The scattering correction method according to claim 32, characterized in that, Based on the two one-dimensional gamma photon energy spectra, the probability density functions of the two scattered photons and the two unscattered photons corresponding to the corresponding response lines are estimated, including: The one-dimensional gamma photon energy spectrum is divided into a scattered part and an unscattered part; By performing fuzzy response recovery processing on the scattered and unscattered portions of two one-dimensional gamma photon energy spectra, the two scattered and two unscattered portions corresponding to the two one-dimensional detector energy spectra corresponding to the response lines are obtained; The two scattered and two unscattered portions corresponding to the two one-dimensional detector energy spectra are normalized, and the probability density functions of the two scattered photons and the two unscattered photons corresponding to the corresponding response lines are estimated based on the obtained normalized values.

38. The scattering correction method according to claim 37, characterized in that, The one-dimensional gamma photon energy spectrum is divided into a scattered part and an unscattered part, including: The one-dimensional gamma photon energy spectrum is divided based on the energy value of the gamma photon when it is not scattered. The part with energy value less than the energy value is the scattered part, and the part with energy value equal to the energy value is the unscattered part.

39. An image reconstruction method, characterized in that, The image reconstruction method includes: The number of scattering coincidence events corresponding to each response line is obtained based on the scattering correction method according to any one of claims 1-38; The scattering events are corrected based on the number of scattering coincidence events to obtain a reconstructed image.

40. A scattering correction device, characterized in that, The scattering correction device includes: The downsampling response line acquisition module is configured to acquire the downsampling response line based on the probe data; The estimation module is configured to obtain the estimation objective function corresponding to each downsampled response line based on the maximum likelihood-expectation-maximization iterative algorithm and the moment estimation method. The estimation objective function is obtained based on the two-dimensional instantaneous coincidence energy histogram, the two-dimensional delayed coincidence energy histogram, the probability density function of scattered photons and the probability density function of unscattered photons corresponding to each downsampled response line. The scattering coincidence event acquisition module is configured to solve the estimated objective function and obtain the number of scattering coincidence events corresponding to each downsampled response line. The upsampling module is configured to perform upsampling processing on the downsampling response line to obtain the upsampling response line; The module for calculating the number of scattering coincidence events is configured to calculate the number of scattering coincidence events corresponding to each upsampled response line. The estimated objective function is: ， in, These represent the number of true matching events, which do not require further processing. Indicates the number of scattering coincidence events. , is the unknown quantity to be determined; coefficient The calculation method is as follows: ， ， in, and It is the probability density function of unscattered photons corresponding to the two detection modules in the downsampling response line. and It is the probability density function of scattered photons corresponding to the two detection modules in the downsampling response line; Among them, the calculation obtained The value represents the number of scattering coincidence events corresponding to the downsampled response line.

41. A scattering correction device, characterized in that, The scattering correction device includes: The response line acquisition module is configured to acquire the response line based on the probe data. The estimation module is configured to obtain the estimation objective function corresponding to each response line based on the maximum likelihood-expectation maximization iterative algorithm and the moment estimation method. The estimation objective function is obtained based on the two-dimensional instantaneous coincidence energy histogram, the two-dimensional delayed coincidence energy histogram, the probability density function of scattered photons and the probability density function of unscattered photons corresponding to each response line. The scattering coincidence event number acquisition module is configured to solve the estimated objective function and acquire the number of scattering coincidence events corresponding to each response line. The estimated objective function is: ， in, These represent the number of true matching events, which do not require further processing. Indicates the number of scattering coincidence events. , is the unknown quantity to be determined; coefficient The calculation method is as follows: ， ， in, and It is the probability density function of unscattered photons corresponding to the two detection modules in the response line. and It is the probability density function of scattered photons corresponding to the two detection modules in the response line; Among them, the calculation obtained The value represents the number of scattering coincidence events corresponding to the corresponding response line.

42. An image reconstruction apparatus, characterized in that, The image reconstruction apparatus includes: A scattering coincidence event acquisition module is configured to acquire the number of scattering coincidence events corresponding to the response line based on the scattering correction device as described in claim 40 or 41. The reconstruction module is configured to use an iterative image reconstruction algorithm based on the number of scattering coincidence events to reconstruct the image and obtain the reconstructed image.

43. A digital device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the method as claimed in any one of claims 1 to 39.

44. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the steps of the method as described in any one of claims 1-39.

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