WO2023035361A1 - Image reconstruction method, apparatus, system, and computer-readable storage medium - Google Patents

Abstract

An image reconstruction method, an apparatus, a system, and a computer-readable storage medium. The method comprises: utilizing a correction parameter and constructing a correspondence relationship between corrected single event data and initial single event data among coincidence events on a line of response detected by a detector (S101); utilizing the correspondence relationship and calculating a number of unscattered single events among the coincidence events and corresponding to the initial single event data (S103); utilizing a number of coincidence events and the number of unscattered single events and calculating a number of scattered coincidence events among the coincidence events (S105); and utilizing the number of scattered coincidence events and an obtained number of random coincidence events and performing image reconstruction according to a preset image reconstruction algorithm (S107). By means of directly performing correction on collected coincidence events during image reconstruction instead of needing an activity image and an attenuation image, scattering correction can be more easily implemented, and the quality of a reconstructed image can be improved.

Description

Image reconstruction method, device, system and computer-readable storage medium

This application claims priority to Chinese Patent Application 202111060795.1 filed on September 10, 2021, the entire contents of which are incorporated herein by reference.

technical field

The present application relates to the field of data processing, in particular, to an image reconstruction method, device, system and computer-readable storage medium.

Background technique

Positron Emission Tomography (PET) technology is one of the world's cutting-edge molecular imaging technologies. It can non-invasively, quantitatively and dynamically evaluate biological Metabolic levels, biochemical reactions and functional activities of various functional organs in the body, with high sensitivity and accuracy.

The working principle of PET is to label radionuclides on compounds that can participate in the metabolic process of living tissues, and inject the compounds into organisms, and the positrons emitted by radionuclides in organisms will then combine with electrons in organisms, Thus, an annihilation event of the electron pair occurs, producing two gamma photons with equal energy and opposite directions. If two detectors located on the Line of Response (LOR) detect two gamma photons within the specified coincidence time window (for example, 0-15 nanoseconds), then the two gamma photons are detected. The event of the horse photon can be called a coincidence event.

Coincidence events may generally include true coincidence events, scattered coincidence events, and random coincidence events. Wherein, the true coincidence event refers to the event that the time difference between two gamma photons generated by the same annihilation event reaching the two scintillation crystals located on the response line is within the coincidence time window. A random coincidence event is a false coincidence event in which two gamma photons detected come from different annihilation events but are mistaken for 2 gamma photons occurring "simultaneously" within the coincidence time window . A scatter coincidence event is defined as an event in which 2 gamma photons are generated for the same detected annihilation event, one of which is altered due to physical effects such as Compton scattering and/or Rayleigh scattering during flight flight direction.

Among these three coincidence events, the data collected for random coincidence events and scattered coincidence events may be wrong, which may affect the resolution, contrast and positioning accuracy of PET imaging. Therefore, it is very important to correct the acquired coincidence events during image reconstruction.

At present, multi-energy window technology, convolution/deconvolution technology, and simulation-based technology are mainly used to correct coincident events. Among these techniques, the most accurate and widely used are simulation-based techniques.

Simulation-based techniques generally include single-scattering simulation methods, double-scattering simulation methods, and Monte Carlo simulation methods. Among them, the single scattering simulation method is for each response line, by selecting a scattering point in the input activity image and attenuation image to obtain the photon movement path of single scattering (that is, a pair of gamma photons scatters once in total) , by calculating the single-scattering events generated on all photon paths corresponding to this response line to obtain the single-scattering events on this response line, finally, using the tail data containing only single-scattering events, through the tail fitting technique ( Tail Fitting (TF for short) fits the obtained single scattering events to obtain the stretching factor of the scattering coincidence event, and applies the stretching factor to all data to realize the scattering correction. The double-scattering simulation method is usually used in combination with the single-scattering simulation method. The main difference between the double-scattering simulation method and the single-scattering simulation method is that the double-scattering simulation method determines the photon movement path through two scattering points. The Monte Carlo simulation method mainly determines the total scattering distribution by simulating the motion of each gamma photon pair determined based on the input activity image and attenuation image.

During the process of implementing this application, the inventor found that the prior art has at least the following problems:

(1) The single-scattering simulation method only considers the case of single-scattering, but in reality, multiple-scattering will occur, which may lead to low accuracy of the scattering correction results, resulting in low quality of the reconstructed image. Although the double-scattering simulation method and the Monte Carlo simulation method consider the situation of multiple scattering, the calculation process is complicated and the amount of calculation is large.

(2) The above method needs to obtain the activity image and the attenuation image, but it is difficult to obtain the accurate activity image and the attenuation image in the actual system.

(3) Since it is equally difficult to obtain activity images outside the field of view, the above methods generally fail to account for external scatter (i.e., scattering events from gamma photon pairs located outside the field of view), which can lead to reconstructed images of lower quality.

Contents of the invention

The present application provides an image reconstruction method, device, imaging system and computer-readable storage medium to solve at least one problem existing in the prior art.

According to one aspect of the present application, an image reconstruction method is provided, including: constructing the corresponding relationship between initial single-event data and corrected single-event data in coincident events on the response line detected by the detector using correction parameters, wherein , the initial single-event data includes the number of single-events in which each measured energy of the gamma photons detected by the detector satisfies a preset condition; The number of corresponding unscattered single events; use the number of coincident events and the number of unscattered single events to calculate the number of scattered coincident events in the coincident event; use the number of scattered coincident events and the number of random coincidence events obtained and calculate An image reconstruction algorithm is set up for image reconstruction.

Optionally, before constructing the corresponding relationship, the method further includes: performing downsampling on a plurality of the response lines to obtain a downsampled response line, and correspondingly, after calculating the scatter on the downsampled response line After the number of coincident events, the method further includes: upsampling the downsampled response line according to a preset method to obtain an upsampled response line; using the number of scattered coincidence events on the downsampled response line to calculate the Number of scatter coincidence events on the upsampled response line.

Optionally, the preset method includes: a bilinear interpolation method, a 4D linear interpolation method or a 5D linear interpolation method.

Optionally, when the preset method is a 4D linear interpolation method, calculate the number of scattering coincidence events on the upsampling response line according to formula (I) and formula (II):

in,

Indicates the number of scattering coincidence events on the up-sampling response line; SC _{K, L} represents the number of scattering coincidence events on the down-sampling response line; I and J represent the numbers of the crystals that constitute the up-sampling response line; K and L represent the number of crystals constituting the down-sampling response W _tot is the normalization factor; N is the number of up-sampling response lines included in a down-sampling response line; T ^I and T ^J represent the down-sampling center crystals corresponding to up-sampling crystals I and J, respectively. Sets of downsampled axially adjacent crystals, downsampled radially adjacent crystals, and downsampled opposite crystals;

Indicates the weight of downsampling crystal K for upsampling crystal I;

Indicates the weight of downsampling crystal L for upsampling crystal J.

Optionally, the step of calculating the number of scattering coincidence events on the upsampling response line by using the number of scattering coincidence events on the downsampling response line includes: calculating the scattering coincidence event number on each of the downsampling response lines The ratio of the number of coincidence events to the number of coincidence events; calculating the scattered coincidence on the upsampling response line according to the number of coincidence events on the upsampling response line included in each of the downsampling response lines and the ratio number of events.

Optionally, the initial single event data is determined in the following manner: splitting the coincident event into single events, counting the measured energy corresponding to each single event, and counting the measured energy corresponding to the same measured energy. The corresponding number of single events to obtain the initial single event data; or split the coincident event into single events, draw at least one energy spectrum according to the position of the probe where the single event is located, and obtain each of the energy spectra The initial single-event data; or split the coincidence event into single events, draw at least one energy spectrum according to the position of the probe where the single event is located, and divide each energy spectrum according to the time difference between gamma photons arriving at the probe The spectrum is divided into a plurality of sub-spectra, and the initial single-event data in each of the sub-spectra is acquired.

Optionally, use formula (III)-formula (VIII) to calculate the number of scattering coincidence events in the coincidence event:

SC≈SC ⁺ formula (Ⅲ)

SC≈SC ^- Formula (IV)

SC≈1/[(1/SC ⁺ )+(1/SC ^- )] Formula (Ⅴ)

SC≈0.5(SC ⁺ +SC ^- ) Formula (Ⅶ)

Wherein, SC ⁺ =S _tot -S _unsc ;

S _tot is the total number of single events, and is twice the number of coincident events; S _unsc is the sum of the number of unscattered single events; SC is the number of scattered coincident events.

Optionally, the step of using the corresponding relationship to calculate the number of unscattered single events corresponding to the initial single event data in the coincident event includes: constructing an objective function of the corresponding relationship; calculating the maximum value of the objective function The value is the value of the corrected single-event data, and the value of the corrected single-event data is determined as the number of unscattered single-events corresponding to the initial single-event data in the coincident event.

Optionally, for each of the acquired initial single-event data, construct the corresponding relationship according to formula (IX):

in,

X is the corrected single-event data, and X={X _j }, j=1,2,...,n, X _j is the real energy E of the gamma photon, E satisfies E _{t and} satisfies E _j ≤ E _t <E _j+1 The number of single events under; Y is the initial single event data, and Y={Y _i }, i=1, 2,..., m, Y _i is the measured energy E _d satisfying E _i ≤ E _d <E _i+1 number of single events;

is the expected value of Y, and

P is a correction parameter, and the element P _ij in P is the actual energy E d of the gamma photon whose real energy E _t satisfies E _i ≤ E _t < E _i+1 after the actual measurement and satisfies E _i ≤ _{E d <} E _{i+ 1} ; E _j and E _j+1 are the jth energy and j+1th energy in the preset energy window {E _j } respectively; E _i and E _i+1 are the measured energy window {E _i } in the i-th energy and i+1-th energy; m, n, i and j are all positive integers.

Optionally, when m=n and {E _i }={E _j }, the elements of the i-th row and j-th column of the correction parameter P are calculated by formula (X):

in,

m is the initial single-event data obtained by scanning the gamma photon with the real energy E _t under the condition of ignoring the scattering coincidence event; the real energy E _t satisfies E _i ≤ E _t < E _i+1 ; i, j, s and m are positive integers.

Optionally, the real energy of the gamma photon includes 202keV, 307keV, 511keV, 622keV or 1.27MeV.

Optionally, the step of constructing the objective function of the corresponding relationship comprises: utilizing the maximum likelihood method to construct the objective function, and the objective function is determined by formula (XI):

Φ(X)＝∑ _i (-∑ _j P _ij X _j +Y _i ln(∑ _j P _ij X _j ))-βR(X) Formula (Ⅺ)

in,

β is a hyperparameter; R(X) is a penalty item; M is a constant.

Optionally, the step of calculating the value of the corrected single-event data when the objective function takes the maximum value includes calculating the value of the corrected single-event data by formula (M) or formula (N):

or,

,in,

in,

and

Both are values for correcting single-event data;

Is the intermediate variable of the kth iteration; k is the number of iterations.

Optionally, the preset image reconstruction algorithm includes MLEM algorithm, OSEM algorithm or MAP-OSL algorithm.

According to another aspect of the present application, an image reconstruction device is provided, including: a construction unit configured to use correction parameters to construct initial single-event data and corrected single-event data within coincident events on the response line detected by the detector Correspondence between data, wherein the initial single-event data includes the number of single-events when each measured energy of the gamma photons detected by the detector satisfies corresponding specific conditions; the first calculation unit is configured In order to use the corresponding relationship to calculate the number of unscattered single events corresponding to the initial single event data in the coincident event; the second calculation unit is configured to use the number of coincident events and the number of unscattered single events to calculate The number of scattering coincidence events in the coincidence events; an image reconstruction unit configured to use the number of scattering coincidence events and the obtained number of random coincidence events to perform image reconstruction according to a preset image reconstruction algorithm.

According to another aspect of the present application, an image reconstruction device is provided, including: a memory on which program codes are stored; a processor connected to the memory, and when the program codes are executed by the processor , to implement the method described above.

According to another aspect of the present application, an imaging system is provided, including: the above-mentioned image reconstruction device; and a detector connected to the image reconstruction device.

Optionally, the detectors include PET detectors, PET-CT detectors, CT detectors, or MR detectors.

According to another aspect of the present application, a computer-readable storage medium is provided, on which program instructions are stored, and the above-mentioned method is implemented when the program instructions are executed.

It can be seen from the above description that this application uses the correction parameters to construct the corresponding relationship between the initial single event data and the corrected single event data in the coincident event on the response line, and uses the corresponding relationship to calculate the number of unscattered single events in the coincident event. Use the number of unscattered single events to calculate the number of scattered coincidence events in the coincidence event, and use the number of scattered coincidence events and the number of random coincidence events obtained to perform image reconstruction according to the preset image reconstruction algorithm, without the need for activity images and attenuation images , making event-compliant scatter correction easier to implement, which can improve image quality.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings that need to be used in the description of the embodiments.

Fig. 1 shows a flowchart of an image reconstruction method according to an embodiment of the present application.

Fig. 2 shows a schematic diagram of an energy spectrum drawn according to an embodiment of the present application.

Fig. 3 shows a flowchart of another image reconstruction method according to an embodiment of the present application.

Fig. 4 shows a schematic diagram of merging multiple response lines according to an embodiment of the present application.

FIG. 5 shows another schematic diagram of merging multiple response lines according to an embodiment of the present application.

Figure 6 shows a schematic diagram of a cylindrical prosthesis with three cold zones.

FIG. 7 shows a reconstructed image obtained using the image reconstruction method without scatter correction for the cylindrical phantom in FIG. 6 .

FIG. 8 shows the reconstructed image obtained by using the image reconstruction method proposed in the present application for the cylindrical prosthesis in FIG. 6 .

Fig. 9 shows a block diagram of an image reconstruction device according to an embodiment of the present application.

Fig. 10 shows a block diagram of another image reconstruction device according to an embodiment of the present application.

Fig. 11 shows a block diagram of another image reconstruction device according to an embodiment of the present application.

Fig. 12 shows a block diagram of an imaging system according to an embodiment of the present application.

Detailed ways

Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this application will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The same reference numerals denote the same or similar parts in the drawings, and thus their repeated descriptions will be omitted.

The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided in order to give a thorough understanding of embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced without one or more of these specific details, or other methods, components, materials, devices or operations may be used. In these instances, well-known structures, methods, devices, implementations, materials, or operations are not shown or described in detail.

The flow charts shown in the drawings are only exemplary illustrations, and do not necessarily include all contents and operations/steps, nor must they be performed in the order described. For example, some operations/steps can be decomposed, and some operations/steps can be combined or partly combined, so the actual order of execution may be changed according to the actual situation.

The terms "first", "second" and the like in the specification and claims of the present application and the above drawings are used to distinguish different objects, rather than to describe a specific order. Furthermore, the terms "include" and "have", as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but optionally also includes unlisted steps or units, or optionally further includes For other steps or units inherent in these processes, methods, products or devices.

It should be noted that, unless otherwise specified, capital letters without subscripts in this article can represent vectors or matrices, while capital letters with subscripts can represent elements in vectors or matrices, and capital letters and small letters can represent different meanings. In addition, the summation symbol described in this article defaults to summing the entire value range, such as Σ can be expressed as

Specific embodiments according to the present application will be described in detail below with reference to the accompanying drawings.

The image reconstruction method provided by the present application can be applied to various imaging systems, for example, PET imaging system, electronic computer tomography (Computed Tomography, referred to as CT) imaging system, PET-CT imaging system, magnetic resonance (Magnetic Resonance, referred to as MR) In the application environment of image reconstruction after imaging system and various detectors collect data. The detector may include a plurality of probes, two probes that detect a coincidence event may form a probe pair, and one or more response lines may be formed on each probe pair.

Fig. 1 shows a flowchart of an image reconstruction method according to an embodiment of the present application. The image reconstruction method will be described in detail below with reference to FIG. 1 .

In step S101, the correction parameters are used to construct the corresponding relationship between the initial single-event data and the corrected single-event data in the coincident events on the response line detected by the detector.

The correction parameter may refer to a parameter that corrects the blurring effect of the detector on the photon energy (ie, the true energy of the gamma photon). Wherein, the ambiguity effect generally refers to the phenomenon that multiple measured energies are actually detected for a gamma photon with a specific real energy (eg, 511KeV, 622KeV, or 1.27MeV, etc.). For example, for a gamma photon whose real energy is E, the actually detected photon energy is not necessarily E, but there may be multiple measured energies such as E ₁ , E ₂ , . . . , E _m+1 .

The initial single-event data may include the number of single-events under which each measured energy of the gamma photons detected by the detector satisfies a corresponding preset condition. For example, the initial single-event data may include the number of single-events Y _i under which the measured energy E _d satisfies E _i ≤ E _d <E _i+1 , where i is a positive integer. Of course, the preset conditions may also refer to other conditions that meet actual needs, which is not limited here. The relationship between the number of single events included in the initial single event data and the measured energy can be expressed by discrete or continuous functions, or in the form of statistical tables. Also, initial single-event data can be obtained in one of the following ways:

According to one embodiment, all coincident events on each response line are split into single events, then the measured energy of gamma photons corresponding to each single event is counted, and the number of single events corresponding to the same measured energy is counted, Thus, initial single-event data are obtained.

According to another embodiment, all coincident events on each response line are split into single events, and one or more energy spectra are drawn according to the position of the probe where the single event is located, as shown in Figure 2, the drawn energy spectrum indicates The corresponding relationship between the number of single events on the probe and the measured energy is obtained, so that the corresponding initial single event data can be obtained from each energy spectrum. In the case of multiple energy spectra, a total of multiple initial single-event data can be obtained. In addition, corresponding energy spectra are drawn for each probe, and the number of energy spectra is the same as the number of probes covered by each response line. For the specific process of drawing the energy spectrum, reference may be made to the prior art, which will not be repeated here.

According to another embodiment, all coincident events on each response line are split into single events, and one or more energy spectra are drawn according to the probe positions where the single events are located. Then, at least one energy spectrum is divided into multiple sub-energy spectra according to the time difference of the gamma photons arriving at the probe, and initial single-event data are obtained from each sub-energy spectrum, so that multiple initial single-event data can be obtained. For example, for Time of Flight (TOF) scattering, after obtaining the energy spectrum, each energy spectrum is divided into multiple sub-energy spectra according to the time difference between the arrival of gamma photons at the probe, and the sub-energy spectrum corresponding to each energy spectrum The number of spectra is the same as the number of segments of the time difference. Each sub-spectrum can indicate the correspondence between the number of single events and the photon energy in a certain time interval. For example, taking a time difference of 200 ps as an example, when the number of time segments is 10, each energy spectrum can be divided into 10 sub-energy spectra, and the time interval corresponding to each sub-energy spectrum is 200 ps. This method of dividing each energy spectrum into multiple sub-energy spectra by the time difference of gamma photons arriving at the probe to obtain the initial single-event data in the coincidence events on each response line can make the subsequent calculation of the number of scattered coincidence events more precise.

In one embodiment, for each acquired initial single-event data, the corresponding relationship between the initial single-event data and the corrected single-event data can be constructed according to formula (1):

Then X＝P ^T Y formula (1')

in,

Wherein, X is the corrected single-event data, which can be represented by a vector, for example, X={X _j }, j=1,2,...,n, X _j is the true energy E _t of the gamma photon satisfying E _j ≤ The number of single events under E _t <E _j+1 ; E _j and E _j+1 are the jth energy and j+1th energy in the preset energy window {E _j } respectively, and in the preset energy In the window, E ₁ <E ₂ <...<E _n+1 ; Y is the initial single-event data, which can also be represented by a vector, for example, Y={Y _i }, i=1, 2,..., m, Y _i is the measured energy E _d of gamma photons satisfying E _i ≤ E _d <E _i+1 the number of single events; E _i and E _i+1 are the ith energy and the ith energy in the measured energy window {E _i } respectively i+1 energies, and in the measured energy window, E ₁ <E ₂ <...<E _m+1 ;

is the expected value of Y, and

P is a correction parameter, which can be in the form of a matrix, and the element P _ij is the actual energy E d of the gamma photon whose real energy E _t satisfies E _i ≤ E _t <E _i+1 after the actual measurement and satisfies _{E i at} the same time Probability of ≤E _d <E _i+1 ; P ^T is the transposition matrix of P; m, n, i and j are all positive integers, and i is less than or equal to m, and j is less than or equal to n.

In addition to the matrix form shown above, the correction parameter P can also adopt other forms, and there is no limitation here. In addition, the above correction parameters can be obtained by using a detector to detect a plurality of gamma photons with different real energies to obtain the energy distribution of these gamma photons, and normalize the energy distribution.

In another embodiment, when m=n and {E _i }={E _j }, the element in row i and column j of correction parameter P can be calculated by formula (2):

in,

is the initial single-event data (s=1, 2, ..., m) obtained by scanning gamma photons with true energy E _t under the neglect of scattering coincidence events; the true energy E _t satisfies E _i ≤ E _t <E _i+1 , E _i and E _i+1 are respectively the i-th energy and the i+1-th energy in the preset energy window {E _i }, usually n=m, {E _i }={E _j }, and these two preset energy windows can be artificially set according to actual needs, E ₁ is the low threshold of the preset energy window, E _n , E _m are the high threshold of the preset energy window; i, j, s , m is a positive integer. Moreover, the real energy E _t of the gamma photon may be 202keV, 307keV, 511keV, 622keV or 1.27MeV, etc.

For example, the entire matrix P can be obtained by scanning only the gamma photons with E=511keV. First, the value of the i-th row of the matrix P can be obtained according to the formula (2), so that the entire matrix can be obtained according to the value of the i-th row, and the obtained matrix P has the following form:

It can be seen from the formula (A) that for the value of the ia-th row or the i+a-th row of the matrix P, the value of the i-th row of the matrix P is shifted left or right by a unit, and a is a positive integer. At this time, if you remember

That is, the vector formed by the reverse order of the value of the i-th row of the matrix P.

By using the correction parameters in step S101 to construct the corresponding relationship between the initial single-event data and the corrected single-event data in each coincident event detected by the detector on each response line, the detector’s influence on the photon energy can be eliminated in the subsequent calculation process The influence of the blur effect.

In step S103, the number of unscattered single events corresponding to the initial single event data within the coincident event is calculated by using the corresponding relationship.

For each initial single-event data, after constructing the corresponding relationship between it and the corrected single-event data, the objective function of the corresponding relationship can be constructed, the maximum value of the objective function can be calculated, and the single-event correction can be calculated when the objective function takes the maximum value The value of the data, and the value of the corrected single-event data is determined as the number of unscattered single-events corresponding to the initial single-event data in the calculation coincident event. Wherein, "the number of unscattered single events corresponding to the initial single event data" may refer to: for an initial single event data, a number of unscattered single events can be calculated.

According to an embodiment, for each corresponding relationship constructed, the maximum likelihood method with penalty is used to construct the objective function shown in formula (3). But the present application is not limited thereto, and any method capable of parameter estimation is suitable for constructing the objective function.

Φ(X)＝∑ _i (-∑ _j P _ij X _j +Y _i ln(∑ _j P _ij X _j ))-βR(X) formula (3)

Among them, β is a hyperparameter, and its specific value can be determined through experiments in advance; R(X) is a penalty term.

Then, the objective function shown in formula (3) can be solved to obtain its maximum value, and the value of the corrected single-event data can be calculated when the objective function takes the maximum value, and the value can be determined as the corresponding value on the response line The number of unscattered single events within the event corresponding to this initial single event data. For the process of calculating the maximum value of the objective function, reference may be made to the corresponding description in the prior art, which will not be repeated here.

According to an embodiment, in order to make the obtained number of unscattered single events more accurate, prior knowledge can be used before solving the above objective function, that is, the photon count is less at the energy less than the radiation source, and at the energy greater than the radiation source The energy of is 0, and almost all of them are concentrated at the energy of the radioactive source. The penalty term R(X) is defined as shown in formula (4).

Taking the partial derivative with respect to R(X) yields formula (B).

Wherein, M is a constant, generally greater than 100.

In addition, the following two methods can be used to calculate the value of the corrected single-event data when the objective function takes the maximum value.

According to one embodiment, utilize the algorithm OSL-EDR (Eliminate Detector Response, be called for short EDR) derived based on the principle of one step later (One Step Later, be called for short OSL), as shown in formula (5), carry out iterative calculation formula (3 ) shows the value of the corrected single-event data when the objective function takes the maximum value, that is, the number of unscattered single-events.

in,

and

Respectively represent the number of single events when the true energy E _t of the gamma photon satisfies E _j ≤ E _t < E _j+1 obtained in the kth and k+1 iterations, that is, the corrected single-event data to be calculated value; k is the number of iterations, and its value range is generally 200 to 500;

It can be calculated according to formula (B); b is a positive integer between 1 and n.

According to another embodiment, utilize the algorithm OT-EDR derived based on the principle of optimization transfer (Optimization Transfer, be called for short OT), as shown in formula (6), carry out iterative calculation The objective function shown in formula (3) takes maximum value The value of the corrected single-event data when , that is, the number of unscattered single-events.

in,

It is an intermediate variable for the kth iteration.

In step S105, the number of scattered coincident events within the coincident event is calculated by using the number of coincident events and the number of unscattered single events.

After calculating the number of all unscattered single events within a coincident event on one or more response lines, the number of unscattered single events and the number of coincident events (that is, the number of coincident events) on the response line can be used to calculate Number of scatter coincidence events within coincidence events on each response line.

Formulas (7) to Formulas (12) give six methods for calculating the number of scattering coincidence events on each response line.

SC≈SC ⁺ formula (7)

SC≈SC ^- Formula (8)

SC≈1/[(1/SC ⁺ )+(1/SC ^- )] Formula (9)

SC≈0.5(SC ⁺ +SC ^- ) formula (11)

Wherein, SC is the number of scattering coincidence events; SC ⁺ =S _tot -S _unsc ;

S _tot is the total number of single events, and it is twice the number of coincident events, as shown in formula (C); S _unsc is the sum of the number of unscattered single events, as shown in formula (D).

S _tot ＝2S ₀ +2S ₁ +2S ₂ formula (C)

S _unsc ＝2S ₀ +S ₁ formula (D)

Among them, in formula (C) and formula (D), S ₀ represents the number of coincidence events in which neither of the two gamma photons produced by annihilation scattered; S ₁ represents the number of gamma photons produced by annihilation only one The number of coincident events in which photons have been scattered one or more times; S ₂ is the number of coincident events in which both gamma photons produced by annihilation have been scattered one or more times.

It should be noted that when only one energy spectrum or sub-energy spectrum is drawn for each response line, the sum of the number of unscattered single events refers to the calculated number of unscattered single events itself, while for each response line When multiple energy spectra or multiple sub-spectra are plotted, the sum of the unscattered single event numbers refers to the sum of the calculated multiple unscattered single event numbers.

According to another embodiment, formulas (7)-(12) can also be used to calculate the total number of scattering events on all response lines. At this time, SC represents the total number of scattering events, and S _tot represents the total number of events on multiple response lines. The number of single events, and it is twice the total number of coincident events, S _unsc represents the total number of unscattered single events.

In step S107, image reconstruction is performed according to a preset image reconstruction algorithm by using the number of scattered coincidence events and the obtained number of random coincidence events.

After calculating the number of scattering coincidence events in the coincidence event, the number of scattering coincidence events and the number of random coincidence events can be substituted into the pre-built image reconstruction model, and then the image reconstruction model is solved by using the preset image reconstruction algorithm, to obtain a reconstructed image. Wherein, the number of random coincidence events may be obtained by randomly correcting the coincidence events before or after obtaining the number of scattering coincidence events. For the specific process of random correction, reference may be made to the corresponding description in the prior art, which will not be repeated here.

In one embodiment, the constructed image reconstruction model can be expressed as formula (13):

Among them, CE _x is the number of coincident events on the xth response line; A _xy is the system response matrix, which contains the information of attenuation correction and normalization correction; V _y is the yth voxel value of the image; SC _xy is the number of scattered coincidence events on the xth response line; R _x is the number of random coincidence events on the xth response line; both x and y are positive integers.

The matrix form of the above image reconstruction model can be expressed by formula (14):

CE＝A*V+SC+R formula (14)

In one embodiment, the preset image reconstruction algorithm may include the Maximum Likelihood Expectation Maximization (MLEM for short) algorithm, the Ordered subsets Expectation Maximization (OSEM for short) algorithm, or the maximum Experimental estimation-one step later (Maximum a Posteriori-One Step Later, MAP-OSL for short) algorithm.

After using the above algorithm to solve the reconstructed image model shown in formula (13) or (14), the following reconstructed image can be obtained:

MLEM:

OSEM:

MAP-OSL:

Among them, V ^k and V ^k+1 represent the images obtained by the kth iteration and the k+1th iteration respectively, S _z is the zth subset on the response line, and z is a positive integer.

For the specific process of the above image reconstruction, reference may be made to the corresponding description in the prior art, which will not be repeated here.

Through the embodiment shown in Figure 1, by determining the initial single event data in the corresponding event on the response line, using the correction parameters to construct the corresponding relationship between the initial single event data and the corrected single event data, and constructing the objective function of the corresponding relationship, Calculate the value of the corrected single-event data when the objective function takes the maximum value, and determine the value of the corrected single-event data as the number of scatter coincident events corresponding to the initial single-event data in the coincident event, and finally use the number of scattered coincident events to perform Image reconstruction does not require activity and attenuation images, which makes it easier to implement event-compliant scatter correction during image reconstruction. In addition, the embodiment shown in FIG. 1 considers all scattering situations, including multi-scattering and external scattering, so that the accuracy of the obtained correction result is high, thereby improving the quality of the reconstructed image. Compared with the double-scattering simulation method and the Monte Carlo simulation method in the prior art, the calculation process of the image reconstruction method shown in FIG. 1 is relatively simple and the calculation amount is small.

Fig. 3 shows a flowchart of another image reconstruction method according to an embodiment of the present application. The image reconstruction method will be described in detail below with reference to FIG. 3 . The method includes:

In order to reduce the calculation amount of the image reconstruction method shown in Fig. 1, after obtaining coincident events on multiple response lines from the detector, step 301 as shown in Fig. 3 is executed to down-sample the multiple response lines to obtain the reduced Sample response line.

Specifically, all the response lines may be grouped according to the positions of the response lines or the angles between the response lines, and then all the response lines in each group are merged into one response line. For example, combine all the response lines between each probe pair into one response line, as shown in Figure 4, or you can also combine the All response lines are merged into one response line, as shown in FIG. 5 ; all response lines whose included angle is less than or equal to a preset angle (for example, 15 degrees) may also be merged into one response line. It should be noted that the specific number of multiple adjacent probe pairs can be selected according to actual needs, and is not limited here. By downsampling all response lines, it is convenient to draw the energy spectrum and reduce the amount of data calculation.

In step S303, the correction parameter is used to construct the corresponding relationship between the initial single-event data and the corrected single-event data in the coincident event on the downsampling response line.

In step S305, the number of unscattered single events corresponding to the initial single event data in the downsampled coincident event is calculated by using the correspondence relationship between the initial single event data and the corrected single event data.

In step S307, the number of scattered coincident events in the coincident event is calculated by using the number of coincident events after downsampling and the number of unscattered single events.

Steps S303-S307 correspond to steps S101-S105 in FIG. 1, and the difference between them is only that the relevant data on the down-sampled response line are processed. Therefore, for the specific execution process of steps S303-S307, reference may be made to the relevant description of steps S101-S105 in FIG.

In step S309, the downsampled response line is upsampled using a preset method to obtain an upsampled response line, and the number of scatter coincidence events on the upsampled response line is calculated using the number of scatter coincidence events on the downsampled response line.

The preset method may include a linear interpolation method, for example, a bilinear interpolation method or a 4D (4D represents axial and radial translation, axial and radial rotation of the response line) linear interpolation method, and may also include other The sampling method is not limited here. In addition, for TOF scattering, the above-mentioned linear interpolation method may include a 5D (that is, time difference is also included on the basis of 4D) linear interpolation method.

As for the specific method of upsampling the downsampled response line, reference may be made to the corresponding description in the prior art, which will not be repeated here.

According to one embodiment, for the method of upsampling the downsampled response lines by using the 4D linear difference method, the scatter on the upsampled response lines included in each downsampled response line can be calculated according to formulas (18)-(19) Number of matching events.

in,

Indicates the number of scattering coincidence events on N upsampling response lines included in a downsampling response line; SC _{K, L} represent the number of scattering coincidence events on a downsampling response line; I and J represent the "small "The crystal number; K and L indicate the number of the "large" crystals that make up the downsampled response line; W _tot is the normalization factor; N is the number of upsampled response lines included in one downsampled response line; T ^I and T ^J represents the set of downsampling central crystals, downsampling axially adjacent crystals, downsampling radially adjacent crystals, and downsampling relative crystals corresponding to upsampling crystals I and J, respectively;

Indicates the weight of downsampling crystal K for upsampling crystal I,

Denotes the weight of downsampling crystal L for upsampling crystal J, and

and

It can be obtained by calculating the axial length and radial length of the down-sampled crystal, and the radial and axial distances from the center of the up-sampled crystal to the central crystal. Among them, the up-sampling crystal is the original crystal in the probe, the down-sampling crystal is a collection of one or more crystals after down-sampling, the central crystal is the down-sampling crystal where the up-sampling crystal is located, and the axially adjacent crystals are the same as the up-sampling crystal. The axially closest crystal, the radially adjacent crystal is the radially closest crystal to the upsampled crystal, and the opposite crystal is the diagonally closest crystal to the upsampled crystal.

According to an embodiment, the number of scatter coincidence events on the upsampled response line can also be calculated in the following manner: firstly, the ratio of the number of scatter coincidence events to the number of coincidence events on each downsampled response line is calculated, and then according to each downsampled response line The number of coincident events on the upsampled response line contained in the response line and the ratio calculate the number of scattered coincidence events on the upsampled response line, as shown in formula (20).

in,

_Indicates the number of scatter coincidence events on all upsampling response lines included in the lth downsampling response line; The number of coincident events on the previous corresponding response line; a _l represents the ratio of the number of scattered coincident events to the number of coincident events on the lth downsampling response line, and l is a positive integer.

In another embodiment of the present application, the number of scatter coincidence events on all upsampled response lines may be added to obtain the total number of scatter coincidence events.

In step S311 , image reconstruction is performed according to a preset image reconstruction algorithm by using the number of scattered coincidence events and the obtained number of random coincidence events.

Step S311 corresponds to step S107 in FIG. 1 , so for its specific execution process, reference may be made to the relevant description of step S107 in FIG. 1 in the above embodiment, and details are not repeated here.

According to the embodiment shown in FIG. 3 , the downsampled response line can be restored to its original state by upsampling the downsampled response line. By using the above formulas (18)-(19) or (20) to calculate the number of scatter coincidence events on the upsampling response line, the calculation result of the number of scatter coincidence events can be made more accurate, thereby improving the accuracy of the scatter correction results.

The beneficial effects of the image reconstruction method provided according to the embodiment of the present application will be described below with specific application examples.

Figure 6 shows a schematic diagram of a cylindrical prosthesis with three cold zones. The gray area is called the background area, which is filled with radioactive sources and filled with water; the white area has no radioactive sources and is filled with air, water, and polytetrafluoroethylene (PTFE) respectively. These three materials have different densities, and the probability of Compton scattering of gamma photons in them is also different.

Figure 7 and Figure 8 respectively show the detection data of the prosthesis shown in Figure 6, the image reconstruction results without scattering correction and the image reconstruction results using the technical solution proposed in this application when the influence of random noise is ignored. It can be seen from Figures 7 and 8 that the image contrast using the image reconstruction method proposed in this application is significantly restored, especially the contrast of the cold area in the lower left corner filled with PTFE material and the cold area in the lower right corner filled with water in the image is significantly restored, and the image The voxel values in the background area of , are more uniform overall, which is more in line with the actual situation.

Fig. 9 shows a block diagram of an image reconstruction method device according to an embodiment of the present application. The image reconstruction device will be described in detail below with reference to FIG. 9 .

As shown in FIG. 9 , the image reconstruction device includes a construction unit 903 , a first calculation unit 905 , a second calculation unit 907 and an image reconstruction unit 913 .

The construction unit 903 is used to construct the corresponding relationship between the initial single-event data and the corrected single-event data in the coincident event on the response line detected by the detector using the correction parameters, wherein the initial single-event data includes Each measured energy of the gamma photon satisfies the number of single events under the corresponding specific conditions. The first calculation unit 905 is used to calculate the number of unscattered single events corresponding to the initial single event data in the coincident event by using the corresponding relationship, specifically to construct the objective function of the corresponding relationship, and to correct the single event data when the calculated objective function takes the maximum value , and the value of the corrected single-event data is determined as the number of unscattered single-events corresponding to the initial single-event data within the event. The second calculating unit 907 is configured to calculate the number of scattered coincident events within the coincident event by using the number of coincident events and the number of unscattered single events. The image reconstruction unit 913 is used to use the number of scattered coincidence events and the number of random coincidence events obtained from a random correction unit (not shown) to perform image reconstruction according to a preset image reconstruction algorithm.

In another embodiment, as shown in FIG. 10, the image reconstruction device may further include a downsampling unit 901, which may be used to Downsample multiple response lines to obtain downsampled response lines. Correspondingly, the first calculation unit 905 can also be used to calculate the number of unscattered single events after downsampling, and the second calculation unit 907 can also be used to calculate the downsampled The number of scatter coincidence events within the coincidence event on the response line.

In another embodiment, as shown in FIG. 10 , the image reconstruction apparatus may also include an upsampling unit 909 and a third calculation unit 911 . Wherein, the upsampling unit 909 can be used to upsample the downsampled response line according to a preset method to obtain an upsampled response line; the third calculation unit 911 can be used to calculate the upsampled response line by using the number of scattering events on the downsampled response line Number of scatter coincidence events on the response line. In addition, the first computing unit 905, the second computing unit 907, and the third computing unit 911 may be integrated into the same module.

For detailed descriptions of the foregoing units, reference may be made to corresponding descriptions in the foregoing method embodiments, and details are not repeated here.

According to some embodiments, by using the image reconstruction device provided by the embodiments of the present application, the scatter correction for coincident events can be realized without the need for activity maps and attenuation maps, and the accuracy of the scatter correction results is high, Thus, the quality of the reconstructed image can be improved.

Fig. 11 shows another image reconstruction device according to an embodiment of the present application. The image reconstruction device shown in FIG. 11 is only an example, and should not limit the functions and scope of use of this embodiment of the present application.

As shown in Fig. 11, the image reconstruction apparatus is represented in the form of a general-purpose computing device. The components of the image reconstruction device may include but not limited to: at least one processor 210, at least one memory 220, a bus 230 connecting different system components (including the memory 220 and the processor 210), a display unit 240, and the like. Wherein, the memory 220 stores program codes, and the program codes can be executed by the processor 210, so that the processor 210 executes the methods described in this specification according to various exemplary embodiments of the present application. For example, the processor 210 may execute the method as shown in FIG. 1 .

The memory 220 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 2201 and/or a cache storage unit 2202 , and may further include a read-only storage unit (ROM) 2203 .

Memory 220 may also include programs/utilities 2204 having a set (at least one) of program modules 2205 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, which Each or some combination of the examples may include the implementation of a network environment.

Bus 230 may represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local area using any of a variety of bus structures. bus.

The image reconstruction apparatus can also communicate with one or more external devices 300 (such as keyboards, pointing devices, bluetooth devices, etc.), and can also communicate with one or more devices that enable the user to interact with the image reconstruction apparatus, and/or communicate with Any device (eg, router, modem, etc.) that enables the image reconstruction apparatus to communicate with one or more other computing devices. Such communication may occur through input/output (I/O) interface 250 . Moreover, the image reconstruction device can also communicate with one or more networks (eg, local area network (LAN), wide area network (WAN) and/or public networks, such as the Internet) through the network adapter 260 . The network adapter 260 can communicate with other modules of the image reconstruction device through the bus 230 . It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the image reconstruction apparatus, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives And data backup storage system, etc.

FIG. 12 shows a block diagram of an imaging system according to an embodiment of the present application. The imaging system according to an embodiment of the present application will be described in detail below with reference to FIG. 12 .

As shown in FIG. 12 , an imaging system according to an embodiment of the present application includes a detector 1201 and an image reconstruction device 1203 . Wherein, the image reconstruction device 1203 may include the image reconstruction device shown in FIG. 9 or FIG. 10 ; the detector 1201 may include a radiation detector such as a PET detector, PET-CT detector, CT detector or MR detector. For detailed descriptions of these detectors, reference may be made to the prior art, which will not be repeated here.

Through the description of the above implementations, those skilled in the art can easily understand that the example implementations described here can be implemented by software, or by combining software with necessary hardware. The technical solutions according to the embodiments of the present application can be embodied in the form of software products, which can be stored in a computer-readable storage medium (which can be CD-ROM, U disk, mobile hard disk, etc.) or on the network, including several The computer program instructions enable a computing device (which may be a personal computer, a server, or a network device, etc.) to execute the above method according to the embodiments of the present application.

A software product may utilize any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: electrical connection with one or more conductors, portable disk, hard disk, random access memory (RAM), read only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

A computer readable storage medium may include a data signal carrying readable program code in baseband or as part of a carrier wave traveling as part of a data signal. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. A readable storage medium may also be any readable medium other than a readable storage medium that can send, propagate or transport a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the readable storage medium may be transmitted by any suitable medium, including but not limited to wireless, cable, optical cable, RF, etc., or any suitable combination of the above.

Program codes for performing the operations of the present application can be written in any combination of one or more programming languages, including object-oriented programming languages—such as Java, C++, etc., as well as conventional procedural programming Language—such as C or a similar programming language. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server to execute. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., using an Internet service provider). business to connect via the Internet).

The above-mentioned computer-readable medium carries one or more program instructions, and when the above-mentioned one or more program instructions are executed by one device, the computer-readable medium can realize the above-mentioned functions.

Those skilled in the art can understand that the above-mentioned modules can be distributed in the device according to the description of the embodiment, and corresponding changes can also be made in one or more devices that are only different from the embodiment. Multiple modules in the above embodiments may be combined into one module, or one module may be further split into multiple sub-modules.

Although the present application provides the operation steps of the method described in the above embodiments or flowcharts, more or fewer operation steps may be included in the method based on routine or no creative effort. In the steps where logically there is no necessary causal relationship, the execution order of these steps is not limited to the execution order provided in the embodiment of the present application.

Each embodiment in this specification is described in a progressive manner, the same and similar parts of each embodiment can be referred to each other, and each embodiment focuses on the differences from other embodiments.

The above is a detailed introduction to the embodiments of the present application. In this paper, specific examples are used to illustrate the principles and implementation methods of the present application. The descriptions of the above embodiments are only used to help understand the methods and core ideas of the present application. At the same time, changes or deformations made by those skilled in the art based on the ideas of the application, specific implementation methods and application scopes of the application all belong to the scope of protection of the application. To sum up, the contents of this specification should not be understood as limiting the application.

Claims (19)

1. An image reconstruction method, characterized in that, comprising:

   Using the correction parameters to construct the corresponding relationship between the initial single event data and the corrected single event data in the coincident event detected by the detector on the response line, wherein the initial single event data includes the gamma detected by the detector The number of single events in which each measured energy of the photon satisfies the preset condition;

   calculating the number of unscattered single events corresponding to the initial single event data within the coincident event by using the corresponding relationship;

   calculating the number of scattered coincidence events within the coincidence event using the number of coincidence events and the number of unscattered single events;

   Image reconstruction is performed by using the number of scattered coincidence events and the obtained number of random coincidence events and according to a preset image reconstruction algorithm.
2. The image reconstruction method according to claim 1, wherein, before constructing the correspondence, the method further comprises:

   downsampling a plurality of said response lines to obtain downsampled response lines,

   Correspondingly, after calculating the number of scattering coincidence events on the downsampling response line, the method further includes:

   Upsampling the downsampling response line according to a preset method to obtain an upsampling response line;

   The number of scatter coincidence events on the upsampled response line is calculated using the number of scatter coincidence events on the downsampled response line.
3. The image reconstruction method according to claim 2, wherein the preset method comprises: a bilinear interpolation method, a 4D linear interpolation method or a 5D linear interpolation method.
4. The image reconstruction method according to claim 3, wherein when the preset method is a 4D linear interpolation method, the above-mentioned values on the upsampling response line are calculated according to formula (I) and formula (II). Number of scatter coincidence events:

   

   

   in,

   

   Indicates the number of scattering coincidence events on the up-sampling response line; SC _{K, L} represent the number of scattering coincidence events on the down-sampling response line; I and J represent the numbers of the crystals constituting the up-sampling response line; K and L represent the number of crystals constituting the down-sampling response W _tot is the normalization factor; N is the number of up-sampling response lines included in a down-sampling response line; T ^I and T ^J represent the down-sampling center crystals corresponding to up-sampling crystals I and J, respectively. Sets of downsampled axially adjacent crystals, downsampled radially adjacent crystals, and downsampled opposite crystals;

   

   Indicates the weight of downsampling crystal K for upsampling crystal I;

   

   Indicates the weight of downsampling crystal L for upsampling crystal J.
5. The image reconstruction method according to claim 4, wherein the step of calculating the number of scattering coincidence events on the upsampling response line by using the number of scattering coincidence events on the downsampling response line comprises:

   calculating the ratio of the number of scatter coincident events to the number of coincident events on each of the downsampled response lines;

   calculating the number of scatter coincidence events on the upsampling response line according to the number of coincidence events on the upsampling response line included in each downsampling response line and the ratio.
6. The image reconstruction method according to any one of claims 1-5, wherein the initial single-event data is determined in the following manner:

   Splitting the coincident event into single events, counting the measured energy corresponding to each of the single events, and counting the number of single events corresponding to the same measured energy to obtain the initial single event data; or

   splitting the coincident event into single events, drawing at least one energy spectrum according to the probe position where the single event is located, and acquiring the initial single event data in each of the energy spectra; or

   Splitting the coincidence event into single events, drawing at least one energy spectrum according to the position of the probe where the single event is located, and dividing each energy spectrum into a plurality of sub-energy spectrums according to the time difference between gamma photons arriving at the probe, And acquiring the initial single-event data in each of the energy sub-spectrums.
7. The image reconstruction method according to claim 6, characterized in that, the number of said scattering coincidence events in said coincidence events is calculated using formula (III)-formula (VIII):

   SC≈SC ⁺ formula (Ⅲ)

   SC≈SC ^- Formula (IV)

   SC≈1/[(1/SC ⁺ )+(1/SC ^- )] Formula (Ⅴ)

   

   SC≈0.5(SC ⁺ +SC ^- ) Formula (Ⅶ)

   

   Wherein, SC ⁺ =S _tot -S _unsc ;

   

   S _tot is the total number of single events, and is twice the number of coincident events; S _unsc is the sum of the number of unscattered single events; SC is the number of scattered coincident events.
8. The image reconstruction method according to claim 1, wherein the step of using the corresponding relationship to calculate the number of unscattered single events corresponding to the initial single event data in the coincident event comprises:

   constructing an objective function of the corresponding relationship;

   calculating the value of the corrected single event data when the objective function takes the maximum value, and determining the value of the corrected single event data as the unscattered single event corresponding to the initial single event data in the coincident event number.
9. The image reconstruction method according to claim 8, wherein, for each of the acquired initial single-event data, the corresponding relationship is constructed according to formula (IX):

   

   in,

   

   X is the corrected single-event data, and X={X _j }, j=1,2,...,n, X _j is the real energy E of the gamma photon, E satisfies E _{t and} satisfies E _j ≤ E _t <E _j+1 The number of single events under; Y is the initial single event data, and Y={Y _i }, i=1, 2,..., m, Y _i is the measured energy E _d satisfying E _i ≤ E _d <E _i+1 number of single events;

   

   is the expected value of Y, and

   

   P is a correction parameter, and the element P _ij in P is the actual energy E d of the gamma photon whose real energy E _t satisfies E _i ≤ E _t < E _i+1 after the actual measurement and satisfies E _i ≤ _{E d <} E _{i+ 1} ; E _j and E _j+1 are the jth energy and j+1th energy in the preset energy window {E _j } respectively; E _i and E _i+1 are the measured energy window {E _i } in the i-th energy and the i+1-th energy; m, n, i and j are all positive integers.
10. The image reconstruction method according to claim 9, characterized in that, when m=n and {E _i }={E _j }, the i-th row and j-th column of the correction parameter P is calculated by formula (X). element:

    

    in,

    

    m is the initial single-event data obtained by scanning the gamma photon with the real energy E _t under the condition of ignoring the scattering coincidence event; the real energy E _t satisfies E _i ≤ E _t < E _i+1 ; i, j, s and m are positive integers.
11. The scattering correction method according to claim 9 or 10, wherein the real energy of the gamma photon comprises 202keV, 307keV, 511keV, 622keV or 1.27MeV.
12. The image reconstruction method according to claim 9, wherein the step of constructing the objective function of the corresponding relationship comprises:

    Utilize maximum likelihood method to construct described objective function, described objective function is determined by formula (Ⅺ):

    Φ(X)＝∑ _i (-∑ _j P _ij X _j +Y _i ln(∑ _j P _ij X _j ))-βR(X) Formula (Ⅺ)

    in,

    

    β is a hyperparameter; R(X) is a penalty item; M is a constant.
13. The image reconstruction method according to claim 12, wherein the step of calculating the value of the corrected single-event data when the objective function takes the maximum value comprises calculating the corrected value by formula (M) or formula (N) The value of single event data:

    

    or,

    

    ,in,

    

    in,

    

    and

    

    Both are values for correcting single-event data;

    

    Is the intermediate variable of the kth iteration; k is the number of iterations.
14. The image reconstruction method according to claim 1, wherein the preset image reconstruction algorithm comprises MLEM algorithm, OSEM algorithm or MAP-OSL algorithm.
15. An image reconstruction device, characterized in that it comprises:

    A construction unit configured to use correction parameters to construct a correspondence between initial single-event data and corrected single-event data in coincident events on the response line detected by the detector, wherein the initial single-event data is included in the Each measured energy of the gamma photon detected by the detector satisfies the number of single events under the corresponding specific conditions;

    a first calculation unit configured to use the correspondence to calculate the number of unscattered single events corresponding to the initial single event data within the coincident event;

    a second calculation unit configured to calculate the number of scattered coincidence events within the coincidence event using the number of coincidence events and the number of unscattered single events;

    An image reconstruction unit configured to use the number of scattered coincidence events and the obtained number of random coincidence events to perform image reconstruction according to a preset image reconstruction algorithm.
16. An image reconstruction device, characterized in that it comprises:

    a memory on which program code is stored;

    A processor coupled to the memory and implementing the method of any one of claims 1 to 14 when the program code is executed by the processor.
17. An imaging system, characterized in that it comprises:

    The image reconstruction device as claimed in claim 15 or 16;

    The detector is connected with the image reconstruction device.
18. The imaging system according to claim 17, wherein the detector comprises a PET detector, a PET-CT detector, a CT detector, or an MR detector.
19. A computer-readable storage medium, wherein program instructions are stored thereon, and the method according to any one of claims 1-14 is implemented when the program instructions are executed.

Images