CN112258506B - Positron emission tomography simulation method and system based on numerical calculation - Google Patents
Positron emission tomography simulation method and system based on numerical calculation Download PDFInfo
- Publication number
- CN112258506B CN112258506B CN202011293921.3A CN202011293921A CN112258506B CN 112258506 B CN112258506 B CN 112258506B CN 202011293921 A CN202011293921 A CN 202011293921A CN 112258506 B CN112258506 B CN 112258506B
- Authority
- CN
- China
- Prior art keywords
- model
- pet
- detectors
- sinogram
- detector
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10104—Positron emission tomography [PET]
Abstract
The invention relates to a positron emission tomography simulation method based on numerical calculation, which comprises the following steps: constructing a standard sample or a human tumor model; constructing a PET drug model; constructing a radionuclide drug emission and annihilation model; constructing a PET detector geometric model; constructing a photon pair coincidence detection calculation model; filling and displaying a PET raw data sinogram; reconstructing the sinogram to obtain a PET image; and storing and displaying the PET image. The invention also relates to a positron emission tomography simulation system based on numerical calculation. The invention does not need hardware and radiopharmaceutical support, and dynamically visualizes the PET imaging principle.
Description
Technical Field
The invention relates to the technical field of positron emission tomography simulation, in particular to a positron emission tomography simulation method and system based on numerical calculation.
Background
Positron Emission Computed Tomography (PET) devices belong to large medical imaging devices, and images of PET devices can display metabolic function differences of tissues and organs occurring at the early stage of diseases, so that early diagnosis is achieved, a foundation is laid for early treatment of diseases, and therefore attention is paid more and more, and application is more and more extensive. However, in the teaching process, the problems of abstract and incomprehensible imaging theory, expensive and large-scale equipment, ionizing radiation hazard, special requirements of experimental places and the like exist, so that experimental teaching is difficult to develop or cannot be developed at all, and adverse effects are caused to the culture of related subjects or researchers.
Disclosure of Invention
The invention aims to solve the technical problem of providing a positron emission tomography simulation method and system based on numerical calculation, which do not need hardware and radiopharmaceutical support and dynamically visualize the PET imaging principle.
The technical scheme adopted by the invention for solving the technical problems is as follows: the positron emission tomography simulation method based on numerical calculation comprises the following steps:
(1) Constructing a standard sample or a human tumor model;
(2) Constructing a PET drug model;
(3) Constructing a radionuclide drug emission and annihilation model;
(4) Constructing a PET detector geometric model;
(5) Constructing a photon pair coincidence detection calculation model;
(6) Filling and displaying a PET raw data sinogram;
(7) And reconstructing the sinogram to obtain a PET image.
The standard sample or the human tumor model constructed in the step (1) comprises a standard sample model, a resolution test model and a human tumor model.
The standard sample model comprises the shape, the size and the activity difference of the standard sample; the resolution test model comprises shape, size, activity difference, spacing, and background activity; the human tumor model includes a digital human three-dimensional matrix, a tissue type of each voxel, and a manually delineated or preset tumor region.
The PET drug model constructed in the step (2) comprises drug species, specific absorption capacity of the drug to various human tissues or tumors, radionuclide activity, energy and half-life.
The radionuclide drug emission and annihilation model constructed in the step (3) comprises range randomness of positrons, emitting direction randomness of annihilation photon pairs and absorption randomness of annihilation photons by human tissues.
The PET detector geometric model constructed in the step (4) comprises the radius of the detector ring, the size of the detection units, the number of the detection units, the detection efficiency, the number of the detector rings, the numbering rule of the detectors, the grid state and the detection visual field, and the size of the coincidence time window is calculated according to the detection visual field.
In the step (5), when detecting single-ring imaging, the calculation model is as follows: calculating the numbers of two detectors detected by a detector ring in a time window under the conditions that the emission direction of annihilation photon pairs is random and the annihilation photon pairs are randomly absorbed by tissues on a transmission path, wherein a connecting line between the numbers of the two detectors is an LOR detection line; repeatedly calculating to complete all annihilation photon pair detection events to form a large number of LOR detection lines; when multi-ring 2D imaging is detected, when the grid state of the PET detector geometric model is set to be out, it indicates that photon pairs can only emit and be detected by two detectors in the same ring, and cannot be detected by two detectors in different rings, namely, a 2D multi-layer detection mode, and the calculation model is as follows: performing loop-by-loop calculation by adopting a calculation model during detection of single-loop imaging; when multi-ring 3D imaging is detected, when the grid state of the geometric PET detector model is set to be in, the photon pair can be detected by two detectors in the same ring and can also be detected by two detectors in different rings, namely a 3D multi-layer detection mode, and the calculation model is as follows: the gamma photons exit in a volume that corresponds to any two detectors on the entire detector ring.
In the step (6), when single-ring imaging is detected, a two-dimensional zero matrix is initialized as a sinogram, and rows and columns are the total number of detectors; if two detectors in the time window detect a pair of gamma photons, adding 1 to the positions of the corresponding rows/columns in the sinogram, which are numbered by the detectors, and refreshing and displaying the sinogram; continuously circulating until all detection events are completed; when multi-ring 2D imaging is detected, a three-dimensional zero matrix is initialized to serve as a sinogram, rows and columns are the total number of detectors, and the third dimension of the sinogram is the number of detector rings; when multi-ring 3D imaging is detected, a three-dimensional zero matrix is initialized to be used as a sinogram, rows and columns are the total number of detectors, and the third dimension of the sinogram is the square of the number of detector rings.
And (7) storing and displaying the reconstructed PET image, wherein the multilayer image acquired by 2D/3D is subjected to three-dimensional reconstruction display or colorized body rotation display of the metabolic abnormal region, and the display of the three-dimensional structure of the functional metabolic abnormal region is increased.
The technical scheme adopted by the invention for solving the technical problems is as follows: there is also provided a numerical calculation based positron emission tomography simulation system comprising: the standard sample or human tumor model building module is used for building a measurement sample for the simulation system; the PET medicament model building module is used for building a characteristic model of a common PET medicament; the radionuclide drug emission and annihilation model construction module is used for calculating and displaying positron emission and annihilation processes; the PET detector geometric model construction module is used for constructing a detector geometric structure model; a photon pair coincidence detection computation module for computing an LOR detection event; and the PET raw data sinogram filling and displaying module is used for filling and displaying the raw data.
Advantageous effects
Due to the adoption of the technical scheme, compared with the prior art, the invention has the following advantages and positive effects: the invention does not need hardware (the price of each hardware is about ten million), is not limited by the hardware, and realizes low-cost batch experiment operation and training. More importantly, the PET real machine imaging needs to use a medicine marked with positive electron nuclide, and the medicine has radioactivity, so the medicine is strictly controlled by the departments of national environmental protection, public security, sanitation and the like.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is an exemplary diagram of constructing a standard or resolution model according to a first embodiment of the present invention;
FIG. 3 is an exemplary illustration of constructing a human tumor model in a first embodiment of the present invention;
FIG. 4 is an exemplary diagram of a geometric model of a probe constructed in accordance with a first embodiment of the present invention;
FIG. 5 is a graph of sinogram dynamic filling 2D display and effect of stereoscopic display in a first embodiment of the invention;
6-9 are images and stereoscopic display effect diagrams obtained by implementing reconstruction with different algorithms according to the first embodiment of the present invention;
FIGS. 10-14 are exemplary views of a PET image storage and display according to a first embodiment of the present invention;
FIG. 15 is a schematic diagram of a 2D/3D acquisition mode employed in a first embodiment of the present invention;
fig. 16 is a schematic configuration diagram of the second embodiment of the present invention.
Detailed Description
The invention will be further illustrated with reference to the following specific examples. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Further, it should be understood that various changes or modifications of the present invention may be made by those skilled in the art after reading the teaching of the present invention, and such equivalents may fall within the scope of the present invention as defined in the appended claims.
The first embodiment of the present invention relates to a positron emission tomography simulation method based on numerical calculation, as shown in fig. 1, including the steps of: step 1: constructing a standard sample or a human tumor model; step 2: constructing a PET drug model; and step 3: constructing a radionuclide drug emission and annihilation model; and 4, step 4: constructing a PET detector geometric model; and 5: constructing a photon pair coincidence detection calculation model; step 6: filling and displaying a PET raw data sinogram; and 7: reconstructing the sinogram to obtain a PET image; and 8: and storing and displaying the PET image. The method comprises the following specific steps:
in step 1, sample models are constructed according to different experimental requirements (see fig. 2). The standard model features need to include the shape, size, activity difference, etc. of the standard. Resolution model features need to include shape, size, activity difference, spacing, background activity, etc. As shown in fig. 3, the human tumor model is a digital human three-dimensional matrix, a tissue type of each voxel, a manually delineated or preset tumor region, and the like.
In step 2, a PET drug model is constructed, including the type and activity of the positively charged electron nuclide labeled by the drug. Classes include, but are not limited to, F18, C-11, N-13, O-15, and the like. After the positron nuclide species is constructed, the half-life period of the positron nuclide species is correspondingly determined. Such as F-18, with a half-life T1/2 of 110 minutes.
In step 4, a geometric model of the PET detector (see fig. 4) is constructed, including the radius of the detector ring, the size of the detection units, the number of the detection units, the detection efficiency, the number of the detector rings, the numbering rule of the detector, the grid state (Septa in or out), and the detection field of view, and the size of the coincidence time window (Δ t = the distance in the long axis direction of the field of view/the light speed) is calculated according to the field of view.
In step 5, a photon pair coincidence detection single-ring calculation model is constructed: for only one detector ring, the serial numbers of two detectors detected on the detector ring in a time window are calculated on the basis of the conditions that the emission direction of annihilation photon pairs is random and the annihilation photon pairs are randomly absorbed by tissues on a transmission path, and a connecting line between the two serial numbers is an LOR detection line. And repeatedly calculating to complete all annihilation photon pair detection events to form a large number of LOR detection lines, wherein the number is determined by model parameters such as total activity of the medicine, absorption coefficient, detection efficiency and the like.
For the number of multi-detector rings and the 2D/3D measurement mode, the acquisition mode is as shown in fig. 15, and the following method is adopted for calculation:
for a plurality of detectors, when the state of a septa grid is set to be out, the photon pair can only emit and be detected by two detectors in the same ring, and cannot be detected by two detectors in different rings, namely the photon pair accords with a detection multi-ring 2D imaging calculation model, namely a 2D multi-layer detection mode; the calculation model only needs to be collected according to the single-loop mode of claim 9 and then carries out loop-by-loop calculation. The sinogram is expanded into a 3-dimensional matrix, and the third dimension is the number of detector rings;
for a multi-detector, when the state of a septa grid is set to be in, the photon pair accords with a detection multi-ring 3D imaging calculation model, namely the photon pair can be detected by two detectors in the same ring and can also be detected by two detectors in different rings, namely a 3D multi-layer detection mode; the calculation model is expanded into: the gamma photons exit in a volume that coincides with the detectors, which can be any two detectors on the entire detector ring. The corresponding sinogram is expanded into a 3-dimensional matrix, the third dimension being the square of the number of detector loops.
In step 6, filling and displaying a sinogram of PET raw data: during single-ring detection, a two-dimensional zero matrix is initialized as a sinogram, and rows and columns are the total number of detectors; if two detectors in the time window detect a pair of gamma photons, adding 1 to the position of the detector number of the corresponding row/column in the sinogram, and refreshing and displaying the sinogram (see fig. 5); and continuously circulating until all detection events are completed. During multi-ring 2D or 3D detection, a three-dimensional zero matrix is initialized to serve as a sinogram array, and the size of the third dimension is the number of detector rings or the square of the number of the detector rings respectively.
In the step 7, reconstructing the sinogram to obtain a PET image, and reconstructing two-dimensional or three-dimensional sinogram data to obtain the PET image; the reconstruction method respectively adopts back projection reconstruction (BP), filtering back projection reconstruction (FBP), iterative reconstruction method or more other methods; these methods are known methods, and are not described in detail herein. Fig. 6 shows an image and a stereoscopic display effect reconstructed by using a BP algorithm, fig. 7 shows an image and a stereoscopic display effect reconstructed by using an FBP algorithm (Ramp + Sinc filter), fig. 8 shows an image and a stereoscopic display effect reconstructed by using an FBP algorithm (Ramp + Hanning filter), and fig. 9 shows an image and a stereoscopic display effect reconstructed by using an ART algorithm (iteration 10 times, iteration factor 0.01).
In step 8, storing and displaying the PET image, and storing original data for offline analysis; the multilayer images acquired by 2D/3D can be used for three-dimensional reconstruction display or colorized body rotation display of metabolic disorder areas. FIG. 10 is a raw data image and a reconstructed PET image of brain imaging; figure 11 is a sinogram for layers 11, 22, 33, 44, 55, 32 respectively; FIG. 12 is a PET image corresponding to slices 11, 22, 33, 44, 55, and 32, respectively; FIG. 13 is a three-dimensional reconstruction of a tumor shown after selection of a center point of the tumor zone; FIG. 14 is a color body rotation display of tumor area.
A second embodiment of the present invention relates to a positron emission tomography simulation system based on numerical calculation, including: the standard sample or human tumor model building module is used for building a measurement sample for the simulation system; the PET medicine model building module is used for building a characteristic model of a common PET medicine; the radionuclide drug emission and annihilation model construction module is used for calculating and displaying positron emission and annihilation processes; the PET detector geometric model construction module is used for constructing a detector geometric structure model; a photon pair coincidence detection computation module for computing an LOR detection event; and the PET raw data sinogram filling and displaying module is used for filling and displaying the raw data.
The system can be used for developing experimental practical teaching of a PET imaging principle, comprises experimental items such as positron emission and annihilation dynamic display, photon pair coincidence detection, LOR data line display, sinogram filling, PET image reconstruction, PET spatial resolution test, signal-to-noise ratio and contrast-to-noise ratio, tumor metabolism cold and hot area, tumor three-dimensional reconstruction, body rotation display of a metabolic abnormal area, 2D/3D tumor imaging and the like, and can also be used for developing pre-research and test calibration of PET equipment; constructing a PET digital map and the like.
Claims (9)
1. A positron emission tomography simulation method based on numerical calculation is characterized by comprising the following steps:
(1) Constructing a standard sample model, a resolution test model and a human tumor model;
(2) Constructing a PET drug model;
(3) Constructing a radionuclide drug emission and annihilation model;
(4) Constructing a PET detector geometric model;
(5) Constructing a photon pair coincidence detection calculation model;
(6) Filling and displaying a PET raw data sinogram;
(7) And reconstructing the sinogram to obtain a PET image.
2. The positron emission tomography simulation method based on numerical calculation as recited in claim 1, wherein the standard sample model includes a shape, a size, and an activity difference of a standard sample; the resolution test model comprises shape, size, activity difference, spacing, and background activity; the human tumor model includes a digital human three-dimensional matrix, a tissue type of each voxel, and a manually delineated or preset tumor region.
3. The positron emission tomography simulation method based on numerical calculation as claimed in claim 1, wherein the PET drug model constructed in the step (2) includes a drug species, a specific absorption capacity of the drug to various human tissues or tumors, a radionuclide activity, an energy and a half-life.
4. The positron emission tomography simulation method based on numerical calculation as claimed in claim 1, wherein the radionuclide drug emission and annihilation model constructed in step (3) comprises range randomness of positrons, emission direction randomness of annihilation photon pairs, and absorption randomness of annihilation photons by human tissues.
5. The positron emission tomography simulation method based on numerical calculation as claimed in claim 1, wherein the PET detector geometric model constructed in the step (4) includes radius of detector ring, size of detection unit, number of detection unit, detection efficiency, number of detector ring, detector numbering rule, grid state and detection view, and the coincidence time window size is calculated according to the detection view.
6. The positron emission tomography simulation method based on numerical calculation as described in claim 1, wherein in said step (5), when single-ring imaging is detected, the calculation model is: calculating the numbers of two detectors detected by a detector ring in a time window on the basis of the conditions that annihilation photon pairs are randomly emitted and randomly absorbed by tissues on a transmission path, wherein a connecting line between the numbers of the two detectors is an LOR detection line; repeatedly calculating to complete all annihilation photon pair detection events to form a large number of LOR detection lines; when multi-ring 2D imaging is detected, when the grid state of the geometric model of the PET detector is set to be out, the photon pair can only be emitted and detected by two detectors in the same ring, and cannot be detected by two detectors in different rings, namely, the geometric model of the PET detector is a 2D multi-layer detection mode, and the calculation model is as follows: performing loop-by-loop calculation by adopting a calculation model during detection of single-loop imaging; when multi-ring 3D imaging is detected, when the grid state of the geometric PET detector model is set to be in, the photon pair can be detected by two detectors in the same ring and can also be detected by two detectors in different rings, namely a 3D multi-layer detection mode, and the calculation model is as follows: the gamma photons exit in a volume that corresponds to any two detectors on the entire detector ring.
7. The positron emission tomography simulation method based on numerical calculation as claimed in claim 1, wherein in the step (6), when a single ring is detected, a two-dimensional zero matrix is initialized as a sinogram, and rows and columns are the total number of detectors; if two detectors in the time window detect a pair of gamma photons, adding 1 to the positions of the corresponding rows/columns in the sinogram, which are numbered by the detectors, and refreshing and displaying the sinogram; continuously circulating until all detection events are completed; when multi-ring 2D imaging is detected, a three-dimensional zero matrix is initialized to serve as a sinogram, rows and columns are the total number of detectors, and the third dimension of the sinogram is the number of detector rings; when multi-ring 3D imaging is detected, a three-dimensional zero matrix is initialized to be used as a sinogram, rows and columns are the total number of detectors, and the third dimension of the sinogram is the square of the number of detector rings.
8. The positron emission tomography simulation method based on numerical calculation as claimed in claim 1, wherein after the step (7), the PET image obtained by reconstruction is stored and displayed, wherein the 2D/3D acquired multi-slice image is subjected to three-dimensional reconstruction display or colored body rotation display of metabolic abnormality regions, and display of three-dimensional structures of functional metabolic abnormality regions is increased.
9. A positron emission tomography simulation system based on numerical computation, comprising: the standard sample and human tumor model building module is used for building a measurement sample for a simulation system; the PET medicament model building module is used for building a characteristic model of a common PET medicament; the radionuclide drug emission and annihilation model construction module is used for calculating and displaying positron emission and annihilation processes; the PET detector geometric model construction module is used for constructing a detector geometric structure model; a photon pair coincidence detection computation module for computing LOR detection events; and the PET raw data sinogram filling and displaying module is used for filling and displaying the raw data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011293921.3A CN112258506B (en) | 2020-11-18 | 2020-11-18 | Positron emission tomography simulation method and system based on numerical calculation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011293921.3A CN112258506B (en) | 2020-11-18 | 2020-11-18 | Positron emission tomography simulation method and system based on numerical calculation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112258506A CN112258506A (en) | 2021-01-22 |
| CN112258506B true CN112258506B (en) | 2022-11-25 |
Family
ID=74266162
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011293921.3A Active CN112258506B (en) | 2020-11-18 | 2020-11-18 | Positron emission tomography simulation method and system based on numerical calculation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112258506B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113069138B (en) * | 2021-03-23 | 2023-06-30 | 上海联影医疗科技股份有限公司 | Positron emission tomography device, coincidence efficiency detection method and normalization method |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101856236A (en) * | 2010-06-13 | 2010-10-13 | 苏州瑞派宁科技有限公司 | Positron emission tomography (PET) method and device with application adaptability |
| CN103099637A (en) * | 2013-02-21 | 2013-05-15 | 清华大学 | Image reconstruction method for dual panel position-emission tomography (PET) detector |
| CN105431884A (en) * | 2013-04-11 | 2016-03-23 | 皇家飞利浦有限公司 | Method for modeling and accounting for cascade gammas in images |
| CN107468269A (en) * | 2017-09-18 | 2017-12-15 | 南京瑞派宁信息科技有限公司 | A kind of positron emission tomography device and method |
| CN107635469A (en) * | 2015-05-19 | 2018-01-26 | 皇家飞利浦有限公司 | The estimation of the decay pattern met based on the scattering in PET system |
| CN108109182A (en) * | 2016-11-24 | 2018-06-01 | 上海东软医疗科技有限公司 | A kind of PET image reconstruction method and device |
| CN109498044A (en) * | 2018-10-15 | 2019-03-22 | 华中科技大学 | A kind of PET annular detection imaging system based on flash fiber |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103393434B (en) * | 2013-08-09 | 2015-04-08 | 中国科学院高能物理研究所 | Method for obtaining system response model of positron emission tomography and method for image reconstruction |
| US11288847B2 (en) * | 2018-11-09 | 2022-03-29 | Siemens Medical Solutions Usa, Inc. | Double scatter simulation for improved reconstruction of positron emission tomography data |
| CN109480892B (en) * | 2018-12-29 | 2022-11-04 | 上海联影医疗科技股份有限公司 | Image generation method |
-
2020
- 2020-11-18 CN CN202011293921.3A patent/CN112258506B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101856236A (en) * | 2010-06-13 | 2010-10-13 | 苏州瑞派宁科技有限公司 | Positron emission tomography (PET) method and device with application adaptability |
| CN103099637A (en) * | 2013-02-21 | 2013-05-15 | 清华大学 | Image reconstruction method for dual panel position-emission tomography (PET) detector |
| CN105431884A (en) * | 2013-04-11 | 2016-03-23 | 皇家飞利浦有限公司 | Method for modeling and accounting for cascade gammas in images |
| CN107635469A (en) * | 2015-05-19 | 2018-01-26 | 皇家飞利浦有限公司 | The estimation of the decay pattern met based on the scattering in PET system |
| CN108109182A (en) * | 2016-11-24 | 2018-06-01 | 上海东软医疗科技有限公司 | A kind of PET image reconstruction method and device |
| CN107468269A (en) * | 2017-09-18 | 2017-12-15 | 南京瑞派宁信息科技有限公司 | A kind of positron emission tomography device and method |
| CN109498044A (en) * | 2018-10-15 | 2019-03-22 | 华中科技大学 | A kind of PET annular detection imaging system based on flash fiber |
Non-Patent Citations (1)
| Title |
|---|
| 基于GATE的小动物PET原型机仿真验证与性能评估;黄衍超等;《中国生物医学工程学报》;20200630;第39卷(第3期);第318-326页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112258506A (en) | 2021-01-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP6440230B2 (en) | Multi-modality imaging system and method | |
| Tong et al. | Image reconstruction for PET/CT scanners: past achievements and future challenges | |
| Zhou et al. | Fast and efficient fully 3D PET image reconstruction using sparse system matrix factorization with GPU acceleration | |
| CN109615674B (en) | Dynamic double-tracing PET reconstruction method based on mixed loss function 3D CNN | |
| Gaudin et al. | Performance simulation of an ultrahigh resolution brain PET scanner using 1.2-mm pixel detectors | |
| CN109658390B (en) | Region of interest extraction method for positron detection sinusoidal matrix diagram | |
| US8509504B2 (en) | Point spread function radial component implementation in Joseph's forward projector | |
| Aklan et al. | GATE Monte Carlo simulations for variations of an integrated PET/MR hybrid imaging system based on the Biograph mMR model | |
| CN112258506B (en) | Positron emission tomography simulation method and system based on numerical calculation | |
| US10222490B2 (en) | PET scanner with emission and transmission structures in a checkerboard configuration | |
| Groiselle et al. | 3D PET list-mode iterative reconstruction using time-of-flight information | |
| Samanta et al. | Performance comparison of a dedicated total breast PET system with a clinical whole-body PET system: a simulation study | |
| Farncombe | Functional dynamic SPECT imaging using a single slow camera rotation | |
| CN113081016A (en) | Whole-body PET imaging system | |
| CN106859686A (en) | Imaging method and imaging system | |
| El Bitar et al. | Acceleration of fully 3D Monte Carlo based system matrix computation for image reconstruction in small animal SPECT | |
| Jian et al. | Applications of the line-of-response probability density function resolution model in PET list mode reconstruction | |
| Alhassen et al. | Ultrafast multipinhole single photon emission computed tomography iterative reconstruction using CUDA | |
| KR20200086814A (en) | image reconstruction method to reconstruct the image by correcting the response depth information included in the observed data using the flight time information of the positron emission tomography | |
| El Fakhri et al. | Quantitative simultaneous/sup 99m/Tc//sup 123/I SPECT: design study and validation with Monte Carlo simulations and physical acquisitions | |
| CN109567845B (en) | Method for constructing random coincidence cases of PET system by case mixing method | |
| US20230206516A1 (en) | Scatter estimation for pet from image-based convolutional neural network | |
| Wu et al. | Studies of DOI estimation method in LOR data for 3D PET list-mode reconstruction | |
| KR20190013161A (en) | image reconstruction method of positron emission tomography with scintillation crystal disposed obliquely | |
| Han | Image reconstruction in quantitaive cardiac SPECT with varying focal-length fan-beam collimators |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |