CN112274169A - PET imaging system and method based on linear track projection data - Google Patents
PET imaging system and method based on linear track projection data Download PDFInfo
- Publication number
- CN112274169A CN112274169A CN202010983394.2A CN202010983394A CN112274169A CN 112274169 A CN112274169 A CN 112274169A CN 202010983394 A CN202010983394 A CN 202010983394A CN 112274169 A CN112274169 A CN 112274169A
- Authority
- CN
- China
- Prior art keywords
- module
- projection data
- imaging system
- pet imaging
- coincidence
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 32
- 238000012879 PET imaging Methods 0.000 title claims abstract description 26
- 239000013078 crystal Substances 0.000 claims abstract description 27
- 238000001514 detection method Methods 0.000 claims abstract description 27
- 238000006243 chemical reaction Methods 0.000 claims abstract description 19
- 238000012545 processing Methods 0.000 claims abstract description 17
- 238000003384 imaging method Methods 0.000 claims abstract description 15
- 238000012636 positron electron tomography Methods 0.000 claims abstract 3
- 238000005070 sampling Methods 0.000 claims description 18
- 238000004458 analytical method Methods 0.000 claims description 11
- 238000012216 screening Methods 0.000 claims description 6
- 230000008707 rearrangement Effects 0.000 claims description 5
- 238000012800 visualization Methods 0.000 claims description 5
- 230000004927 fusion Effects 0.000 claims description 4
- 238000002600 positron emission tomography Methods 0.000 description 13
- 238000009825 accumulation Methods 0.000 description 6
- 206010028980 Neoplasm Diseases 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 201000010099 disease Diseases 0.000 description 2
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 2
- 230000003902 lesion Effects 0.000 description 2
- 238000013139 quantization Methods 0.000 description 2
- 206010027476 Metastases Diseases 0.000 description 1
- 241001465754 Metazoa Species 0.000 description 1
- 208000012902 Nervous system disease Diseases 0.000 description 1
- 238000005842 biochemical reaction Methods 0.000 description 1
- 210000004556 brain Anatomy 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000003759 clinical diagnosis Methods 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 238000002059 diagnostic imaging Methods 0.000 description 1
- 238000013399 early diagnosis Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000005714 functional activity Effects 0.000 description 1
- 238000001727 in vivo Methods 0.000 description 1
- 230000002503 metabolic effect Effects 0.000 description 1
- 230000009401 metastasis Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 210000000056 organ Anatomy 0.000 description 1
- 230000008289 pathophysiological mechanism Effects 0.000 description 1
- 238000004393 prognosis Methods 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 239000000700 radioactive tracer Substances 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
Images
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
- A61B6/02—Arrangements for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
- A61B6/03—Computed tomography [CT]
- A61B6/037—Emission tomography
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
- A61B6/42—Arrangements for detecting radiation specially adapted for radiation diagnosis
- A61B6/4208—Arrangements for detecting radiation specially adapted for radiation diagnosis characterised by using a particular type of detector
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
- A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
Landscapes
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- Medical Informatics (AREA)
- Pathology (AREA)
- Heart & Thoracic Surgery (AREA)
- High Energy & Nuclear Physics (AREA)
- Physics & Mathematics (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Optics & Photonics (AREA)
- Veterinary Medicine (AREA)
- Radiology & Medical Imaging (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Molecular Biology (AREA)
- Surgery (AREA)
- Animal Behavior & Ethology (AREA)
- General Health & Medical Sciences (AREA)
- Public Health (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Nuclear Medicine (AREA)
Abstract
The invention provides a PET imaging system and a method based on linear trajectory projection data, which comprises the following steps: the detection module receives gamma photons emitted by an object to be detected and converts the gamma photons into visible light by utilizing the characteristics of the scintillation crystal; the photoelectric conversion module converts the visible light signal into an electric signal; the high-speed reading electronic module converts the electric signal into a flicker pulse signal; the coincidence processing module screens coincidence events from the coincidence discrimination and obtains projection data of the coincidence events; the image reconstruction module reconstructs an ROI area image by using the projection data; the high-precision stepping motor module moves the detected object in the axial direction, and the over-range detection is met. The invention aims at the problem of axial missing angles in a PET system, and solves the problems of small axial imaging range and low axial resolution in the PET system by utilizing a linear track scanning mode.
Description
Technical Field
The invention relates to the field of radiation detection imaging, in particular to a PET imaging system and method based on linear trajectory projection data.
Background
Positron Emission Tomography (PET) is a novel noninvasive medical imaging technology, can noninvasively, quantitatively and dynamically evaluate the metabolic level, biochemical reaction and functional activity of various organs and tissues in an animal or a human body in vivo by describing the position distribution and the change of radioactivity of a radioactive tracer in a body, can be applied to early diagnosis and treatment stages of tumors, heart and brain system diseases and nervous system diseases, and plays a unique role in early detection of diseases, pathophysiological mechanism research, curative effect monitoring, prognosis evaluation and the like.
PET reconstruction methods can be roughly classified into analytical methods represented by FBP and iterative methods represented by ML-EM. The analytical method has the advantages of reconstruction speed, which can be applied to large-scale projects such as human body three-dimensional whole body reconstruction, and the iterative method has the advantages of introducing prior information and a noise model and having better reconstruction effect under low photon number. Both algorithms are based on complete projection data.
In practical clinical diagnosis, 80% of functions of PET are used for detecting tumor lesions, and are limited by the influence of cost and imaging algorithm, the imaging range of the traditional PET system cannot completely cover the whole human body, and the detection of tumor metastasis and lesions cannot provide a definite diagnosis method
Conventional PET systems are based on a closed ring structure, acquire emission data in various directions by a ring detector, and obtain coincidence events therein by coincidence screening, where the obtained projection values cover the entire FOV, but lack effective imaging and detection means in the axial direction.
Therefore, aiming at the problems, the invention provides a PET imaging system and a PET imaging method based on linear track projection data, the method combines the advantages of high reconstruction speed of an analytic method and noise model introduction of an iterative method, and the axial direction movement of the detected object is carried out by utilizing a high-precision stepping motor module so as to meet the axial resolution required by imaging.
Disclosure of Invention
The invention aims to provide a PET imaging system and a PET imaging method based on linear track projection data, and further solves the problems of small axial imaging range and low axial resolution in a PET system.
In order to solve the technical problems, a PET imaging system based on linear track projection data and a new scanning mode are provided.
A PET imaging system based on linear trajectory projection data, the system comprising: the detection module comprises a scintillation crystal module, a photoelectric conversion module and a high-speed reading electronic module;
the scintillation crystal module is connected with the photoelectric conversion module and receives a high-energy gamma photon signal emitted from a detected object and converts the high-energy gamma photon signal into a visible light signal;
the photoelectric conversion module is used for converting visible light signals into electric signals;
the high-speed reading electronic module converts the electric signal into a flicker pulse signal;
the coincidence processing module screens coincidence events from the pulse signals by utilizing coincidence screening and obtains projection data of the system;
the image reconstruction module reconstructs an ROI area image by utilizing the projection data;
the high-precision motor stepping module moves the detected object in the axial direction to meet the requirement of over-range detection;
and the developing module is used for splicing all axial imaging images to obtain a complete reconstructed image.
Preferably, the detection module consists of a LYSO crystal module and a BGO crystal module.
Preferably, the photoelectric conversion module is formed by combining a PMT module and an SiPM module array.
Preferably, the high-speed readout electronics module employs amplitude sampling, as opposed to conventional time-based sampling, thereby improving the efficiency of sampling the leading edge of the scintillation pulse.
Preferably, the coincidence processing module utilizes an ML-EM algorithm therein, and the formula is:
the pile-up events are processed to separate the unidentifiable pulse signals into individual impulse function forms.
Preferably, the coincidence processing module rearranges the detected list data, and the LORs of all detector crystals connected to the annular PET detection system can be regarded as a cluster of angularly uniformly distributed parallel projections, and the cluster of projections can be rearranged into a sinogram format according to the angle and the distance from the central point.
Preferably, the method adopted by the image reconstruction module is a method combining the advantages of rapid reconstruction of an analytic method and an iterative method, such as introduction of a noise model and the like.
Preferably, the high-precision motor stepping module has the step length of each transmission of the high-precision motor stepping module being the maximum axial resolution S of the system.
Preferably, the visualization module actually operates on the registration and fusion of the images.
A PET imaging method based on linear trajectory projection data, the method comprising the steps of:
step S1: the detection module converts high-energy gamma photons into visible light;
step S2: the photoelectric conversion module is used for converting the visible light signal into an electric signal;
step S3: high-speed reading electronic module converts electric signal into flash pulse signal
Step S4: the coincidence processing module is used for performing coincidence discrimination on the scintillation pulse signals and obtaining projection data through data rearrangement;
step S5: obtaining a reconstructed image by utilizing a reconstruction algorithm based on a straight-line track according to the projection data;
step S6: the high-precision motor stepping module moves the detected object in the axial direction and repeats the steps S1-S4;
step S7: and the imaging module splices all the axial images to obtain a complete reconstructed image.
Has the advantages that:
compared with the prior art, the invention solves the problems of small axial imaging range and low axial resolution in the PET system by utilizing a linear track scanning mode aiming at the problem of axial missing angles in the PET system.
Drawings
FIG. 1 is a flow chart of a PET imaging system and method based on linear trajectory projection data in accordance with the present invention;
FIG. 2 is a block diagram of a PET imaging system and method of the present invention based on linear trajectory projection data;
FIG. 3 is a schematic diagram of data rearrangement in a PET imaging system and method based on linear trajectory projection data according to the present invention.
In the figure: 100. a detection module; 200. a coincidence processing module; 300. an image reconstruction module; 400. a high precision stepper motor module; 500. a visualization module; 110. a scintillation crystal module; 120. a photoelectric conversion module; 130. a high-speed readout electronics module; 111. a LYSO crystal module; 112. a BGO crystal module; 121. a SiPM module; 122. a PMT module; 210 stacking the screening module; 220 stacking recovery module; 230. a data reordering module 230; 310. a fast analytic method reconstruction module; 320. and a Poisson model iterative reconstruction module.
Detailed Description
The present invention will be further described with reference to the following specific examples. It should be understood that the following examples are illustrative only and are not intended to limit the scope of the present invention.
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
In order to solve the technical problems, a PET imaging system based on linear track projection data and a new scanning mode are provided.
The invention specifically adopts the following scheme:
step S1: the detection module converts the high-energy gamma photons into visible light;
step S2: the photoelectric conversion module is used for converting the visible light signal into an electric signal;
step S3: high-speed reading electronic module converts electric signal into flash pulse signal
Step S4: the coincidence processing module is used for performing coincidence discrimination on the scintillation pulse signals and obtaining projection data through data rearrangement;
step S5: obtaining a reconstructed image by utilizing a reconstruction algorithm based on a straight-line track according to the projection data;
step S6: the high-precision motor stepping module moves the detected object in the axial direction and repeats the steps S1-S5;
step S7: and the imaging module splices all the axial images to obtain a complete reconstructed image.
The detection module in the S1 is connected with the photoelectric conversion module and receives high-energy gamma photon signals emitted from the detected object and converts the high-energy gamma photon signals into visible light signals, and the detection module consists of a LYSO crystal module and a BGO crystal module.
The photoelectric conversion module in the S2 converts the visible light signal into an electrical signal, and is formed by combining a PMT module and an SiPM module array.
The high-speed readout electronics module in the S3 converts the electrical signal into a scintillation pulse signal, which is different from the conventional time-based sampling, and adopts amplitude sampling, thereby improving the sampling efficiency of the rising edge of the scintillation pulse.
The S4 coincidence processing module screens coincidence events from the pulse signals by coincidence discrimination and obtains projection data of the system, wherein an ML-EM algorithm is used, and the formula is as follows:
the pile-up events are processed to separate the unidentifiable pulse signals into individual impulse function forms, and projection data is obtained by data rearrangement.
The image reconstruction module of the S5 reconstructs an ROI region image using the projection data, and the adopted method is a method that combines the advantages of fast reconstruction of an analytic method and an iterative method, such as introduction of a noise model.
S6 high-precision motor stepping module, the high-precision motor stepping module moves the detected object in the axial direction to meet the over-range detection, and the transmission step length is the maximum axial resolution S of the system each time
And the S7 visualization module is used for splicing all axial imaging images to obtain a complete reconstructed image, and the actual operation content is the registration and fusion of the images.
Fig. 2 shows a block diagram of a PET imaging system and method based on linear trajectory projection data, which can be mainly divided into a detection module 100, a coincidence processing module 200, an image reconstruction module 300, a high-precision stepper motor module 400, and a visualization module 500.
Wherein the detection module 100 is connected to the coincidence processing module 200, and the detection module 100 is used for converting gamma photons into a form of scintillation pulses to facilitate subsequent data processing, and comprises a scintillation crystal module 110, a photoelectric conversion module 120, and a high-speed readout electronics module 130. The gamma photon firstly hits the scintillation crystal module 110, the scintillation crystal module 110 further comprises 2 different crystal modules LYSO crystal module 111 and BGO crystal module 112, the composite crystal converts the gamma photon from high-energy photon to visible light, the visible light generated from the scintillation crystal module 110 is input to the photoelectric conversion module 120, the photoelectric conversion module is composed of SiPM module 121 and PMT module 122, the high-energy gamma photon signal is converted into an analog electrical signal through the scintillation crystal module 110 and the photoelectric conversion module 120, then the high-speed reading electronics module 130 carries out digital sampling on the analog electrical signal, the case pulse analog signal is sampled at equal intervals, and then the case pulse is compared with the quantization level.
The sampled data is input into the coincidence processing module 200 to screen out a pulse instance signal pair satisfying a time window and an energy window, and the coincidence processing module 200 includes a stacking and screening module 210, a stacking and restoring module 220, and a data rearranging module 230. In the accumulation screening module 210, to screen out an accumulation case pulse that needs to be restored, two voltage-time pairs corresponding to the lowest quantization level value are extracted first, if the number of sampling points is greater than two, we define the first and last voltage-time pair sampling points as information data of the pulse, and then calculate the time difference between the two sampling points, if the time difference is greater than the time difference of a single pulse, it is determined as an accumulation event, otherwise, it is a single pulse signal. And restoring the accumulation event through an accumulation restoring module, and restoring the example pulse signal with accumulation into a plurality of single example pulse signals. And fitting the stacking case pulses according to a semi-Gaussian-exponential model, restoring the first stacking case in the stacking case, subtracting the restored first stacking case pulse from the stacking case pulse, and so on until all the case pulses are restored. We will then get the List sample data List-mod in the PET system, which cannot be used directly for later data reconstruction and needs to be reordered. The projection data of the system can be obtained by rearranging the data through the data rearranging module 230. Considering the two-dimensional fault condition, the LORs connecting all the detector crystals of the annular PET detection system can be regarded as a cluster of parallel projections with uniform angular distribution. As shown in FIG. 3, the solid line may be defined as a parallel projection at an angle 1 and the dashed line may be defined as a parallel projection at an angle 2. Based on this, the PET detection system projection data can be stored in sinograms at 180 °/N angular intervals, with N being the number of detection crystals over the entire ring. In each set of parallel projections, the sampling density at the edges is higher than that in the middle. For ROI reconstruction, only LORs within a distance R from the center are taken, in which case the difference in spacing between adjacent parallel projections is almost negligible.
The image reconstruction module 300 reconstructs the image data by using the upper projection data, which includes a fast analysis reconstruction module 310 and a Poisson model iteration reconstruction module 320. PET reconstruction methods can be roughly classified into analytical methods represented by FBP and iterative methods represented by ML-EM. The analytical method has the advantages of reconstruction speed, which can be applied to large-scale projects such as human body three-dimensional whole body reconstruction, and the iterative method has the advantages of introducing prior information and a noise model and having better reconstruction effect under low photon number. The image reconstruction module comprises the following specific operation steps: firstly, the fast analytic method reconstruction module 310 performs a global image reconstruction as a reconstruction starting image of a subsequent iterative method, and then the Poisson model iterative reconstruction module performs reconstruction by using the starting image to obtain a final reconstructed image.
The high-precision stepper motor module 400 performs axial movement on the detected object after the data acquisition of one section is completed by the above modules, and finally obtains the beyond-detection-range reconstructed image of each section through the registration and fusion of the images by the imaging module 500.
The first embodiment is as follows:
the parameters of the present embodiment are given here:
the time window is set to 2ns and the energy window is 350 KeV-650 KeV.
The sampling rate of the equidistant sampling is 500 MHZ.
The time of each step of the high-precision stepping motor is 10min, and the stepping distance is 1 mm.
Example two:
the parameters of the present embodiment are given here:
the time window is set to 5ns and the energy window is 350 KeV-650 KeV.
The sampling rate of the equidistant sampling is 400 MHZ.
The time of each step of the high-precision stepping motor is 20min, and the stepping distance is 2 mm.
Claims (10)
1. A PET imaging system based on linear trajectory projection data, the system comprising:
the detection module comprises a scintillation crystal module, a photoelectric conversion module and a high-speed reading electronic module;
the scintillation crystal module is connected with the photoelectric conversion module and receives a high-energy gamma photon signal emitted from a detected object and converts the high-energy gamma photon signal into a visible light signal;
the photoelectric conversion module is used for converting visible light signals into electric signals;
the high-speed reading electronic module converts the electric signal into a flicker pulse signal;
the coincidence processing module screens coincidence events from the pulse signals by utilizing coincidence screening and obtains projection data of the system;
the image reconstruction module reconstructs an ROI area image by utilizing the projection data;
the high-precision motor stepping module moves the detected object in the axial direction to meet the requirement of over-range detection;
and the developing module is used for splicing all axial imaging images to obtain a complete reconstructed image.
2. The linear trajectory projection data-based PET imaging system of claim 1, wherein the detection module is comprised of a LYSO crystal module and a BGO crystal module.
3. The linear trajectory projection data based PET imaging system of claim 1, wherein said photoelectric conversion module is a combination of a PMT module and an array of SiPM modules.
4. The linear trajectory projection data-based PET imaging system of claim 1, wherein said high-speed readout electronics module employs amplitude sampling, as opposed to conventional time-based sampling, to improve the sampling efficiency of the leading edge of the scintillation pulse.
5. The linear trajectory projection data based PET imaging system of claim 1, wherein said coincidence processing module utilizes an ML-EM algorithm, formulated as:
the pile-up events are processed to separate the unidentifiable pulse signals into individual impulse function forms.
6. The linear trajectory projection data based PET imaging system of claim 1, wherein said coincidence processing module is configured to reorder the detected list data such that the LORs connecting all detector crystals of the annular PET detection system can be viewed as a cluster of angularly uniformly distributed parallel projections which can be rearranged into sinogram format by angle and distance from a central point.
7. The PET imaging system based on linear trajectory projection data according to claim 1, wherein the image reconstruction module adopts a method that combines the advantages of fast reconstruction by an analytic method and an iterative method, such as introduction of a noise model.
8. The linear trajectory projection data based PET imaging system of claim 1, wherein said high precision motor stepper module has a maximum axial resolution S per pass step.
9. The linear trajectory projection data-based PET imaging system of claim 1, wherein the visualization module is operative to perform image registration and fusion.
10. A PET imaging method based on linear trajectory projection data, the method comprising the steps of:
step S1: the detection module converts high-energy gamma photons into visible light;
step S2: the photoelectric conversion module is used for converting the visible light signal into an electric signal;
step S3: high-speed reading electronic module converts electric signal into flash pulse signal
Step S4: the coincidence processing module is used for performing coincidence discrimination on the scintillation pulse signals and obtaining projection data through data rearrangement;
step S5: obtaining a reconstructed image by utilizing a reconstruction algorithm based on a straight-line track according to the projection data;
step S6: the high-precision motor stepping module moves the detected object in the axial direction and repeats the steps S1-S4;
step S7: and the imaging module splices all the axial images to obtain a complete reconstructed image.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010983394.2A CN112274169A (en) | 2020-09-18 | 2020-09-18 | PET imaging system and method based on linear track projection data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010983394.2A CN112274169A (en) | 2020-09-18 | 2020-09-18 | PET imaging system and method based on linear track projection data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112274169A true CN112274169A (en) | 2021-01-29 |
Family
ID=74419656
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010983394.2A Pending CN112274169A (en) | 2020-09-18 | 2020-09-18 | PET imaging system and method based on linear track projection data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112274169A (en) |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1971620A (en) * | 2005-11-21 | 2007-05-30 | 清华大学 | Image rebuilding system and method using straight track scanning |
| CN102753993A (en) * | 2011-01-20 | 2012-10-24 | 株式会社东芝 | Positron emission computed tomography device, method executed by positron emission computed tomography device, and program |
| CN104458771A (en) * | 2013-09-23 | 2015-03-25 | 同方威视技术股份有限公司 | Linear track tomography device and perspective imaging device |
| CN104541183A (en) * | 2012-08-10 | 2015-04-22 | 皇家飞利浦有限公司 | Virtual frames for distributed list-mode time-of-flight reconstruction with continuous bed movement |
| CN104939858A (en) * | 2015-04-29 | 2015-09-30 | 北京航空航天大学 | X-ray and fluorescence combined multi-model tomography system and X-ray and fluorescence combined multi-model tomography method |
| CN105389788A (en) * | 2015-10-13 | 2016-03-09 | 沈阳东软医疗系统有限公司 | Positron emission tomography (PET) multi-bed image reconstruction method, PET multi-bed image reconstruction device, PET multi-bed image combining method and PET multi-bed image combining device |
| CN106097409A (en) * | 2016-06-15 | 2016-11-09 | 沈阳东软医疗系统有限公司 | A kind of image rebuilding method, device and equipment |
| CN106228584A (en) * | 2016-07-20 | 2016-12-14 | 中国人民解放军信息工程大学 | Cone-Beam CT circle adds straight path backprojection-filtration method for reconstructing |
| 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 |
| CN110772274A (en) * | 2019-10-29 | 2020-02-11 | 南昌大学 | Multi-pixel gamma imaging system and method |
-
2020
- 2020-09-18 CN CN202010983394.2A patent/CN112274169A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1971620A (en) * | 2005-11-21 | 2007-05-30 | 清华大学 | Image rebuilding system and method using straight track scanning |
| CN102753993A (en) * | 2011-01-20 | 2012-10-24 | 株式会社东芝 | Positron emission computed tomography device, method executed by positron emission computed tomography device, and program |
| CN104541183A (en) * | 2012-08-10 | 2015-04-22 | 皇家飞利浦有限公司 | Virtual frames for distributed list-mode time-of-flight reconstruction with continuous bed movement |
| CN104458771A (en) * | 2013-09-23 | 2015-03-25 | 同方威视技术股份有限公司 | Linear track tomography device and perspective imaging device |
| CN104939858A (en) * | 2015-04-29 | 2015-09-30 | 北京航空航天大学 | X-ray and fluorescence combined multi-model tomography system and X-ray and fluorescence combined multi-model tomography method |
| CN105389788A (en) * | 2015-10-13 | 2016-03-09 | 沈阳东软医疗系统有限公司 | Positron emission tomography (PET) multi-bed image reconstruction method, PET multi-bed image reconstruction device, PET multi-bed image combining method and PET multi-bed image combining device |
| CN106097409A (en) * | 2016-06-15 | 2016-11-09 | 沈阳东软医疗系统有限公司 | A kind of image rebuilding method, device and equipment |
| CN106228584A (en) * | 2016-07-20 | 2016-12-14 | 中国人民解放军信息工程大学 | Cone-Beam CT circle adds straight path backprojection-filtration method for reconstructing |
| 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 |
| CN110772274A (en) * | 2019-10-29 | 2020-02-11 | 南昌大学 | Multi-pixel gamma imaging system and method |
Non-Patent Citations (1)
| Title |
|---|
| 张必翔: "《专业学位研究生实验课程 医科篇》", 30 November 2017, 武汉：华中科技大学出版社 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Vaquero et al. | Positron emission tomography: current challenges and opportunities for technological advances in clinical and preclinical imaging systems | |
| CN106491153B (en) | A kind of PET scatter correction methods, PET imaging methods and PET imaging systems | |
| US6297506B1 (en) | System and method for reducing pile-up errors in multi-crystal gamma ray detector applications | |
| Tong et al. | Image reconstruction for PET/CT scanners: past achievements and future challenges | |
| Cabello et al. | Advances in PET/MR instrumentation and image reconstruction | |
| JP5864658B2 (en) | Inverse data reconstruction for optimal time sampling of counts in physiological list mode nuclear medicine imaging | |
| CN106716179A (en) | Time of flight calibration in digital positron emission tomography | |
| US9437016B2 (en) | Image domain pansharpening method and system for spectral CT with large pixel energy discriminating detectors | |
| CN109683188A (en) | A kind of Qi Liankefu event and gamma events meet imaging device and method | |
| US20160216385A1 (en) | A tof-pet tomograph and a method of imaging using a tof-pet tomograph, based on a probability of producyion and lifetime of a positronium | |
| CN1416781A (en) | Fast iterative reconstruction method of high-resolution heat source image in positive-electron tomographic scanning | |
| Wu et al. | PET performance evaluation of an MR-compatible PET insert | |
| Cooper et al. | SmartPET: Applying HPGe and pulse shape analysis to small-animal PET | |
| WO2015028602A1 (en) | A method for calibration of tof-pet detectors using cosmic radiation | |
| Kang et al. | Initial results of a mouse brain PET insert with a staggered 3-layer DOI detector | |
| Vaska et al. | A prototype CZT-based PET scanner for high resolution mouse brain imaging | |
| EP1631844B8 (en) | Generating detector efficiency estimates for a pet scanner | |
| CN112274169A (en) | PET imaging system and method based on linear track projection data | |
| Yamada et al. | Development of a small animal PET scanner using DOI detectors | |
| Visvikis et al. | PET technology: current trends and future developments | |
| Kao et al. | A direct sinogram-restoration method for fast image reconstruction in compact DOI-PET systems | |
| CN210697662U (en) | Cherotkoff event and gamma event coincidence imaging device | |
| CN211834429U (en) | Full-simulation nucleolin distribution imaging system | |
| Shokouhi et al. | System performance simulations of the RatCAP awake rat brain scanner | |
| CN203673073U (en) | Radioactive photoelectric detector |
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 | ||
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20210129 |