CN103136731A - Parameter imaging method of dynamic Positron Emission Tomography (PET) images - Google Patents
Parameter imaging method of dynamic Positron Emission Tomography (PET) images Download PDFInfo
- Publication number
- CN103136731A CN103136731A CN2013100451001A CN201310045100A CN103136731A CN 103136731 A CN103136731 A CN 103136731A CN 2013100451001 A CN2013100451001 A CN 2013100451001A CN 201310045100 A CN201310045100 A CN 201310045100A CN 103136731 A CN103136731 A CN 103136731A
- Authority
- CN
- China
- Prior art keywords
- dynamic
- image
- dynamic pet
- parameter
- pet
- 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.)
- Granted
Links
Images
Landscapes
- Nuclear Medicine (AREA)
- Image Processing (AREA)
Abstract
A parameter imaging method of dynamic Positron Emission Tomography (PET) images successively comprises the following steps: (1) acquiring projection data of all time frames of dynamic PET; (2) adopting a PET rebuilding method to rebuild images for the dynamic PET projection data acquired in the step (1) to acquire the dynamic PET images; (3) designing a dynamic PET image filter to carry out a noise reduction treatment on the dynamic PET images acquired in the step (2); the form of the filter is a weight factor; (4) carrying out parameter image on the dynamic PET images acquired in the step (3) after the noise reduction treatment. Due to the fact that the dynamic PET image filter is designed, pattern noise can be effectively eliminated, and the quality of the parameter image can be improved.
Description
Technical field
The present invention relates to a kind of technical field of image processing of medical image, be specifically related to a kind of parameter imaging method of dynamic PET image.
Background technology
Positron emission tomography (Positron Emission Tomography, PET) as the outstanding representative of functional molecular image, is applied to clinical diagnosis, especially early diagnosis just more and more widely.
Dynamic pet imaging carries out video picture by the distribution of the radiotracer injected in human body and activity are changed, can without wound the needed Human Physiology of clinician and Biochemical Information are provided, and then potential disease is carried out early stage diagnosis and treatment.Yet, because the time of dynamically PET scanning is short, photon counting is low, and easily be subject to noise and the impact of other physical factors during the scanning process image data, therefore the dynamic PET signal noise ratio (snr) of image of rebuilding is lower, cause dynamic analysis result and the truth of focal zone to have relatively large deviation, affect the clinician to the diagnostic result of disease.
In order to improve the quality of parametric imaging, filtering technique is usually used in the noise reduction process of dynamic PET image, as gaussian filtering technology, bilateral filtering technology etc.Therefore, present parametric imaging be generally first to the dynamic PET sequence image rebuild one by one time frame carry out image filtering, then carry out kinetic parameter by the dynamic sequence image applications kinetic model after noise reduction and estimate.
But the PET image that existing filtering technique is based on single time frame more carries out noise reduction process, does not consider the correlativity on dynamic PET temporal sequence of images.Because the signal to noise ratio (S/N ratio) of dynamic PET image is lower, has now and can not remove preferably picture noise based on the filtering technique of single frames, thereby make by the parametric image of dynamic PET Image estimation of low quality.
Therefore, not enough for prior art, provide a kind of parameter imaging method of high-quality dynamic PET image very necessary to solve the prior art deficiency.
Summary of the invention
The invention provides a kind of parameter imaging method of dynamic PET image, the method can effectively be removed picture noise, and the parametric image quality of acquisition is high.
Above-mentioned purpose of the present invention realizes by following technological means.
A kind of parameter imaging method of dynamic PET image in turn includes the following steps:
(1) obtain the data for projection of dynamic all time frames of PET;
(2) adopt the PET method for reconstructing to carry out image reconstruction to the dynamic PET data for projection that obtains in step (1), obtain dynamic PET image;
(3) the dynamic PET image filter of design, carry out noise reduction process to the dynamic PET image that obtains in step (2);
(4) the dynamic PET image after step (3) noise reduction process is carried out parametric imaging.
The form of the wave filter of above-mentioned steps (3) design is:
, wherein,
Be dynamic PET image,
Be the index value of pixel in single-frame images,
Be the number of pixels of single-frame images,
Be the index value of image time frame,
Be the dynamic total time frame number of PET image,
Be pixel in single-frame images
Neighborhood on every side;
Be weight factor.
Above-mentioned weight factor
,
It is pixel in dynamic PET image
And pixel
The time activity curve (Time-Activity Curve, TAC) that the place is corresponding, namely
, parameter
With
Controlling respectively the level and smooth degree of image space adjacency and TAC similarity,
For Gauss's Weighted distance is estimated.
PET method for reconstructing in above-mentioned steps (2) is set to filtered back-projection method or iterative reconstruction approach.
Parametric imaging in above-mentioned steps (4) is for to carry out imaging by dynamic PET parameter imaging method.
Above-mentioned dynamic PET parameter imaging method is based on the kinetic parameter method of estimation of compartment model or based on the kinetic parameter method of estimation of Patlak linear model or based on the kinetic parameter method of estimation of Logan linear model.
The invention provides a kind of parameter imaging method of dynamic PET image, in turn include the following steps: (1) obtains the data for projection of dynamic all time frames of PET; (2) adopt the PET method for reconstructing to carry out image reconstruction to the dynamic PET data for projection that obtains in step (1), obtain dynamic PET image; (3) the dynamic PET image filter of design, carry out noise reduction process to the dynamic PET image that obtains in step (2); (4) the dynamic PET image after step (3) noise reduction process is carried out parametric imaging.The parameter imaging method of dynamic PET image of the present invention has been owing to having designed dynamic PET image filter, effectively the removal of images noise, improve the quality of parametric image.
Description of drawings
The present invention is further illustrated to utilize accompanying drawing, but the content in accompanying drawing does not consist of any limitation of the invention.
Fig. 1 is the schematic flow sheet of the inventive method.
Fig. 2 is the human body brain Voxel Phantom image schematic diagram that adopts in the embodiment of the present invention 2.
Fig. 3 is time activity curve corresponding to regional in the human body brain Voxel Phantom of this Fig. 2.
Fig. 4 is the dynamic PET parametric image that method of the present invention obtains.
Fig. 5 is the dynamic PET parametric image that employing does not have the direct parameter formation method of noise reduction process to obtain.
Fig. 6 is the dynamic PET parametric image that adopts the parameter imaging method after gaussian filtering is processed to obtain.
Fig. 7 is the dynamic PET parametric image that adopts the parameter imaging method after bilateral filtering is processed to obtain.
Embodiment
The invention will be further described with the following Examples.
Embodiment 1.
A kind of parameter imaging method of dynamic PET image in turn includes the following steps:
(1) obtain the data for projection of dynamic all time frames of PET.The data for projection method of obtaining dynamic all time frames of PET is general knowledge known in this field, does not repeat them here.
(2) adopt the PET method for reconstructing to carry out image reconstruction to the dynamic PET data for projection that obtains in step (1), obtain dynamic PET image.The PET method for reconstructing can be set to filtered back-projection method or iterative reconstruction approach.Certainly, the PET method for reconstructing also can be set to additive method as the case may be, and is not limited to the situation in the present embodiment.
(3) the dynamic PET image filter of design, carry out noise reduction process to the dynamic PET image that obtains in step (2).
The form of the wave filter of step (3) design is specially:
, wherein,
Be dynamic PET image,
Be the index value of pixel in single-frame images,
Be the number of pixels of single-frame images,
Be the index value of image time frame,
Be the dynamic total time frame number of PET image,
Be pixel in single-frame images
Neighborhood on every side;
Be weight factor.
Weight factor
,
It is pixel in dynamic PET image
And pixel
The time activity curve (Time-Activity Curve, TAC) that the place is corresponding, namely
, parameter
With
Controlling respectively the level and smooth degree of image space adjacency and TAC similarity,
For Gauss's Weighted distance is estimated.
Enter at last the dynamic PET image of step (4) after to step (3) noise reduction process and carry out parametric imaging.
Wherein parametric imaging is for to carry out imaging by dynamic PET parameter imaging method.Dynamically the PET parameter imaging method is based on the kinetic parameter method of estimation of compartment model or based on the kinetic parameter method of estimation of Patlak linear model or based on the kinetic parameter method of estimation of Logan linear model.
The parameter imaging method of dynamic PET image of the present invention is for the characteristics of dynamic PET data, utilize the temporal information of dynamic PET image, designed dynamic PET image filter, effectively the removal of images noise, thereby improve the quality of parametric image, better aid in clinical diagnosis.
Embodiment 2.
Describe the specific implementation process of the inventive method in detail with the Voxel Phantom data instance of Computer Simulation.
Referring to Fig. 1, the specific implementation process of example of the present invention is as described below:
Adopt human body head Voxel Phantom image shown in Figure 2 as computer simulation experiment object of the present invention.The Voxel Phantom image is made of cerebral gray matter, cerebral white matter and brain fritter tumour three parts, the image pixel matrix size is 64 * 64, the compartment model of dynamics simulation simulation two tissues, adopt the Feng model (referring to paper: D.G. Feng, et al, IEEE Trans. Inf. Technol. Biomed., 1 (4): 243-254,1997) the blood input function of match, tracer agent are that (chemical name is FDG to fluorodeoxyglucose, is called for short
18F-FDG), the time activity curve (Time-Activity Curves, TACs) of each corresponding tissue as shown in Figure 3, dynamically the acquisition time of PET data is set to 4 * 20s, 4 * 40s, 4 * 60s, 4 * 180s and 14 * 300s, totally 30 time frames.
Step (1), the TACs of each tissue of generating is incorporated in Voxel Phantom, generate real dynamically PET image, then obtain corresponding dynamic projection data by system's probability matrix front projection, then by adding poisson noise and adjusting the dynamic PET data for projection that the photon tale generates simulation.
Step (2), the dynamic PET data for projection that simulation in step (1) is generated adopts traditional filtered back-projection method to carry out image reconstruction, obtains dynamic PET image.
Step (3), the dynamic PET image to rebuilding in step (2) uses dynamic PET filtering method shown in the present to carry out noise reduction process.In example of the present invention,
Be 5 * 5 neighborhood window, parameter
With
Value is 2 and 40 respectively, the weight during Gauss's Weighted distance is estimated
Be taken as the duration of each of section sweep time.
Step (4) is carried out parameter estimation to the dynamic PET image applications after noise reduction process in step (3) based on the kinetic parameter method of estimation of compartment model, obtains the rate of influx parametric image, as shown in Figure 4.
The parameter imaging method of dynamic PET image of the present invention is for the characteristics of dynamic PET data, utilize the temporal information of dynamic PET image, designed dynamic PET image filter, effectively the removal of images noise, thereby improve the quality of parametric image, better aid in clinical diagnosis.
In order to verify the effect of method shown in the present, the dynamic PET image that step (2) is rebuild does not have respectively the parametric imaging after direct parameter imaging, the gaussian filtering of noise reduction are processed, the parametric imaging after the processing of traditional bilateral filtering, and parametric image is respectively as Fig. 5, Fig. 6, shown in Figure 7.Image and Fig. 5 to Fig. 7 of Fig. 4 are compared, can find out, method shown in the present can effectively be removed picture noise, improves the parametric image quality.The signal to noise ratio (S/N ratio) of calculating chart 4-Fig. 7 correspondence image, result is respectively: 13.12dB, 9.72dB, 9.88dB and 10.61dB, can see that method shown in the present can improve the signal to noise ratio (S/N ratio) of parametric image significantly.
In sum, the parameter imaging method of dynamic PET image of the present invention has been owing to having designed dynamic PET image filter, effectively the removal of images noise, improve the quality of parametric image.
Should be noted that at last; above embodiment is only in order to illustrate technical scheme of the present invention but not limiting the scope of the invention; although with reference to preferred embodiment, the present invention has been done detailed description; those of ordinary skill in the art is to be understood that; can modify or be equal to replacement technical scheme of the present invention, and not break away from essence and the scope of technical solution of the present invention.
Claims (6)
1. the parameter imaging method of a dynamic PET image is characterized in that: in turn include the following steps:
(1) obtain the data for projection of dynamic all time frames of PET;
(2) adopt the PET method for reconstructing to carry out image reconstruction to the dynamic PET data for projection that obtains in step (1), obtain dynamic PET image;
(3) the dynamic PET image filter of design, carry out noise reduction process to the dynamic PET image that obtains in step (2);
(4) the dynamic PET image after step (3) noise reduction process is carried out parametric imaging.
2. the parameter imaging method of dynamic PET image according to claim 1 is characterized in that:
The form of the wave filter of described step (3) design is:
, wherein,
Be dynamic PET image,
Be the index value of pixel in single-frame images,
Be the number of pixels of single-frame images,
Be the index value of image time frame,
Be the dynamic total time frame number of PET image,
Be pixel in single-frame images
Neighborhood on every side;
Be weight factor.
3. the parameter imaging method of dynamic PET image according to claim 2 is characterized in that:
Described weight factor
,
It is pixel in dynamic PET image
And pixel
The time activity curve that the place is corresponding, namely
, parameter
With
Controlling respectively the level and smooth degree of image space adjacency and time activity curve similarity,
For Gauss's Weighted distance is estimated.
4. the parameter imaging method of the described dynamic PET image of according to claim 1 to 3 any one, it is characterized in that: the PET method for reconstructing in described step (2) is set to filtered back-projection method or iterative reconstruction approach.
5. the parameter imaging method of the described dynamic PET image of according to claim 1 to 3 any one, it is characterized in that: the parametric imaging in described step (4) is for to carry out imaging by dynamic PET parameter imaging method.
6. the parameter imaging method of dynamic PET image according to claim 5 is characterized in that:
Described dynamic PET parameter imaging method is based on the kinetic parameter method of estimation of compartment model or based on the kinetic parameter method of estimation of Patlak linear model or based on the kinetic parameter method of estimation of Logan linear model.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310045100.1A CN103136731B (en) | 2013-02-05 | 2013-02-05 | A kind of parameter imaging method of dynamic PET images |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310045100.1A CN103136731B (en) | 2013-02-05 | 2013-02-05 | A kind of parameter imaging method of dynamic PET images |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103136731A true CN103136731A (en) | 2013-06-05 |
| CN103136731B CN103136731B (en) | 2015-11-25 |
Family
ID=48496527
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201310045100.1A Active CN103136731B (en) | 2013-02-05 | 2013-02-05 | A kind of parameter imaging method of dynamic PET images |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103136731B (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103955899A (en) * | 2014-05-02 | 2014-07-30 | 南方医科大学 | Dynamic PET image denoising method based on combined image guiding |
| CN106510744A (en) * | 2016-04-27 | 2017-03-22 | 上海联影医疗科技有限公司 | Estimation method for dynamic parameters of multiple tracer agents in PET scanning |
| CN108765318A (en) * | 2018-05-15 | 2018-11-06 | 南方医科大学 | A kind of dynamic PET images factor treatment based on dynamics cluster |
| CN109712209A (en) * | 2018-12-14 | 2019-05-03 | 深圳先进技术研究院 | The method for reconstructing of PET image, computer storage medium, computer equipment |
| WO2020118617A1 (en) * | 2018-12-13 | 2020-06-18 | 深圳先进技术研究院 | Large-view magnetic resonance scanning image reconstruction method and device based on deep learning |
| CN113052933A (en) * | 2021-03-15 | 2021-06-29 | 深圳高性能医疗器械国家研究院有限公司 | Parameter imaging method and system |
| WO2022133639A1 (en) * | 2020-12-21 | 2022-06-30 | 深圳先进技术研究院 | Medical image processing method and apparatus, and device and storage medium |
| WO2022193072A1 (en) * | 2021-03-15 | 2022-09-22 | 深圳高性能医疗器械国家研究院有限公司 | Parameter imaging method and system |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6493416B1 (en) * | 2001-11-21 | 2002-12-10 | Ge Medical Systems Global Technology Company, Llc | Method and apparatus for noise reduction in computed tomographic systems |
| CN1641700A (en) * | 2005-01-06 | 2005-07-20 | 东南大学 | Positive electron emitted computerised tomography full-variation weighted image method |
| CN102013108A (en) * | 2010-11-23 | 2011-04-13 | 南方医科大学 | Regional spatial-temporal prior-based dynamic PET reconstruction method |
-
2013
- 2013-02-05 CN CN201310045100.1A patent/CN103136731B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6493416B1 (en) * | 2001-11-21 | 2002-12-10 | Ge Medical Systems Global Technology Company, Llc | Method and apparatus for noise reduction in computed tomographic systems |
| CN1641700A (en) * | 2005-01-06 | 2005-07-20 | 东南大学 | Positive electron emitted computerised tomography full-variation weighted image method |
| CN102013108A (en) * | 2010-11-23 | 2011-04-13 | 南方医科大学 | Regional spatial-temporal prior-based dynamic PET reconstruction method |
Non-Patent Citations (2)
| Title |
|---|
| THOMAS E. NICHOLS ET AL.: "Spatiotemporal Reconstruction of List-Mode PET Data", 《IEEE TRANSACTIONS ON MEDICAL IMAGING》 * |
| 边兆英: "基于区域时空先验的动态PE丁重建及PE丁图像恢复算法研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103955899A (en) * | 2014-05-02 | 2014-07-30 | 南方医科大学 | Dynamic PET image denoising method based on combined image guiding |
| CN106510744A (en) * | 2016-04-27 | 2017-03-22 | 上海联影医疗科技有限公司 | Estimation method for dynamic parameters of multiple tracer agents in PET scanning |
| CN108765318A (en) * | 2018-05-15 | 2018-11-06 | 南方医科大学 | A kind of dynamic PET images factor treatment based on dynamics cluster |
| WO2020118617A1 (en) * | 2018-12-13 | 2020-06-18 | 深圳先进技术研究院 | Large-view magnetic resonance scanning image reconstruction method and device based on deep learning |
| CN109712209A (en) * | 2018-12-14 | 2019-05-03 | 深圳先进技术研究院 | The method for reconstructing of PET image, computer storage medium, computer equipment |
| CN109712209B (en) * | 2018-12-14 | 2022-09-20 | 深圳先进技术研究院 | PET image reconstruction method, computer storage medium, and computer device |
| WO2022133639A1 (en) * | 2020-12-21 | 2022-06-30 | 深圳先进技术研究院 | Medical image processing method and apparatus, and device and storage medium |
| CN113052933A (en) * | 2021-03-15 | 2021-06-29 | 深圳高性能医疗器械国家研究院有限公司 | Parameter imaging method and system |
| WO2022193072A1 (en) * | 2021-03-15 | 2022-09-22 | 深圳高性能医疗器械国家研究院有限公司 | Parameter imaging method and system |
| CN113052933B (en) * | 2021-03-15 | 2024-06-28 | 深圳高性能医疗器械国家研究院有限公司 | Parameter imaging method and system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103136731B (en) | 2015-11-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103136731B (en) | A kind of parameter imaging method of dynamic PET images | |
| CN109035169B (en) | Unsupervised/semi-supervised CT image reconstruction depth network training method | |
| WO2021232653A1 (en) | Pet image reconstruction algorithm combining filtered back-projection algorithm and neural network | |
| CN106683144B (en) | Image iterative reconstruction method and device | |
| US8897527B2 (en) | Motion-blurred imaging enhancement method and system | |
| CN103279929B (en) | A kind of prediction of the CT image metallic traces based on integral cosine and artifact minimizing technology | |
| Wolf et al. | Abnormal resting-state connectivity of motor and cognitive networks in early manifest Huntington's disease | |
| CN102136138B (en) | Processing method for denoising and enhancing medical image | |
| CA2609781A1 (en) | Method for segmenting a sequence of three-dimensional images, in particular in pharmaco-imaging | |
| US20040165756A1 (en) | Visualization of medical images | |
| Xue et al. | LCPR-Net: low-count PET image reconstruction using the domain transform and cycle-consistent generative adversarial networks | |
| CN103955899A (en) | Dynamic PET image denoising method based on combined image guiding | |
| He et al. | Dynamic PET image denoising with deep learning-based joint filtering | |
| CN103971349B (en) | Computed tomography images method for reconstructing and ct apparatus | |
| CN113052933B (en) | Parameter imaging method and system | |
| Pustelnik et al. | Parallel algorithm and hybrid regularization for dynamic PET reconstruction | |
| CN102013108A (en) | Regional spatial-temporal prior-based dynamic PET reconstruction method | |
| Durrer et al. | Diffusion models for contrast harmonization of magnetic resonance images | |
| CN114219820B (en) | Neural network generation method, denoising method and device thereof | |
| CN105488824B (en) | A kind of method and apparatus for rebuilding PET image | |
| Tsartsalis et al. | Dynamic image denoising for voxel-wise quantification with statistical parametric mapping in molecular neuroimaging | |
| Hachama et al. | A classifying registration technique for the estimation of enhancement curves of DCE-CT scan sequences | |
| Rodríguez-Gallo et al. | Inpainting-filtering for metal artifact reduction (IMIF-MAR) in computed tomography | |
| CN105279777B (en) | Based on the static PET image reconstruction method for improving Sequential filter | |
| Fang et al. | Tissue-specific sparse deconvolution for low-dose CT perfusion |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant |