Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Maximum posteriori optimizing image rebuilding method in PET imaging

A technique of maximum a posteriori image reconstruction, applied in the field of medical image processing

Inactive Publication Date: 2008-04-09
陈武凡
View PDF0 Cites 21 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in fact, the region-based Gibbs prior method has certain limitations in practical applications due to the limitation of complex region identification calculations or the required anatomical prior information.
However, the boundary-based Gibbs prior method strongly relies on the parametric design of the level set, and this process may produce unpredictable results

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Maximum posteriori optimizing image rebuilding method in PET imaging
  • Maximum posteriori optimizing image rebuilding method in PET imaging
  • Maximum posteriori optimizing image rebuilding method in PET imaging

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0032] The present invention implements four steps (see Image 6 ),details as follows:

[0033] 1. Use the PET imaging system to collect the detector data before imaging. The specific collection method can be flexibly set by the user. The data acquisition method in the experiment is designed as follows: 128 radial samples and 128 angular samples are taken within an angle interval of 180°; the system matrix A corresponds to the parallel beam ribbon integral geometry model. Store sampled data in an array.

[0034] 2. Data correction. Calibration coefficients for scan time, detector efficiency, attenuation coefficients and dead time correction coefficients obtained by the system c i and all detected random and scatter counts r i . According to parameter c i and r i Correction of detector data is performed to obtain data for generalized Gibbs prior maximum a posteriori image reconstruction.

[0035] 3. Build an image reconstruction model. Using the maximum a posteriori m...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a maximal posteriori optimized image reconstruction method for leading in a general Gibbs experiment in the PET imaging. The method comprises the following procedures: (1) PET imaging equipment is utilized to collect detector data before imaging, and the corrective parameter value and the system matrix of various data in the imaging equipment are obtained simultaneously; (2) a mathematical statistic statistical model used for reconstructing an image is reconstructed according to a statistical feature that is met by the corrective data acquainted by the procedure (1) before imaging; (3) the general Gibbs experiment is led in aiming at the compute of a mathematical model in the procedure (2) , a maximal posteriori estimate method is adopted to perform the conversion of a reconstruction model, to obtain an optimized equation with a constrained objective function used for obtaining a PET reconstruction image; (4) a parabola is adopted to replace a coordinate descent algorithm, to perform the iterative computation treatment and to reconstruct the image based on the selection of a global parameter in the optimized equation through a result obtained by the procedure (3). The invention can greatly improve the quality of the PET reconstruction image.

Description

technical field [0001] The invention relates to a medical image imaging processing method, in particular to a maximum a posteriori optimization image reconstruction method which introduces generalized Gibbs prior in PET imaging. Background technique [0002] Positron emission tomography (PET) is one of the important inspection methods of medical imaging. Due to the influence of low count rate and noise, the reconstruction from detection data to image is a pathological problem in most cases. According to literature reports, most existing PET imaging algorithms still cannot obtain satisfactory images. Therefore, there is a huge clinical need to effectively improve the quality of PET imaging, and it has always been a research hotspot in medical PET imaging and is also one of the technical problems. [0003] As an effective method to solve ill-conditioned problems in image reconstruction, the maximum a posteriori method has been widely accepted. Based on the Gibbs random field...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): A61B6/00G06T1/00
Inventor 马建华陈阳陈武凡冯衍秋冯前进
Owner 陈武凡
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com