The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Apr. 14, 2020

Filed:

Jun. 09, 2017
Applicant:

Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences, Shenzhen, Guangdong, CN;

Inventors:

Xi Peng, Guangdong, CN;

Dong Liang, Guangdong, CN;

Xin Liu, Guangdong, CN;

Hairong Zheng, Guangdong, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G01R 33/563 (2006.01); A61B 5/055 (2006.01); G01R 33/56 (2006.01); G06T 5/00 (2006.01); A61B 5/00 (2006.01);
U.S. Cl.
CPC ...
G01R 33/56341 (2013.01); A61B 5/055 (2013.01); G01R 33/5602 (2013.01); G06T 5/002 (2013.01); A61B 5/7203 (2013.01); G06T 2207/10092 (2013.01);
Abstract

The application provides a method, apparatus and computer program product for denoising a magnetic resonance diffusion tensor, wherein the method comprises: collecting data of K space; calculating a maximum likelihood estimator of a diffusion tensor according to the collected data of K space; calculating a maximum posterior probability estimator of the diffusion tensor by using sparsity of the diffusion tensor and sparsity of a diffusion parameter and taking the calculating maximum likelihood estimator as an initial value; and calculating the diffusion parameter according to the calculated maximum posterior probability estimator. The application solves the technical problem in the prior art of how to realize high precision denoising of diffusion tensor while not increasing scanning time and affecting spatial resolution, achieves the technical effects of effectively suppressing noises in the diffusion tensor and improving the estimation accuracy of the diffusion tensor.


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