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:
Jan. 16, 2024

Filed:

Mar. 27, 2020
Applicant:

The General Hospital Corporation, Boston, MA (US);

Inventors:

Qiyuan Tian, Charlestown, MA (US);

Susie Yi Huang, Boston, MA (US);

Berkin Bilgic, Boston, MA (US);

Assignee:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G01V 3/00 (2006.01); G01R 33/563 (2006.01); G01R 33/56 (2006.01); G06N 3/08 (2023.01); G06N 3/045 (2023.01);
U.S. Cl.
CPC ...
G01R 33/56341 (2013.01); G01R 33/5608 (2013.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01);
Abstract

Higher quality diffusion metrics and/or diffusion-weighted images are generated from lower quality input diffusion-weighted images using a suitably trained neural network (or other machine learning algorithm). High-fidelity scalar and orientational diffusion metrics can be extracted using a theoretical minimum of a single non-diffusion-weighted image and six diffusion-weighted images, achieved with data-driven supervised deep learning. As an example, a deep convolutional neural network ('CNN') is used to map the input non-diffusion-weighted image and diffusion-weighted images sampled along six optimized diffusion-encoding directions to the residuals between the input and output high-quality non-diffusion-weighted image and diffusion-weighted images, which enables residual learning to boost the performance of CNN and full tensor fitting to generate any scalar and orientational diffusion metrics.


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