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. 09, 2024

Filed:

Feb. 05, 2020
Applicant:

University of Virginia Patent Foundation, Charlottesville, VA (US);

Inventors:

Craig H. Meyer, Charlottesville, VA (US);

Xue Feng, Charlottesville, VA (US);

Michael Salerno, Charlottesville, VA (US);

Assignee:

UNIVERSITY OF VIRGINIA PATENT FOUNDATION, Charlottesville, VA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
A61B 5/00 (2006.01); A61B 5/026 (2006.01); A61B 5/055 (2006.01); G01R 33/483 (2006.01); G01R 33/563 (2006.01); G06N 3/08 (2023.01); G06T 3/00 (2006.01); G06T 3/60 (2006.01); G06T 7/00 (2017.01); G06T 7/11 (2017.01); G06T 7/143 (2017.01); G06T 7/149 (2017.01); G06T 7/194 (2017.01); G06T 7/215 (2017.01); G06T 7/33 (2017.01); G16H 30/40 (2018.01);
U.S. Cl.
CPC ...
A61B 5/0044 (2013.01); A61B 5/0263 (2013.01); A61B 5/055 (2013.01); A61B 5/7267 (2013.01); G01R 33/4835 (2013.01); G01R 33/56366 (2013.01); G06N 3/08 (2013.01); G06T 3/0006 (2013.01); G06T 3/60 (2013.01); G06T 7/0014 (2013.01); G06T 7/11 (2017.01); G06T 7/143 (2017.01); G06T 7/149 (2017.01); G06T 7/194 (2017.01); G06T 7/215 (2017.01); G06T 7/33 (2017.01); G16H 30/40 (2018.01); A61B 2576/023 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30048 (2013.01); G06T 2207/30104 (2013.01); G06T 2207/30168 (2013.01);
Abstract

A computerized system and method of modeling myocardial tissue perfusion can include acquiring a plurality of original frames of magnetic resonance imaging (MRI) data representing images of a heart of a subject and developing a manually segmented set of ground truth frames from the original frames. Applying training augmentation techniques to a training set of the originals frame of MRI data can prepare the data for training at least one convolutional neural network (CNN). The CNN can segment the training set of frames according to the ground truth frames. Applying the respective input test frames to a trained CNN can allow for segmenting an endocardium layer and an epicardium layer within the respective images of the input test frames. The segmented images can be used in calculating myocardial blood flow into the myocardium from segmented images of the input test frames.


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