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:
Jun. 02, 2020

Filed:

Sep. 14, 2017
Applicant:

University of Louisville Research Foundation, Inc., Louisville, KY (US);

Inventors:

Ayman S. El-Baz, Louisville, KY (US);

Ahmed Soliman, Louisville, KY (US);

Fahmi Khalifa, Louisville, KY (US);

Ahmed Shaffie, Louisville, KY (US);

Neal Dunlap, Louisville, KY (US);

Brian Wang, Louisville, KY (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
A61B 6/00 (2006.01); G06T 7/33 (2017.01); G06T 7/149 (2017.01); G06T 7/143 (2017.01); G06T 7/174 (2017.01); G06T 7/38 (2017.01); G06T 7/246 (2017.01); A61B 6/03 (2006.01); G06T 7/00 (2017.01); A61N 5/10 (2006.01); A61B 5/08 (2006.01); A61B 5/00 (2006.01);
U.S. Cl.
CPC ...
A61B 6/5217 (2013.01); A61B 6/032 (2013.01); A61B 6/486 (2013.01); A61B 6/50 (2013.01); A61B 6/5235 (2013.01); G06T 7/0016 (2013.01); G06T 7/143 (2017.01); G06T 7/149 (2017.01); G06T 7/174 (2017.01); G06T 7/246 (2017.01); G06T 7/344 (2017.01); G06T 7/38 (2017.01); A61B 5/08 (2013.01); A61B 5/7267 (2013.01); A61B 2576/00 (2013.01); A61N 5/1048 (2013.01); G06T 2200/04 (2013.01); G06T 2207/10076 (2013.01); G06T 2207/10081 (2013.01); G06T 2207/20076 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30061 (2013.01);
Abstract

A system and computation method is disclosed that identifies radiation-induced lung injury after radiation therapy using 4D computed tomography (CT) scans. After deformable image registration, the method segments lung fields, extracts functional and textural features, and classifies lung tissues. The deformable registration locally aligns consecutive phases of the respiratory cycle using gradient descent minimization of the conventional dissimilarity metric. Then an adaptive shape prior, a first-order intensity model, and a second-order lung tissues homogeneity descriptor are integrated to segment the lung fields. In addition to common lung functionality features, such as ventilation and elasticity, specific regional textural features are estimated by modeling the segmented images as samples of a novel 7-order contrast-offset-invariant Markov-Gibbs random field (MGRF). Finally, a tissue classifier is applied to distinguish between the injured and normal lung tissues.


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