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:
Feb. 28, 2023

Filed:

Jul. 26, 2018
Applicant:

Siemens Healthcare Gmbh, Erlangen, DE;

Inventors:

Tiziano Passerini, Plainsboro, NJ (US);

Thomas Redel, Poxdorf, DE;

Paul Klein, Princeton, NJ (US);

Lucian Mihai Itu, Brasov, RO;

Saikiran Rapaka, Pennington, NJ (US);

Puneet Sharma, Princeton Junction, NJ (US);

Assignee:

Siemens Healthcare GmbH, Erlangen, DE;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
A61B 5/00 (2006.01); A61B 34/10 (2016.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 50/70 (2018.01); G16H 20/40 (2018.01); G16H 50/50 (2018.01); G16H 50/30 (2018.01); G06N 3/08 (2023.01); G06N 3/04 (2023.01); G06T 7/00 (2017.01); A61B 5/026 (2006.01);
U.S. Cl.
CPC ...
A61B 34/10 (2016.02); A61B 5/026 (2013.01); A61B 5/4887 (2013.01); A61B 5/7267 (2013.01); A61B 5/7278 (2013.01); G06N 3/0445 (2013.01); G06N 3/0454 (2013.01); G06N 3/08 (2013.01); G06T 7/0012 (2013.01); G16H 20/40 (2018.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01); G16H 50/50 (2018.01); G16H 50/70 (2018.01); A61B 2034/104 (2016.02); A61B 2034/105 (2016.02); A61B 2034/107 (2016.02); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30048 (2013.01); G06T 2207/30096 (2013.01); G06T 2207/30101 (2013.01);
Abstract

A method and system for non-invasive assessment and therapy planning for coronary artery disease from medical image data of a patient is disclosed. Geometric features representing at least a portion of a coronary artery tree of the patient are extracted from medical image data. Lesions are detected in coronary artery tree of the patient and a hemodynamic quantity of interest is computed at a plurality of points along the coronary artery tree including multiple points within the lesions based on the extracted geometric features using a machine learning model, resulting in an estimated pullback curve for the hemodynamic quantity of interest. Post-treatment values for the hemodynamic quantity of interest are predicted at the plurality of points along the coronary artery tree including the multiple points within the lesions for each of one or more candidate treatment options for the patient, resulting in a respective predicted post-treatment pullback curve for the hemodynamic quantity of interest for each of the one or more candidate treatment options. A visualization of a treatment prediction for at least one of the candidate treatment options is displayed.


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