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:
Nov. 05, 2019

Filed:

Nov. 16, 2015
Applicant:

Siemens Healthcare Gmbh, Erlangen, DE;

Inventors:

Lucian Mihai Itu, Brasov, RO;

Puneet Sharma, Monmouth Junction, NJ (US);

Saikiran Rapaka, Pennington, NJ (US);

Tiziano Passerini, Plansboro, NJ (US);

Max Schöbinger, Hirschaid, DE;

Chris Schwemmer, Forchheim, DE;

Dorin Comaniciu, Princeton Junction, NJ (US);

Thomas Redel, Poxdorf, DE;

Assignee:

Siemens Healthcare GmbH, Erlangen, DE;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
A61B 6/00 (2006.01); G06T 7/00 (2017.01); G06K 9/52 (2006.01); G06K 9/62 (2006.01); A61B 5/00 (2006.01); A61B 5/026 (2006.01); A61B 8/06 (2006.01); A61B 8/08 (2006.01); G06T 7/11 (2017.01); A61B 6/03 (2006.01); G16H 50/50 (2018.01); G16H 50/20 (2018.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 20/00 (2018.01); A61B 5/02 (2006.01); A61B 8/00 (2006.01); G06F 19/00 (2018.01);
U.S. Cl.
CPC ...
A61B 6/5217 (2013.01); A61B 5/026 (2013.01); A61B 5/7267 (2013.01); A61B 6/032 (2013.01); A61B 6/504 (2013.01); A61B 6/507 (2013.01); A61B 8/06 (2013.01); A61B 8/065 (2013.01); A61B 8/5223 (2013.01); G06F 19/00 (2013.01); G06K 9/52 (2013.01); G06K 9/6201 (2013.01); G06K 9/627 (2013.01); G06K 9/6262 (2013.01); G06T 7/0012 (2013.01); G06T 7/11 (2017.01); G16H 20/00 (2018.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 50/50 (2018.01); H05K 999/99 (2013.01); A61B 5/02007 (2013.01); A61B 5/02028 (2013.01); A61B 5/0263 (2013.01); A61B 5/743 (2013.01); A61B 6/469 (2013.01); A61B 8/469 (2013.01); A61B 2576/00 (2013.01); G06F 19/321 (2013.01); G06T 2200/04 (2013.01); G06T 2207/10072 (2013.01); G06T 2207/10076 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30101 (2013.01); G06T 2207/30104 (2013.01);
Abstract

A method and system for determining hemodynamic indices, such as fractional flow reserve (FFR), for a location of interest in a coronary artery of a patient is disclosed. Medical image data of a patient is received. Patient-specific coronary arterial tree geometry of the patient is extracted from the medical image data. Geometric features are extracted from the patient-specific coronary arterial tree geometry of the patient. A hemodynamic index, such as FFR, is computed for a location of interest in the patient-specific coronary arterial tree based on the extracted geometric features using a trained machine-learning based surrogate model. The machine-learning based surrogate model is trained based on geometric features extracted from synthetically generated coronary arterial tree geometries.


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