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:
Aug. 04, 2020

Filed:

Aug. 28, 2014
Applicant:

Siemens Healthcare Gmbh, Erlangen, DE;

Inventors:

Dominik Neumann, Erlangen, DE;

Tommaso Mansi, Plainsboro, NJ (US);

Sasa Grbic, Princeton, NJ (US);

Bogdan Georgescu, Plainsboro, NJ (US);

Ali Kamen, Skillman, NJ (US);

Dorin Comaniciu, Princeton Junction, NJ (US);

Ingmar Voigt, Erlangen, DE;

Assignee:

Siemens Healthcare GmbH, Erlangen, DE;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G09B 23/28 (2006.01); G16H 50/50 (2018.01); A61B 5/00 (2006.01); G06N 5/00 (2006.01); A61B 5/04 (2006.01); A61B 5/02 (2006.01); A61B 8/08 (2006.01); A61B 5/055 (2006.01);
U.S. Cl.
CPC ...
G09B 23/288 (2013.01); G16H 50/50 (2018.01); A61B 5/0044 (2013.01); A61B 5/02 (2013.01); A61B 5/04 (2013.01); A61B 5/055 (2013.01); A61B 8/0883 (2013.01); A61B 2576/023 (2013.01); G06N 5/003 (2013.01);
Abstract

A method and system for estimating physiological heart measurements from medical images and clinical data disclosed. A patient-specific anatomical model of the heart is generated from medical image data of the patient. A patient-specific multi-physics computational heart model is generated based on the patient-specific anatomical model by personalizing parameters of a cardiac electrophysiology model, a cardiac biomechanics model, and a cardiac hemodynamics model based on medical image data and clinical measurements of the patient. Cardiac function of the patient is simulated using the patient-specific multi-physics computational heart model. The parameters can be personalized by inverse problem algorithms based on forward model simulations or the parameters can be personalized using a machine-learning based statistical model.


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