Method and system for purely geometric machine learning based fractional flow reserve

A technology of machine learning and geometric features, applied in the direction of instruments, applications, instruments, etc. for radiological diagnosis, can solve problems such as high computational cost and complexity

Active Publication Date: 2017-12-01
SIEMENS HEALTHCARE GMBH
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  • Abstract
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Problems solved by technology

However, a disadvantage of such mechanistic models is the high computational cost and complexity associated with model preparation and numerical solution of the physics-based equations

Method used

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  • Method and system for purely geometric machine learning based fractional flow reserve
  • Method and system for purely geometric machine learning based fractional flow reserve
  • Method and system for purely geometric machine learning based fractional flow reserve

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Embodiment Construction

[0019] The present invention relates to methods and systems for machine learning based evaluation of hemodynamic indicators for coronary artery stenosis, such as Fractional Flow Reserve (FFR). Embodiments of the invention are described herein to give an intuitive understanding of the method for assessing coronary artery stenosis. Digital images typically include digital representations of one or more objects (or shapes). Digital representations of objects are generally described herein in terms of identifying and manipulating objects. Such manipulations are virtual manipulations done in the computer system's memory or other circuitry / hardware. Accordingly, it is to be understood that embodiments of the invention can be implemented within a computer system using data stored within the computer system.

[0020] Embodiments of the present invention utilize a data-driven, statistical approach to compute one or more hemodynamic indices based purely on geometric features extracted...

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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 are 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.

Description

[0001] This application claims the benefit of US Provisional Application No. 62 / 079,641, filed November 14, 2014, and US Provisional Application No. 62 / 083,373, filed November 24, 2014, the disclosures of which are incorporated herein by reference in their entirety. This application also claims priority to US Application No. 14 / 804,609, filed July 21, 2015, and US Application No. 14 / 876,852, filed October 7, 2015, the disclosures of which are incorporated herein by reference in their entirety. technical field [0002] The present invention relates to non-invasive functional assessment of coronary artery stenosis, and more particularly to machine learning-based non-invasive functional assessment of coronary artery stenosis from medical image data. Background technique [0003] Cardiovascular disease (CVD) is the leading cause of death worldwide. Among the various CVDs, coronary artery disease (CAD) accounts for nearly fifty percent of those deaths. Despite significant improv...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B6/00G06T7/00
CPCA61B6/504A61B6/5217G06T7/0012A61B6/032G06T2207/10072G06T2207/30101G06T2207/30104G06T2207/20081G06V40/14G06V2201/03G16H50/50
Inventor L.M.伊图T.帕塞里尼S.拉帕卡C.施韦默M.舍宾格P.沙尔马T.雷德尔D.科马尼丘
Owner SIEMENS HEALTHCARE GMBH
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