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

US10463336B2 · kind B2 · utility

Assignee

• Siemens Healthcare Limited · DE

Inventors

• Lucian Mihai Itu · Brașov, RO
• Puneet Sharma · Rahway, US
• Saikiran Rapaka · Ewing, US
• Tiziano Passerini · Plainsboro, US
• Max Schöbinger · Hirschaid, DE
• Chris Schwemmer · Forchheim, DE
• Dorin Comaniciu · Princeton Junction, US
• Thomas Redel · Baiersdorf, DE

Key dates

• Filing date: Nov 16, 2015
• Grant date: Nov 5, 2019
• Expiry date: Jan 17, 2036

Classification

• Technology area (CPC G): Physics
• CPC primary: G16H30/20
• WIPO field: Medical technology
• WIPO sector: Instruments

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.