Methods for estimating mechanical properties from magnetic resonance elastography data using artificial neural networks

US11543481B2 · kind B2 · utility

Assignee

• Mayo Foundation for Medical Education and Research · US

Inventors

• Matthew C. Murphy · Rochester, US
• Richard L. Ehman · Rochester, US
• Kevin J. Glaser · Rochester, US
• Joshua D. Trzasko · Rochester, US
• Armando Manduca · Rochester, US
• John Huston, III · Rochester, US
• Jonathan M. Scott · Spanish Fork, US
• Arvin Forghanian-Arani · Rochester, US

Key dates

• Filing date: Nov 19, 2018
• Grant date: Jan 3, 2023
• Expiry date: Sep 14, 2039

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N3/09
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

Magnetic resonance elastography (“MRE”), or other imaging-based elastography techniques, generate estimates of the mechanical properties, such as stiffness and damping ratio, of tissues in a subject. A machine learning approach, such as an artificial neural network, is implemented to perform an inversion of displacement data in order to generate the estimates of the mechanical properties.