Systems, methods, and media for estimating a mechanical property based on a transformation of magnetic resonance elastography data using a trained artificial neural network

US12019133B2 · kind B2 · utility

Assignee

• Mayo Foundation for Medical Education and Research · US

Inventors

• Matthew C. Murphy · Rochester, US
• Joshua D. Trzasko · Rochester, US
• Richard L. Ehman · Rochester, US
• John Huston, III · Rochester, US
• Armando Manduca · Rochester, US
• Jonathan M. Scott · Spanish Fork, US

Key dates

• Filing date: Jan 28, 2021
• Grant date: Jun 25, 2024
• Expiry date: Apr 12, 2041

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N3/0464
• WIPO field: Measurement
• WIPO sector: Instruments

Abstract

In accordance with some embodiments, systems, methods, and media for estimating a mechanical property based on a transformation of magnetic resonance elastography (MRE) data using a trained artificial neural network are provided. In some embodiments, a system is provided, the system comprising: a hardware processor programmed to: receive displacement data of tissue in vivo; provide the displacement data to a trained ANN that was trained using noisy input datasets as training data, and derivative datasets corresponding to the noisy input datasets to evaluate performance during training, such that the trained ANN provides an output dataset corresponding to an analytical solution to a derivative of a function represented in an unlabeled input dataset thereby transforming the unlabeled input dataset into its derivative; receive, from the trained ANN, an output dataset indicative of a derivative of the displacement data; and estimate stiffness of the tissue based on the derivative.