Patent · US Active

Fast diffusion tensor MRI using deep learning

US11874359B2 · kind B2 · utility

0Cited by

2References

23Claims

0Family size

Assignee

The General Hospital Corporation · US

Inventors

Qiyuan Tian · Stanford, US
Susie Yi Huang · Boston, US
Berkin Bilgic · Cambridge, US

Key dates

Filing date	Mar 27, 2020
Grant date	Jan 16, 2024
Priority date	—
Expiry date	Jul 13, 2040

Classification

Technology area (CPC G)Physics
CPC primaryG06N3/09
WIPO fieldComputer technology
WIPO sectorElectrical engineering

Abstract

Higher quality diffusion metrics and/or diffusion-weighted images are generated from lower quality input diffusion-weighted images using a suitably trained neural network (or other machine learning algorithm). High-fidelity scalar and orientational diffusion metrics can be extracted using a theoretical minimum of a single non-diffusion-weighted image and six diffusion-weighted images, achieved with data-driven supervised deep learning. As an example, a deep convolutional neural network (“CNN”) is used to map the input non-diffusion-weighted image and diffusion-weighted images sampled along six optimized diffusion-encoding directions to the residuals between the input and output high-quality non-diffusion-weighted image and diffusion-weighted images, which enables residual learning to boost the performance of CNN and full tensor fitting to generate any scalar and orientational diffusion metrics.

Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.