Mayo Foundation for Medical Education and Research

Arvin Forghanian-Arani

Richard L Ehman

Joshua D Trzasko

John Huston, Iii

Philip A Araoz

Armando Manduca

Matthew A Bernstein

Yunhong Shu

Kevin J Glaser

Matthew C Murphy

Yi Sui

Jonathan M Scott

John Huston Iii

Described here are systems and methods for a robust magnetic resonance elastography ('MRE') imaging platform for rapid dynamic 3D MRE imaging. The imaging platform includes an MRE pulse sequence and advanced image reconstruction framework that work synergistically in order to greatly expand the domains where MRE can be deployed successfully.

Continuous three-dimensional imaging for magnetic resonance elastography

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.

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

Described here are systems and methods for correcting motion-encoding gradient nonlinearities in magnetic resonance elastography ('MRE'). In general, the systems and methods described in the present disclosure compute gradient nonlinearity corrected displacement data based on information about the motion-encoding gradients used when acquiring magnetic resonance data.

Methods for correcting motion-encoding gradient nonlinearities in magnetic resonance elastography

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Rochester, MN, United States of America

Arvin Forghanian-Arani