Mayo Foundation for Medical Education and Research

John Huston, Iii

Richard L Ehman

Joshua D Trzasko

Arvin Forghanian-Arani

Armando Manduca

Matthew A Bernstein

Yi Sui

Philip A Araoz

Matthew C Murphy

Jonathan M Scott

Yunhong Shu

Kevin J Glaser

Meng Yin

Ziying Yin

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

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.

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

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

Described here are systems and methods for evaluating the extent of brain-skull tethering, which may also be referred to as loss of trabecular reserve, in subjects using magnetic resonance elastography ('MRE'). The present disclosure describes a method for assessing progressive damage to arachnoid space ('SAS') trabeculae. The method generally includes measuring the relative movement between the brain and the skull using MRE. As one example, an MRE-based method named slip interface imaging (&#x201c;SII&#x201d;) can be implemented. By measuring trabecular reserve in subjects who have a history of prior head trauma, the susceptibility of a given subject to future injury can be assessed.

Methods for quantifying trabecular reserve using magnetic resonance elastography

In an MRI system using high-performance gradient hardware, a method includes de-rating selected lobes in a 2DTOF imaging pulse sequence; employing images from the two-dimensional imagery to detect the presence of disease; and performing three-dimensional contrast-enhanced MRA if disease was detected at a sufficiently high level to make three-dimensional imaging useful. By de-rating selected lobes of the 2DTOF imaging pulse sequence, sensitivity to carotid stenosis at or above the clinically important range of 60-70% is achieved.

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Rochester, MN, United States of America

John Huston, Iii