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.

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.

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.

Abstract: Described here are systems and methods for magnetic resonance elastography (“MRE”), or other imaging-based elastography techniques, in which a machine learning approach, such as an artificial neural network, is implemented to perform an inversion of displacement data in order to generate estimates of the mechanical properties, such as stiffness and damping ratio, of tissues in a subject.

Abstract: A system and method for generating a magnetic resonance elastography (MRE) report includes a) acquiring MRE data from a subject including positively motion encoded medical imaging data and negatively motion encoded medical imaging data and b) deriving uncorrected difference medical imaging data from the MRE data for a given slice. The method also includes c) filtering the uncorrected difference medical imaging data to create filtered medical imaging data corrected for errors associated with phase ramps occurring during gradient switching used to derive the positively motion encoded medical imaging data and negatively motion encoded medical imaging data, d) generating a corrected difference image for the given slice from the filtered medical imaging data and the uncorrected difference medical imaging data, e) repeating steps b) through d) for each slice reflected in the MRE data, and f) generating a report of elastic properties of the subject from the corrected difference image.

Abstract: A system and method for generating a magnetic resonance elastography (MRE) report includes a) acquiring MRE data from a subject including positively motion encoded medical imaging data and negatively motion encoded medical imaging data and b) deriving uncorrected difference medical imaging data from the MRE data for a given slice. The method also includes c) filtering the uncorrected difference medical imaging data to create filtered medical imaging data corrected for errors associated with phase ramps occurring during gradient switching used to derive the positively motion encoded medical imaging data and negatively motion encoded medical imaging data, d) generating a corrected difference image for the given slice from the filtered medical imaging data and the uncorrected difference medical imaging data, e) repeating steps b) through d) for each slice reflected in the MRE data, and f) generating a report of elastic properties of the subject from the corrected difference image.

Abstract: A system and method for analyzing a subject using a magnetic resonance imaging (MRI) system is provided. The technique includes positioning a subject within the MRI system and coupling a driver to the subject to impart vibrational energy to the subject. The technique further includes using the MRI system and in coordination with operation of the driver, acquiring medical imaging data from the subject's brain and deriving stiffness information of the subject's brain from the medical imaging data. A report, such as an image, can be provided indicating the stiffness information of the subject's brain relative to baseline stiffness information to indicate a status of the subject with respect to a neurodegenerative disease.