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Abstract #3232

Multi-Contrast Generation and On Demand Quantification of Saturation Transfer, Relaxivity, and Field Homogeneity using a Deep MRI on a Chip

Dinor Nagar1, Moritz Zaiss2,3, and Or Perlman4,5
1Electrical Engineering, Tel Aviv University, Tel Aviv, Israel, 2Institute of Neuroradiology, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Erlangen, Germany, 3Dept. of AI in Biomedical Engineering, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Erlangen, Germany, 4Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel, 5Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel

Synopsis

Keywords: Other AI/ML, Machine Learning/Artificial Intelligence, Transformers, Contrast Generation, Quantification, Relaxometry, CEST, MT, Field Mapping

Motivation: Multi-contrast and multi-metabolite CEST, MT, and relaxivity imaging necessitate a separate pulse sequence for each application of interest, rendering it time-consuming and seldom performed in clinical settings.

Goal(s): To develop a computational framework that learns the RF excitation to tissue response manifold transfer, enabling on-demand contrast generation.

Approach: A hybrid CNN transformer was designed to generate a new set of contrast-weighted and quantitative images in response to a user-defined (on-demand) unseen set of acquisition parameters. Validation was performed on four subjects and patients scanned at two sites.

Results: An excellent similarity between the generated images and ground-truth was obtained with 94% acceleration.

Impact: A deep learning framework was designed to provide rich biological information in less than 30 seconds. It can capture the magnetic signal dynamics in humans and decode the tissue response to RF excitation, constituting a deep MRI on a chip.

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Keywords