Decoding the human brain tissue response to radiofrequency excitation using a biophysical-model-free deep MRI on a chip framework

• URL: http://arxiv.org/abs/2408.08376v2
• Date: Mon, 19 Aug 2024 05:15:04 GMT
• Title: Decoding the human brain tissue response to radiofrequency excitation using a biophysical-model-free deep MRI on a chip framework
• Authors: Dinor Nagar, Moritz Zaiss, Or Perlman,
• Abstract summary: We developed a vision-based framework that captures the RF magnetic signal evolution and decodes rapid brain tissue response to excitation. The deep MRI on a chip (DeepMonC) framework may reveal the molecular composition of the human brain tissue in a wide range of pathologies.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Magnetic resonance imaging (MRI) relies on radiofrequency (RF) excitation of proton spin. Clinical diagnosis requires a comprehensive collation of biophysical data via multiple MRI contrasts, acquired using a series of RF sequences that lead to lengthy examinations. Here, we developed a vision transformer-based framework that captures the spatiotemporal magnetic signal evolution and decodes the brain tissue response to RF excitation, constituting an MRI on a chip. Following a per-subject rapid calibration scan (28.2 s), a wide variety of image contrasts including fully quantitative molecular, water relaxation, and magnetic field maps can be generated automatically. The method was validated across healthy subjects and a cancer patient in two different imaging sites, and proved to be 94% faster than alternative protocols. The deep MRI on a chip (DeepMonC) framework may reveal the molecular composition of the human brain tissue in a wide range of pathologies, while offering clinically attractive scan times.

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