Decoding the human brain tissue response to radiofrequency excitation using a biophysical-model-free deep MRI on a chip framework
<|reference_start|>Decoding the human brain tissue response to radiofrequency excitation using a biophysical-model-free deep MRI on a chip framework: 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.<|reference_end|>
@article{nagar2024decoding,
    title={Decoding the human brain tissue response to radiofrequency excitation
  using a biophysical-model-free deep MRI on a chip framework},
    author={Dinor Nagar (1), Moritz Zaiss (2 and 3), Or Perlman (4 and 5) ((1)
  School of Electrical Engineering, Tel Aviv University, Tel Aviv, Israel, (2)
  Institute of Neuroradiology, Friedrich-Alexander Universitat
  Erlangen-Nurnberg (FAU), University Hospital Erlangen, Erlangen, Germany, (3)
  Department of Artificial Intelligence in Biomedical Engineering,
  Friedrich-Alexander Universitat Erlangen-Nurnberg, Erlangen, Germany, (4)
  Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel,
  (5) Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel)},
    journal={arXiv preprint arXiv:2408.08376},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.08376},
    primaryClass={physics.med-ph cs.AI q-bio.NC}
}