Multi-contrast generation and quantitative MRI using a transformer-based framework with RF excitation embeddings.
Authors
Affiliations (6)
Affiliations (6)
- School of Electrical Engineering, Tel Aviv University, Tel Aviv, Israel.
- School of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel.
- Institute of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany.
- Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany.
- School of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel. [email protected].
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel. [email protected].
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 explicitly utilizes RF excitation information alongside per-subject calibration data (acquired within 28.2 s), to generate a wide variety of image contrasts including fully quantitative molecular, water relaxation, and magnetic field maps. 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 transformer-based MRI framework (TBMF) may support the efforts to reveal the molecular composition of the human brain tissue in a wide range of pathologies, while offering clinically attractive scan times.