Multi-parameter molecular MRI quantification using physics-informed self-supervised learning

• Published in: Communications physics Vol. 8; no. 1; pp. 164 - 11
• Main Authors: Finkelstein, Alex, Vladimirov, Nikita, Zaiss, Moritz, Perlman, Or
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 15.04.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 631/57/2266; 639/766/930/2735; Brain; Differential equations; Inverse problems; Machine learning; Magnetic resonance imaging; Medical imaging; Neural networks; Ordinary differential equations; Parameter estimation; Physics; Physics and Astronomy; Self-supervised learning; Semisolids; System dynamics

Biophysical model fitting plays a key role in obtaining quantitative parameters from physiological signals and images. However, the model complexity for molecular magnetic resonance imaging (MRI) often translates into excessive computation time, which makes clinical use impractical. Here, we present...