Quantitative molecular imaging using deep magnetic resonance fingerprinting - Vladimirov, Nikita; Cohen, Ouri; Heo, Hye Young; Zaiß, Moritz; Farrar, Christian T.; Perlman, Or (Nature Protocols, 2025)
Deep learning-based saturation transfer magnetic resonance fingerprinting (MRF) is an emerging approach for noninvasive in vivo imaging of proteins, metabolites and pH. It involves a series of steps, including sample/participant preparation, image acquisition schedule design, biophysical model formulation and artificial intelligence and computational model training, followed by image acquisition, deep reconstruction and analysis. Saturation transfer-based molecular MRI has been slow to reach clinical maturity and adoption for clinical practice due to its technical complexity, semi-quantitative contrast-weighted nature and long scan times needed for the extraction of quantitative molecular biomarkers. Deep MRF provides solutions to these challenges by providing a quantitative and rapid framework for extracting biologically and clinically meaningful molecular information. Here we define a complete protocol for quantitative molecular MRI using deep MRF. We describe in vitro sample preparation and animal and human scan considerations, and provide intuition behind the acquisition protocol design and optimization of chemical exchange saturation transfer (CEST) and semi-solid magnetization transfer (MT) quantitative imaging. We then extensively describe the building blocks for several artificial intelligence models and demonstrate their performance for different applications, including cancer monitoring, brain myelin imaging and pH quantification. Finally, we provide guidelines to further modify and expand the pipeline for imaging a variety of other pathologies (such as neurodegeneration, stroke and cardiac disease), accompanied by the related open-source code and sample data. The procedure takes between 48 min (for two proton pools or in vitro imaging) and 57 h (for complex multi-proton pool in vivo imaging) to complete and is suitable for graduate student-level users.
CRIS-Team Scopus Importer:2025-04-18