ABSTRACT Magnetic resonance fingerprinting (MRF) enables quantitative MRI by allowing the simultaneous mapping of multiple tissue properties through innovative acquisition and computational methods. This review focuses on the application of MRF techniques to cerebral physiology, emphasizing advancements in vascular imaging and the integration of biophysical modeling. We discuss the principles of MRF, its adaptation to quantify hemodynamic and vascular parameters, and its potential to overcome challenges in mapping vascular‐related parameters. The review categorizes MRF‐based imaging approaches, including MRF‐arterial spin labeling (MRF‐ASL), MR vascular fingerprinting (MRvF), and vascular fluid dynamics‐MRF (VFD‐MRF), highlighting their technical implementations, accuracy, and clinical applications in conditions such as stroke, brain tumors, and cerebrovascular diseases. We also explore the role of machine learning in enhancing dictionary matching and reducing computational time for more accurate and reliable real‐time parameter estimation. The challenges such as low signal‐to‐noise ratios and computational demands are addressed through tailored sequence designs, noise‐resilient dictionaries, and deep learning approaches. This comprehensive review provides a detailed technical framework for advancing the role of MRF in assessing cerebral physiology and its clinical translation.
Lu et al. (Sun,) studied this question.