MR Fingerprinting for Imaging Brain Hemodynamics and Oxygenation

TL;DR

This review discusses recent advances in magnetic resonance fingerprinting (MRF) techniques for imaging brain hemodynamics and oxygenation, highlighting improved models, sequences, and reconstruction methods for clinical application.

Contribution

It provides a comprehensive overview of recent developments in vascular MRF, emphasizing new models, sequences, and machine learning-based reconstruction techniques.

Findings

Enhanced accuracy in vascular parameter estimation

Integration of machine learning in reconstruction

Potential for clinical translation

Abstract

Over the past decade, several studies have explored the potential of magnetic resonance fingerprinting (MRF) for the quantification of brain hemodynamics, oxygenation, and perfusion. Recent advances in simulation models and reconstruction frameworks have also significantly enhanced the accuracy of vascular parameter estimation. This review provides an overview of key vascular MRF studies, emphasizing advancements in geometrical models for vascular simulations, novel sequences, and state-of-the-art reconstruction techniques incorporating machine learning and deep learning algorithms. Both pre-clinical and clinical applications are discussed. Based on these findings, we outline future directions and development areas that need to be addressed to facilitate their clinical translation. Evidence Level N/A. Technical Efficacy Stage 1.