GRASP MRI: A Decade of Innovation from Bench to Bedside

Abstract

GRASP (Golden-angle RAdial Sparse Parallel) MRI has emerged as one of the most influential motion-robust dynamic MRI frameworks over the past decade. By combining continuous golden-angle radial sampling with compressed sensing and parallel imaging, GRASP enables free-breathing data acquisition with flexible retrospective image reconstruction. Since its original introduction, the framework has evolved substantially and has inspired a broad range of technical developments, including motion-resolved reconstruction, real-time imaging, quantitative MRI, deep learning-enabled reconstruction, and multidimensional cardiovascular imaging. These advances have further expanded the role of GRASP MRI in a range of clinical applications where conventional breath-hold imaging is challenging. This review summarizes the technical evolution and clinical translation of GRASP MRI over the past decade, with a particular focus on the conceptual advantages of continuous radial acquisition, flexible retrospective reconstruction, and motion-robust imaging. Emerging developments in deep learning reconstruction, real-time volumetric imaging, and quantitative free-breathing MRI are also discussed together with future directions of motion-robust MRI acquisition and reconstruction.