Acceleration of Diffusion-Relaxation Multidimensional MRI acquisition exploiting Locally low-rank with Block Adaptive Regularization

Motivation: Multidimensional (MD)-MRI provides valuable sub-voxel information. However, it suffers from prohibitively long acquisition time making it impractical for routine clinical use. Goal(s): To reduce MD-MRI scan time via partial k-space sampling in conjunction with a novel reconstruction framework. Approach: Achieve data reduction by using random incoherent sampling, followed by locally low-rank reconstruction with block adaptive regularization, and comparison with ground-truth. Results: In-vivo performance of MD-MRI image reconstruction method that achieves R=4 reduction factor was demonstrated. This framework provides whole-brain coverage with 2mm$$$^3$$$ voxels in 20 minutes, while maintaining robustness and accuracy. This innovation has significant potential for clinical neurological applications. Impact: Multidimensional MRI is crucial for investigating tissue microstructure, brain connectivity, and pathology in clinical study. Here we present a novel image reconstruction framework that allows R=4 k-space data reduction factor, providing whole-brain coverage with 2mm$$$^3$$$ voxels MD-MRI data in 20minutes.