Sub-Second GRASP-LLR DCE: Locally Low-Rank Subspace Constraint aided by Deep Learning

Motivation: We hope to advance the assessment of breast dynamic contrast-enhanced MRI (DCE-MRI) by enhancing image quality, temporal resolution, and temporal fidelity. Goal(s): Propose a new radial GRASP reconstruction pipeline for DCE-MRI, which enables reliable spatially localized dynamics at a sub-second temporal resolution. Approach: Presenting globally and locally low-rank reconstruction approaches for GRASP DCE-MRI aided by Residual Network (ResNet) architecture. Results: Our results suggest that GRASP-LLR offers not only enhanced tumor lesion delineation with reduced background noise but also good separation between healthy, benign, and malignant cases. Impact: We propose a new radial reconstruction pipeline for DCE-MRI which leverages a locally low-rank (LLR) subspace model in combination with deep learning approach, resulting in reliable spatially localized dynamics at a sub-second temporal resolution.