Fast 3D Neuro T2-FLAIR with Learned Sampling and fully 3D Model Based Deep learning

Motivation: T2-FLAIR is an essential contrast for clinical neuroimaging. However, the inherently long scan time limits its application in screening. Goal(s): We aim to accelerate 3D T2-FLAIR scan while maintaining sufficient image quality. Approach: We developed a framework that simultaneously learns a sampling pattern and a fully 3D deep learning reconstruction neural network. This allows exploiting the optimization space in both sampling and reconstruction. Results: Learned sampling pattern with MoDL reconstruction trained with added Gaussian noise was able to provide high quality T2-FLAIR scan with 1x1x1.6mm resolution in 1 min 39s. Impact: This work confirms the feasibility of a short 3D T2-FLAIR scan, provides insights for optimization strategies, and could lead to clinical implementation.