Abstract #0277

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

Chenwei Tang¹, Leonardo A Rivera-Rivera^1,2, Laura B Eisenmenger³, and Kevin M Johnson^1,3

¹Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, United States, ²Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States, ³Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States

Synopsis

Keywords: White Matter, Brain

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.

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