Meeting Banner
Abstract #1221

Deep Learning of ADC Maps from Under-sampled Diffusion-Weighted Radially Sampled MRI

Yuemeng Li1, Hee Kwon Song1, Miguel Romanello Giroud Joaquim1, Stephen Pickup1, Rong Zhou1, and Yong Fan1
1Radiology, University of Pennsylvania, Philadelphia, PA, United States

Respiratory motion and high magnetic fields pose challenges for quantitative diffusion weighted MRI (DWI) of mouse abdomen on preclinical MRI systems. EPI-based DWI method yields inadequate suppression of motion and magnetic susceptibility artifacts. Diffusion-weighted radial spin-echo (Rad-SE-DW) produces artifact-free images but require substantially longer acquisition times. Here, we demonstrate a new deep learning concept for accelerating acquisition of RAD-SE-DW. Fully sampled Rad-SE-DW images are used to train a convolution neural network for directly extracting apparent diffusion coefficient (ADC) maps from highly under-sampled Rad-SE-DW data. Comparisons with standard ADC extraction and acceleration methods are made to support this concept.

This abstract and the presentation materials are available to members only; a login is required.

Join Here