Meeting Banner
Abstract #3381

High efficient reconstruction of overlapping-echo detachment (OLED) planar imaging based on deep residual network

Congbo Cai1, Chao Wang2, Xinghao Ding2, Shuhui Cai2, Zhong Chen2, and Jianhui Zhong3

1Xiamen University, Xiamen, China, 2Xiamen University, xiamen, China, 3University of Rochester, Rochester, NY, United States

Overlapping-echo detachment (OLED) planar imaging sequence can provide reliable T2 mapping within milliseconds even under continuous object motion. A detachment algorithm based on the sparsity and structure similarity constraints has been used to separate the echo signals to form T2 map. However, the effectiveness of separation is limited and the reconstruction is time consuming. Here, an end-to-end deep convolutional network based on deep residual network was introduced. The results of simulation and in vivo human brain show that it can reconstruct T2 mapping efficiently and reduce the reconstruction time from minutes to milliseconds after deep residual network is trained.

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

Join Here