Abstract #1981

Kernel-based Fast EPTI Reconstruction with Neural Network

Muheng Li¹, Jie Xiang², Fuyixue Wang^3,4, Zijing Dong^3,5, and Kui Ying²

¹Department of Automation, Tsinghua University, Beijing, China, ²Department of Engineering Phycics, Tsinghua University, Beijing, China, ³A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, ⁴Harvard-MIT Health Sciences and Technology, MIT, Cambridge, MA, United States, ⁵Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, United States

A machine learning based reconstruction framework for Echo Planar Time-resolved Imaging(EPTI) is proposed. This work utilized the special data acquisition trajectory of EPTI, a highly-accelerated spatiotemporal CAIPI sampling, to divide the k-space recovery task into a multi-process program. The missing data is filled within an indicated small kernel with a fully connected neural network. Through image reconstruction tests on human brain data set acquired by EPTI, we demonstrated the high efficiency of this algorithm by shortening the reconstruction time of 216×216×48×32 k-data from over 10 minutes to about 20 seconds.

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

Join Here