Meeting Banner
Abstract #4336

High efficient Bloch simulation of MRI sequences based on deep learning

Haitao Huang1, Qinqin Yang1, Zhigang Wu2, Jianfeng Bao3, Jingliang Cheng3, Shuhui Cai1, and Congbo Cai1
1Department of Electronic Science, Xiamen University, Xiamen, China, 2MSC Clinical & Technical Solutions, Philips Healthcare, Shenzhen, China, 3Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China


To overcome the difficulty of obtaining a large number of real training samples, the utilization of synthetic training samples based on Bloch simulation has become more and more popular in deep learning based MRI reconstruction. However, a large amount of Bloch simulation is usually very time-consuming even with the help of GPU. In this study, a simulation network that receives sequence parameters and contrast templates, was proposed to simulate MR images from different imaging sequences. The reliability and flexibility of the proposed method were verified by distortion correction for GRE-EPI images and T2 maps obtained with overlapping-echo detachment planar imaging.

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

Join Here