Abstract #1796

Deeping-Learning based acceleration for Serial MR imaging using Transformer (SMR-Transformer): Comparison with CNN on Three Datasets

Chuyu Liu¹, Shuo Chen¹, Yibing Chen², Rui Li¹, and Xiaolei Song¹

¹Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China, ²Xi’an Key Lab of Radiomics and Intelligent Perception, School of Information Sciences and Technology, Northwest University, Xi'an, China

Synopsis

Serial image acquisition is always required for quantitative and dynamic MRI, in which the spatial and temporal resolution as well as the contrast is restricted by acquisition speed. Herein we employed a novel transformer based deep-learning method for reconstruction of highly under-sampled MR serial images. The network architecture and input were designed to take advantages of Transformer’s capacity on global information learning along the redundant series dimension. SMR-transformer was compared with 3D Res-U-Net, on a self-acquired CEST dataset, a cardiac dataset from OCMR and a diffusion dataset from HCP. Both images and quantitative comparison indicated SMR-Transformer allowed fast serial MRI.

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