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Abstract #1332

Improved Super-Resolution reconstruction for DWI using multi-contrast information

Xinyu Ye1, Pylypenko Dmytro1, Yuan Lian1, Yajing Zhang2, and Hua Guo1
1Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, 2MR Clinical Science, Philips Healthcare, Suzhou, China

In clinical scans, the acquired DWI images usually has limited resolution. Super resolution method has the potential to improve the image resolution without adding scan time. Here we propose a deep-learning based multi-contrast super resolution network with gradient-map guidance and a novel FA loss function to reconstruct high-resolution DWI images from low-resolution DWI images and high–resolution anatomical images. In-vivo DWI data are used to test the proposed method. The results show that the image quality can be improved.

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