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
1Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, 2MR Clinical Science, Philips Healthcare, Suzhou, China
We propose an improved deep-learning based 3D super resolution
network to increase resolution for DWI images. With the help of anatomical
images and a novel FA loss function, the proposed method improves the
reconstruction accuracy.
Fig. 5. Colored FA maps of different methods. The proposed method can recover fine fiber structures. With the introduction of FA loss function, the contrast contamination among diffusion directions can be reduced.
Fig. 3. Selected comparison results and zoomed-in images of in-vivo DWI data. b0 and mean DWI results from 2 representative slices are shown. In the zoomed-in images, the arrows point to the structures that SRCNN and SRResNet fail to recover.
