Zifei Liang1 and Jiangyang Zhang1
1Center for Biomedical Imaging, Dept. of Radiology, New York University School of Medicine, NEW YORK, NY, United States
1Center for Biomedical Imaging, Dept. of Radiology, New York University School of Medicine, NEW YORK, NY, United States
We applied the hybrid-contrast images training to achieve the diffusion weighted images super-resolution. However, verified that distinct resolution or contrast sacrifice some efficacy of the super-resolution algorithm.
Figure 4. Super-resolution of human brain diffusion weighted images. From
top to the bottom row: A. initial Low resolution data; B. cubic interpolation;
C. Zero-filling interpolation; D. ResNet output by zero-filling as pre-step
interpolation; E. High resolution Reference).
Figure 1. The architecture of ResNet architecture (input patch size
could be three dimensional 21x21x21 (or m×n×k) or two dimensional m×n, and
convolution kernel should be corresponded as three dimensional and two
dimensional).