Yu He1, Fangfang Tang1, Jin Jin1,2, and Feng Liu1
1School of Information Technology and Electrical Engineering, the University of Queensland, Brisbane, Australia, 2Research and Development MR, Siemens Healthcare, Brisbane, Australia
1School of Information Technology and Electrical Engineering, the University of Queensland, Brisbane, Australia, 2Research and Development MR, Siemens Healthcare, Brisbane, Australia
Deep
learning has been a hot topic in MRI reconstruction, such as super-resolution; however,
DL usually requires a substantial amount of training data. In this work, we
propose a data augmentation method to increase training data for MRI super-resolution.
Figure
3. Results of the reconstructed MR images by different algorithms on an image
example from the IXI dataset. The reference (a) is the full view of the T2
brain slice. The zoom-in area (denoted by red rectangular) of the image based
on different methods is displayed on the right. (b) denotes the ground truth,
(d) denotes the image upsampled by bicubic interpolation. (c), (e) denotes the
recovered images using EDSR without and with applying the OF method,
respectively. As can be seen, the dura matter from the ground truth was
partly recovered by the proposed method (denoted by red arrows).
Figure 1. An illustration of arriving at
synthesis image (d) by applying the estimated optical flow field (c) on the
source image (a) (denoted by red lines). The estimated optical flow field (c)
is achieved by applying the OF method on source image (a) and target image (b)
(denoted by green lines). The colors in (c) denote the field color-coding,
where smaller vectors are lighter, and color represents the direction.
