Rita Kharboush1, Anita Karsa1, Barbara Dymerska1, and Karin Shmueli1
1Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
1Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
This novel, exact
model of phase unwrapping can be used to train any neural network. Networks
trained using masked (and unmasked) images showed unwrapping performance
similar to state-of-the-art SEGUE phase unwrapping on test brain images and
showed some generalisation to pelvic images.
Training strategy. An
axial slice of a representative image is shown unmasked (b.1) and masked (b.2).
In all cases the label (or ground truth image) was the Laplacian PCG unwrapped
phase, and the input was the rewrapped phase of the label such that the label
was the exact unwrapping solution of the label. Differences can be seen between
the raw and the rewrapped phase images.
The
CNN unwrapping performance on raw phase images. A coronal, axial and sagittal
slice of the raw phase (a) and phase unwrapping solutions using the masked CNN
(b), Iterative Laplacian PCG (c) and SEGUE (d) for a representative healthy volunteer
in vivo. The computation times are shown below. Red arrows indicate where the Laplacian
or SEGUE solutions appear more accurate than the CNN. The yellow arrows
indicate where the CNN was more accurate.