Distortion Free Image Reconstruction using a Deep Neural Network for an MRI-Linac
Shanshan Shan1,2, Yang Gao3, Paul Liu1,2, Brendan Whelan1, Hongfu Sun3, Feng Liu3, Paul Keall1,2, and David Waddington1,2
1ACRF Image X Institute, University of Sydney, Sydney, Australia, 2Ingham Institute For Applied Medical Research, Sydney, Australia, 3School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia
The advent of MRI-guided radiotherapy has elevated demand for high geometric fidelity imaging. However, gradient nonlinearity can cause image distortion, which limits the accuracy of radiotherapy. In this work, we develop a deep neural network, namely DFReconNet, to reconstruct distortion free images directly from raw k-space in real time. Experiments on simulated brain datasets and phantom images acquired from an MRI-Linac demonstrated the utility of the proposed method.
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