  Magnetic resonance imaging (MRI) is increasingly utilized for image-guided
radiotherapy due to its outstanding soft-tissue contrast and lack of ionizing
radiation. However, geometric distortions caused by gradient nonlinearity (GNL)
limit anatomical accuracy, potentially compromising the quality of tumour
treatments. In addition, slow MR acquisition and reconstruction limit the
potential for real-time image guidance. Here, we demonstrate a deep
learning-based method that rapidly reconstructs distortion-corrected images
from raw k-space data for real-time MR-guided radiotherapy applications. We
leverage recent advances in interpretable unrolling networks to develop a
Distortion-Corrected Reconstruction Network (DCReconNet) that applies
convolutional neural networks (CNNs) to learn effective regularizations and
nonuniform fast Fourier transforms for GNL-encoding. DCReconNet was trained on
a public MR brain dataset from eleven healthy volunteers for fully sampled and
accelerated techniques including parallel imaging (PI) and compressed sensing
(CS). The performance of DCReconNet was tested on phantom and volunteer brain
data acquired on a 1.0T MRI-Linac. The DCReconNet, CS- and PI-based
reconstructed image quality was measured by structural similarity (SSIM) and
root-mean-squared error (RMSE) for numerical comparisons. The computation time
for each method was also reported. Phantom and volunteer results demonstrated
that DCReconNet better preserves image structure when compared to CS- and
PI-based reconstruction methods. DCReconNet resulted in highest SSIM (0.95
median value) and lowest RMSE (<0.04) on simulated brain images with four times
acceleration. DCReconNet is over 100-times faster than iterative, regularized
reconstruction methods. DCReconNet provides fast and geometrically accurate
image reconstruction and has potential for real-time MRI-guided radiotherapy
applications.
