  MRI-Linac systems require fast image reconstruction with high geometric
fidelity to localize and track tumours for radiotherapy treatments. However, B0
field inhomogeneity distortions and slow MR acquisition potentially limit the
quality of the image guidance and tumour treatments. In this study, we develop
an interpretable unrolled network, referred to as RebinNet, to reconstruct
distortion-free images from B0 inhomogeneity-corrupted k-space for fast
MRI-guided radiotherapy applications. RebinNet includes convolutional neural
network (CNN) blocks to perform image regularizations and nonuniform fast
Fourier Transform (NUFFT) modules to incorporate B0 inhomogeneity information.
The RebinNet was trained on a publicly available MR dataset from eleven healthy
volunteers for both fully sampled and subsampled acquisitions. Grid phantom and
human brain images acquired from an open-bore 1T MRI-Linac scanner were used to
evaluate the performance of the proposed network. The RebinNet was compared
with the conventional regularization algorithm and our recently developed
UnUNet method in terms of root mean squared error (RMSE), structural similarity
(SSIM), residual distortions, and computation time. Imaging results
demonstrated that the RebinNet reconstructed images with lowest RMSE (<0.05)
and highest SSIM (>0.92) at four-time acceleration for simulated brain images.
The RebinNet could better preserve structural details and substantially improve
the computational efficiency (ten-fold faster) compared to the conventional
regularization methods, and had better generalization ability than the UnUNet
method. The proposed RebinNet can achieve rapid image reconstruction and
overcome the B0 inhomogeneity distortions simultaneously, which would
facilitate accurate and fast image guidance in radiotherapy treatments.
