Semi-supervised super-resolution of diffusion-weighted images based on multiple references

Spatial resolution of diffusion tensor images is usually compromised to accelerate the acquisitions, and the state-of-the-art (SOTA) image super-resolution (SR) reconstruction methods are commonly based on supervised learning models. Considering that matched low-resolution (LR) and high-resolution (HR) diffusion-weighted (DW) image pairs are not readily available, we propose a semi-supervised DW image SR reconstruction method based on multiple references (MRSR) extracted from other subjects. In MRSR, the prior information of multiple HR reference images is migrated into a residual-like network to assist SR reconstruction of DW images, and a CycleGAN-based semi-supervised strategy is used to train the network with 30% matched and 70% unmatched LR-HR image pairs. We evaluate the performance of the MRSR by comparing against SOTA methods on an HCP dataset in terms of the quality of reconstructed DW images and diffusion metrics. MRSR achieves the best performance, with the mean PSNR/SSIM of DW images being improved by at least 14.3%/28.8% and 1%/1.4% respectively relative to SOTA unsupervised and supervised learning methods, and with the fiber orientations deviating from the ground truth by about 6.28 degrees on average, the RMSEs of fractional anisotropy, mean diffusivity, axial diffusivity and radial diffusivity being 3.0%, 4.6%, 5.7% and 4.5% respectively relative to the ground truth. We validate the effectiveness of the proposed network structure, multiple-reference and CycleGAN-based semi-supervised learning strategies for SR reconstruction of diffusion tensor images through the ablation studies. The proposed method allows us to achieve SR reconstruction for diffusion tensor images with a limited number of matched image pairs.