ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning

TL;DR

This paper introduces ShuffleUNet, a deep learning method for super-resolution of diffusion-weighted MRI images, improving image quality from low-resolution scans to aid in brain fiber tracking.

Contribution

The study proposes a novel ShuffleUNet architecture for super-resolution of DW-MRI, demonstrating significant improvements over baseline methods using the IXI dataset.

Findings

Achieved SSIM of 0.913±0.045 on super-resolved DW-MRI images.

Significant statistical improvement over baseline super-resolution techniques.

Enhanced visualization of brain fiber tracts with higher resolution images.

Abstract

Diffusion-weighted magnetic resonance imaging (DW-MRI) can be used to characterise the microstructure of the nervous tissue, e.g. to delineate brain white matter connections in a non-invasive manner via fibre tracking. Magnetic Resonance Imaging (MRI) in high spatial resolution would play an important role in visualising such fibre tracts in a superior manner. However, obtaining an image of such resolution comes at the expense of longer scan time. Longer scan time can be associated with the increase of motion artefacts, due to the patient's psychological and physical conditions. Single Image Super-Resolution (SISR), a technique aimed to obtain high-resolution (HR) details from one single low-resolution (LR) input image, achieved with Deep Learning, is the focus of this study. Compared to interpolation techniques or sparse-coding algorithms, deep learning extracts prior knowledge from big datasets and produces superior MRI images from the low-resolution counterparts. In this research, a deep learning based super-resolution technique is proposed and has been applied for DW-MRI. Images from the IXI dataset have been used as the ground-truth and were artificially downsampled to simulate the low-resolution images. The proposed method has shown statistically significant improvement over the baselines and achieved an SSIM of $0.913\pm0.045$.