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 $0.913\pm0.045$.

翻译：高空间分辨率磁共振成像(MRI)将在以优异方式直观这种纤维通道方面发挥重要作用。然而,获得这种分辨率的图像要花费较长的扫描时间。由于病人的心理和身体状况,可以使用更长的扫描时间来描述神经组织的微结构。单一图像超级分辨率(SISR),一种旨在从一个单一的低分辨率(LR)输入图像中获取高分辨率(HR)细节的技术,通过深层学习实现。与内插技术或稀释编码算法相比,深度学习从大数据集中提取先前的知识,从低分辨率对应方产生高级的MRI图像。在这项研究中,提出了一种基于深层学习的超分辨率技术,并应用于DW-$M.95的SIS.45 单一图像(HR),这种技术旨在从一个单一的低分辨率(LR)输入图像中获取高分辨率(HR)的细节,这是本项研究的重点。 与内层图像模型相比,从大数据集中提取了高级知识,从低分辨率对低分辨率对应方产生高级的MRI图像。