Stacked Deep U-Net with Hybrid Assisted Priors for Through-Plane Super-Resolution in Brain MRI

Motivation: The motivation behind this study is to alleviate discomfort during clinical exams by improving through-plane resolution. Goal(s): Our objective is to develop a deep learning-based super-resolution approach for low-resolution T2 images, reducing patient discomfort and improving diagnosis accuracy. Approach: We develop a deep learning framework that combines information from T1 and T2 scans, enabling the generation of high-quality images in the through-plane direction. Results: The proposed approach successfully enhances through-plane super-resolution in brian MRI, resulting in superior image quality. This improvement has the potential to improve diagnostic accuracy and alleviate patient discomfort during clinical exams. Impact: This study presents a novel deep learning framework that improves through-plane Super-Resolution in brain MRIs, thus enhancing diagnostic accuracy and reducing patient discomfort during routine health checks.