Unsupervised Representation Learning for 3D MRI Super Resolution with Degradation Adaptation

arXiv:2205.06891v5 Announce Type: replace-cross
Abstract: High-resolution (HR) magnetic resonance imaging is critical in aiding doctors in their diagnoses and image-guided treatments. However, acquiring HR images can be time-consuming and costly. Consequently, deep learning-based super-resolution reconstruction (SRR) has emerged as a promising solution for generating super-resolution (SR) images from low-resolution (LR) images. Unfortunately, training such neural networks requires aligned authentic HR and LR image pairs, which are challenging to obtain due to patient movements during and between image acquisitions. While rigid …

abstract arxiv cs.cv deep learning doctors eess.iv however image images imaging low mri physics.med-ph representation representation learning resolution solution super resolution type unsupervised