Abstract
Magnetic Resonance Imaging (MRI) is a powerful medical imaging modality that provides high-resolution anatomical information about tissues. However, the intrinsic trade-off between acquisition time and image quality poses challenges in obtaining high-resolution images within a clinically feasible timeframe. This study introduces a novel approach to acquire high-resolution images in short scan times based on Super-Resolution Diffusion Bridges (SRDB). The proposed method leverages advanced machine learning techniques based on diffusion models to upscale MR images. While standard diffusion models learn a mapping from Gaussian distributed noise images to target images, SRDB instead learns a mapping from low-resolution MR images to high-resolution images. Unlike the task-independent learning in standard diffusion model, SRDB thus performs task-based learning to improve structural consistency and better preservation of anatomical features. In this way, the trained models help capture fine details that may be missed in standard low-resolution MRI acquisitions.
Usama Mirza
Ph.D. Student in Electrical and Electronics Engineering
My research focuses on generative models (diffusion models, GANs) for computational imaging, particularly accelerated MRI reconstruction and multi-contrast MRI synthesis.