Date of Award


Document Type


Degree Name

Master of Science (MS)


Mathematics and Systems Engineering

First Advisor

Xianqi Li

Second Advisor

Pei Liu

Third Advisor

Ryan T. White

Fourth Advisor

Gnana Bhaskar Tenali


Magnetic Resonance Imaging (MRI) is a cornerstone in obtaining intricate visualizations of anatomy and physiological processes within the human body. However, its extensive scan duration not only causes patient discomfort but also increases the likelihood of motion-induced artifacts in the images. To address such a challenge, this study investigates deep neural network models for reconstructing high-resolution MRI images from noisy and significantly undersampled data in a supervised learning manner. Specifically, it compares three models: a conventional U-Net, a self-attentive U-Net, and an innovative probabilistic diffusion model that builds upon the self-attentive U-Net architecture. These models are evaluated on their ability to reconstruct high resolution images from low-quality, undersampled inputs derived via various K-Space subsampling techniques, which mimic real-world MRI scan sampling patterns.


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