Ke Wang

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2023-178

May 16, 2023

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2023/EECS-2023-178.pdf

Magnetic Resonance Imaging (MRI) is an effective medical imaging modality, offering excellent soft tissue contrast, versatile orientation capabilities, and no ionizing radiation exposure. However, its inherent physics constraints lead to time-consuming data acquisition and prolonged scan times. To reduce scan time, recently, deep learning (DL) has achieved notable success in reconstructing high-quality MR images from under-sampled data, surpassing conventional non-learned approaches. Despite this progress, challenges such as hand-crafted loss functions, high computational costs, and limited training data remain. In this dissertation, I will present a series of projects focused on enhancing fidelity and efficiency in MRI reconstruction.

I will first introduce a supervised learning method that synthesizes multi-contrast MR images from a single MRF scan. Next, I will present a novel feature loss designed to preserve perceptual similarity, demonstrating its effectiveness in high-fidelity image reconstruction. Following that, I will touch upon memory-efficient learning for high-dimensional MRI reconstruction and present a novel framework for rigorous uncertainty estimation. Lastly, I will introduce a novel complex-valued representation tailored for tasks with limited training data.

Advisors: Michael Lustig and Stella Yu

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BibTeX citation:

@phdthesis{Wang:EECS-2023-178,
    Author= {Wang, Ke},
    Title= {Magnetic Resonance Image Reconstruction with Greater Fidelity and Efficiency},
    School= {EECS Department, University of California, Berkeley},
    Year= {2023},
    Month= {May},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2023/EECS-2023-178.html},
    Number= {UCB/EECS-2023-178},
    Abstract= {Magnetic Resonance Imaging (MRI) is an effective medical imaging modality, offering excellent soft tissue contrast, versatile orientation capabilities, and no ionizing radiation exposure. However, its inherent physics constraints lead to time-consuming data acquisition and prolonged scan times. To reduce scan time, recently, deep learning (DL) has achieved notable success in reconstructing high-quality MR images from under-sampled data, surpassing conventional non-learned approaches. Despite this progress, challenges such as hand-crafted loss functions, high computational costs, and limited training data remain. In this dissertation, I will present a series of projects focused on enhancing fidelity and efficiency in MRI reconstruction.

I will first introduce a supervised learning method that synthesizes multi-contrast MR images from a single MRF scan. Next, I will present a novel feature loss designed to preserve perceptual similarity, demonstrating its effectiveness in high-fidelity image reconstruction. Following that, I will touch upon memory-efficient learning for high-dimensional MRI reconstruction and present a novel framework for rigorous uncertainty estimation. Lastly, I will introduce a novel complex-valued representation tailored for tasks with limited training data.},
}

EndNote citation:

%0 Thesis
%A Wang, Ke 
%T Magnetic Resonance Image Reconstruction with Greater Fidelity and Efficiency
%I EECS Department, University of California, Berkeley
%D 2023
%8 May 16
%@ UCB/EECS-2023-178
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2023/EECS-2023-178.html
%F Wang:EECS-2023-178