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PhD Defense Deep Learning Methods for Accelerated MRI Reconstruction: Optimization-Inspired Unrolled Networks and Beyond
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Speaker: Bingyu Xin

Bio

Location : CBIM 22

Committee: 

Professor Dimitris N. Metaxas

Professor Hongyi Wang

Professor Konstantinos Michmizos

Professor Junzhou Huang (University of Texas at Arlington)

Event Type: PhD Defense

Abstract: Magnetic Resonance Imaging (MRI) provides superior soft-tissue contrast without ionizing radiation, but long acquisition times limit clinical efficiency and increase motion artifacts. Accelerated MRI reconstructs images from undersampled k-space measurements, forming an ill-posed inverse problem that requires strong prior knowledge. Deep unrolled networks have become apowerful framework by combining data consistency with learned regularization; however, existing models require separate networks for different acquisition settings, rely on slow and poorly scalable gradient descent schemes, and often become unstable when scaled. This dissertation develops a unified and scalable framework for accelerated MRI reconstruction. We first establish a learned half-quadratic splitting formulation that provides a principled foundation for deep unrolling and enables prompt-based conditioning, allowing a single model to generalize across diverse acquisition protocols. We then introduce adaptive gradient descent with pixel-wise learning rates and momentum acceleration, achieving up to 9× faster convergence while reducing GPU memory usage by 55%. To support large-scale modeling, we scale the architecture to 245 million parameters and propose a cascade-wise spectral-norm optimization strategy for stable training. Experiments on cardiac, brain, and knee MRI benchmarks demonstrate state-of-the-art reconstruction quality across datasets and sampling patterns. Beyond supervised unrolled models, we further explore diffusion-based generative priors for plug-and-play reconstruction and implicit neural representations for fully self-supervised reconstruction. Together, these contributions improve scalability, generalization, and stability, revealing a fundamental tradeoff between reconstruction quality and supervision.

Organization: 

Contact  Professor Dimitris Metaxas (Chair)

Zoom Link: https://rutgers.zoom.us/j/99279783790?pwd=9DNM6KqZGw8RgkXD17xKc621I52Ltq.1