Topic: Solving Magnetic Resonance Imaging Inverse Problems with Physics-Guided Machine Learning and Agentic Artificial Intelligence
EAS Doctoral Dissertation Defense by Gulfam Ahmed Saju
Committee chair: Dr. Yuchou Chang, Department of Computer and Information Science
Committee members:
- Dr. Haiping Xu, Department of Computer and Information Science
- Dr. Long Jiao, Department of Computer and Information Science
- Dr. Donghui Yan, Mathematics Department
Magnetic Resonance Imaging (MRI) reconstruction is an ill-posed inverse problem. It is complicated by undersampling, noise, motion, and uncertainty in coil sensitivities. Recently, physics-guided and learning-based methods have advanced the field, but current systems remain vulnerable to distribution shift, calibration errors, and manual method selection in clinical workflows. This dissertation develops physics-guided, data-efficient reconstruction and Vision Language Model (VLM) based Agentic artificial intelligence systems for automated, robust MRI under multiple degradation. First, it introduces untrained neural network (UNN) priors for Periodically Rotated Overlapping ParallEL Lines with Enhanced Reconstruction (PROPELLER) and Cartesian MRI acquisitions. UNN suppresses artifacts without external training data. Second, it proposes a novel synthetic blade augmentation technique to strengthen unrolled deep networks for PROPELLER MRI. This method improves generalization across scanners and protocol variations. Third, an ensemble learning-based approach for accelerated and noise-
resilient parallel MRI is introduced. It stabilizes joint image-sensitivity estimation under limited or imperfect calibration. Fourth, a retrospective motion correction strategy is developed that ensembles three generative AI models, each developed to address distinct motion types. Finally, this dissertation introduces two agentic artificial intelligence frameworks using VLM to automate MRI inverse problems end-to-end. First, AgentMRI is a single VLM-driven controller that uses multi-query, confidence-weighted consensus to identify degradation and dispatch the appropriate correction tool. Additionally, a hierarchical multi-agent framework is developed, where agents debate and reach a reliability-weighted decision. The agents report decision confidence and reduce operator intervention via tool use and structured reasoning. Comprehensive experiments on diverse MRI datasets evaluate image quality, robustness to shift, motion severity, and computation. The agents are assessed using degradation-classification accuracy, confidence calibration, and reduction in manual steps. Results demonstrate improved reconstruction fidelity at matched acceleration, enhanced resilience to motion, and practical gains from automated method selection. Collectively, these contributions advance physics-guided, learning-based inverse problems and establish agentic AI as a viable controller for self-regulating MRI reconstruction.
Time: Nov 17, 2025, 11:00 AM Eastern Time (US and Canada)
In-Person attendance
Location: DION 311
Online attendance
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https://umassd.zoom.us/j/96763916424?pwd=2tC00OunXtqsf1dsbMhLe9lVroKrfZ.1
Meeting ID: 967 6391 6424
Passcode: 604765
For further information please contact Dr. Yuchou Chang
Dion 311 and virtual
Dr Yuchou Chang
X8475
ychang1@umassd.edu
https://umassd.zoom.us/j/96763916424?pwd=2tC00OunXtqsf1dsbMhLe9lVroKrfZ.1