04/23/2025
By Danielle Fretwell

The Francis College of Engineering, Department of Electrical and Computer Engineering, invites you to attend a Doctoral Dissertation Proposal defense by Shuo Han on: "Generative Model-based Cardiac Computed Tomography Reconstruction from Incomplete Projection Data."

Candidate Name: Shuo Han
Degree: Doctoral
Defense Date: Friday, May 2, 2025
Time: 11 a.m.-12 p.m.
Location: Ball Hall 302

Committee:

  • Advisor: Hengyong Yu, Professor, Department of Electrical and Computer Engineering, UMass Lowell
  • Yan Luo, Professor, Department of Electrical and Computer Engineering, UMass Lowell
  • Seung Woo Son, Department of Electrical and Computer Engineering, UMass Lowell

Abstract:
Computed tomography (CT) has become an essential imaging modality in medical diagnosis, offering detailed anatomical insights across various clinical applications. Specifically, cardiac CT has emerged as particularly valuable for diagnosing and managing cardiovascular diseases due to its ability to visualize the heart's anatomy and function in detail. However, cardiac CT requires high temporal resolution to effectively capture the rapid motion of the beating heart. Limited-angle acquisitions help achieve this by shortening scan times, but this technique inherently results in incomplete projection data, leading to substantial image degradation.

This thesis investigates novel reconstruction methods leveraging generative diffusion models to overcome challenges associated with incomplete projection data in cardiac CT. Firstly, a physics-informed score-based diffusion model (PSDM) is proposed, integrating data-driven priors from diffusion models with model-driven priors derived from the primal-dual hybrid gradient (PDHG) algorithm and Fourier fusion techniques. Secondly, an optimized 3D diffusion based reconstruction method is developed, combining regularized derivative least squares (RDLS) algorithms and denoising diffusion implicit models, significantly reducing computational time while maintaining high-quality image reconstruction.

Comprehensive numerical simulations and real-data experiments demonstrate the effectiveness of the proposed generative diffusion approaches in reconstructing high-fidelity cardiac CT images, substantially mitigating reconstruction artifacts and enhancing temporal resolution. These methodologies represent significant advancements in cardiac CT imaging, ultimately contributing to improved diagnostic accuracy and clinical outcomes.