05/14/2026
By Danielle Fretwell

The Francis College of Engineering, Department of Electrical and Computer Engineering, invites you to attend a Doctoral Dissertation Proposal defense by Changsheng Fang titled: "Probabilistic Generative Modeling for Incomplete CT Reconstruction."

Candidate Name: Changsheng Fang
Degree: Doctoral
Defense Date: Tuesday, May, 26, 2026
Time: 10 - 11 a.m.
Location: Ball Hall 302

Committee:

  • Advisor: Hengyong Yu, Professor, Electrical & Computer Engineering, University of Massachusetts Lowell
  • Yan Luo, Professor, Electrical & Computer Engineering, University of Massachusetts Lowell
  • SeungWoo Son, Associate Professor, Electrical & Computer Engineering, University of Massachusetts Lowell
  • Dayang Wang, Ph.D, Research Scientist, Subtle Medical Inc.

Brief Abstract:
Computed tomography (CT) is widely used in clinical diagnosis and intervention, but high-quality reconstruction typically requires sufficient projection measurements. In practice, complete data acquisition is often limited by radiation dose, scanning time, system geometry, patient motion, and clinical workflow constraints. Therefore, incomplete CT reconstruction, including sparse-view CT and limited-angle CT, remains a challenging ill-posed inverse problem that can cause severe artifacts, structural distortion, and loss of anatomical details. This thesis investigates probabilistic generative modeling methods for accurate, efficient, and physically consistent incomplete CT reconstruction.

Firstly, a Residual Poisson Flow Generative Model (ResPF) is proposed for sparse-view CT reconstruction. By adapting Poisson Flow Generative Models to paired sparse-view and full-view CT data, ResPF learns deterministic generative trajectories for high-quality image recovery. Accelerated sampling, projection-domain data consistency, and residual fusion are incorporated to improve reconstruction fidelity and reduce inference time. Secondly, a Lightweight Wavelet Diffusion with Kolmogorov–Arnold Network (WDK-Net) is developed for limited-angle cardiac CT reconstruction. WDK-Net uses wavelet-domain diffusion to recover global anatomical structures in a reduced-dimensional space, high-frequency enhancement to restore directional details, and Kolmogorov–Arnold Network-based refinement to suppress artifacts and improve local structures. This structure–detail decoupled design improves reconstruction quality across multiple limited-angle settings while maintaining practical efficiency. The proposed studies are evaluated using both simulation and clinical CT datasets to assess reconstruction accuracy, artifact suppression, structural preservation, and computational efficiency under different incomplete acquisition settings. Experimental results demonstrate that probabilistic generative modeling, when combined with imaging physics and domain-specific reconstruction design, can provide an effective and practical solution for incomplete CT reconstruction.