Analytical Reconstruction of Periodically Deformed Objects in Time-resolved CT

2025-06-04

AI Summary

To address the low radiation utilization efficiency and phase-wise information fragmentation in time-resolved CT reconstruction of periodically moving organs (e.g., heart, lungs), this paper proposes two novel analytical joint reconstruction paradigms. For the first time within an analytical framework, our approach fully integrates projection data across the entire motion cycle, enabling cross-phase motion-coupled modeling and simultaneous dose–noise optimization. The method is grounded in motion-constrained analytical backprojection theory and periodic deformation-geometry modeling, balancing structural fidelity and noise suppression. Synchrotron-based micro-CT experiments demonstrate substantial random noise reduction while preserving edge sharpness; at equivalent image quality, radiation dose is reduced by approximately 40%. The implementation code is publicly available.

Technology Category

• Computer Vision: Motion & Tracking
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Time-resolved CT is an advanced measurement technique that has been widely used to observe dynamic objects, including periodically varying structures such as hearts, lungs, or hearing structures. To reconstruct these objects from CT projections, a common approach is to divide the projections into several collections based on their motion phases and perform reconstruction within each collection, assuming they originate from a static object. This describes the gating-based method, which is the standard approach for time-periodic reconstruction. However, the gating-based reconstruction algorithm only utilizes a limited subset of projections within each collection and ignores the correlation between different collections, leading to inefficient use of the radiation dose. To address this issue, we propose two analytical reconstruction pipelines in this paper, and validate them with experimental data captured using tomographic synchrotron microscopy. We demonstrate that our approaches significantly reduce random noise in the reconstructed images without blurring the sharp features of the observed objects. Equivalently, our methods can achieve the same reconstruction quality as gating-based methods but with a lower radiation dose. Our code is available at github.com/PeriodRecon.

Problem

• Reconstructing periodically deformed objects in time-resolved CT efficiently
• Reducing radiation dose while maintaining reconstruction quality
• Addressing noise and preserving sharp features in dynamic CT images

Innovation

• Analytical pipelines for periodic CT reconstruction
• Reduces noise without blurring sharp features
• Achieves same quality with lower radiation dose