Analytical Reconstruction of Periodically Deformed Objects in Time-resolved CT

TL;DR

This paper introduces two analytical reconstruction methods for time-resolved CT that improve image quality and reduce radiation dose by better utilizing projection data from periodically deformed objects.

Contribution

The paper presents novel analytical reconstruction pipelines that outperform standard gating-based methods in noise reduction and dose efficiency for time-resolved CT.

Findings

Significantly reduce noise in reconstructed images

Achieve comparable quality to gating-based methods with lower radiation dose

Validated with experimental data from synchrotron microscopy

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.