BPF Algorithms for Multiple Source-Translation Computed Tomography Reconstruction

TL;DR

This paper introduces two novel BPF algorithms for multiple source-translation CT that improve imaging efficiency and reduce sampling requirements while maintaining high resolution, making large object imaging more practical.

Contribution

It develops two new BPF-based algorithms, S-BPF and D-BPF, for mSTCT, enhancing reconstruction quality and efficiency over existing methods like V-FBP.

Findings

D-BPF reduces source sampling by 75% compared to V-FBP.

D-BPF achieves high-resolution reconstruction with fewer projections.

S-BPF provides more stable results than D-BPF.

Abstract

Micro-computed tomography (micro-CT) is a widely used state-of-the-art instrument employed to study the morphological structures of objects in various fields. However, its small field-of-view (FOV) cannot meet the pressing demand for imaging relatively large objects at high spatial resolutions. Recently, we devised a novel scanning mode called multiple source translation CT (mSTCT) that effectively enlarges the FOV of the micro-CT and correspondingly developed a virtual projection-based filtered backprojection (V-FBP) algorithm for reconstruction. Although V-FBP skillfully solves the truncation problem in mSTCT, it requires densely sampled projections to arrive at high-resolution reconstruction, which reduces imaging efficiency. In this paper, we developed two backprojection-filtration (BPF)-based algorithms for mSTCT, i.e., S-BPF (derivatives along source) and D-BPF (derivatives along detector). D-BPF can achieve high-resolution reconstruction with fewer projections than V-FBP and S-BPF. Through simulated and real experiments conducted in this paper, we demonstrate that D-BPF can reduce source sampling by 75% compared with V-FBP at the same spatial resolution, which makes mSTCT more feasible in practice. Meanwhile, S-BPF can yield more stable results than D-BPF, which is similar to V-FBP.