BPF-type Region-of-interest Reconstruction for Parallel Translational Computed Tomography

TL;DR

This paper introduces two novel BPF-type algorithms for ROI reconstruction in parallel translational CT, effectively reducing artifacts from truncated data and improving image quality.

Contribution

It presents two new BPF-type algorithms, MP-BPF and MZ-BPF, specifically designed for ROI reconstruction in PTCT with truncated projections.

Findings

Accurate ROI reconstruction from truncated data.

High-quality images for entire support in certain cases.

Effective handling of data redundancy in multi-linear modes.

Abstract

Recently, an ultra-low-cost linear scan based tomography architecture was proposed by our team. Similar to linear tomosynthesis, the source and detector are translated in opposite directions and the data acquisition system targets on a region-of-interest (ROI) to acquire data for image reconstruction. This kind of tomography architecture was named parallel translational computed tomography (PTCT). In our previous studies, filtered backprojection (FBP)-type algorithms were developed to reconstruct images from PTCT. However, the reconstructed ROI images from truncated projections have severe truncation artifacts. In this paper, we propose two backprojection filtering (BPF)-type algorithms named MP-BPF and MZ-BPF to reconstruct ROI images from truncated PTCT data. A weight function is constructed to deal with data redundancy for multi-linear translations modes. Extensive numerical simulations are performed to evaluate the proposed MP-BPF and MZ-BPF algorithms for PTCT in fan-beam geometry. Qualitative and quantitative results demonstrate that the proposed BPF-type algorithms can not only accurately reconstruct ROI images from truncated projections but also provide high-quality images for the entire image support in some circumstances.