# BPF Algorithms for Multiple Source-Translation Computed Tomography Reconstruction

arXiv: https://arxiv.org/abs/2305.18878 · HTML: https://mlsanity.com/paper/2305.18878/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 52 references that are in the map.
- First posted on arXiv 30 May 2023.

## Builds on (most cited first, 3 of 3)

- [One Network to Solve All ROIs: Deep Learning CT for Any ROI using Differentiated Backprojection](https://mlsanity.com/paper/1810.00500/) (2018)
- [Field of View Extension in Computed Tomography Using Deep Learning Prior](https://mlsanity.com/paper/1911.01178/) (2019)
- [Fiducial marker recovery and detection from severely truncated data in navigation assisted spine surgery](https://mlsanity.com/paper/2108.13844/) (2021)

## Built on by (most cited first, 3 of 3)

- [Analytical reconstructions of full-scan multiple source-translation computed tomography under large field of views](https://mlsanity.com/paper/2305.19767/) (2023)
- [OSNet & MNetO: Two Types of General Reconstruction Architectures for Linear Computed Tomography in Multi-Scenarios](https://mlsanity.com/paper/2309.11858/) (2023)
- [Geometric Artifact Correction for Symmetric Multi-Linear Trajectory CT: Theory, Method, and Generalization](https://mlsanity.com/paper/2408.15069/) (2024)

## Abstract (arXiv)

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.