ArticlePhysics in medicine and biology2019
A detector's eye view (DEV)-based OSEM algorithm for benchtop x-ray fluorescence computed tomography (XFCT) image reconstruction.
Article in Physics in medicine and biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Monte carlo study of detection limit for gold nanoparticles within small animal sized object during benchtop X-ray fluorescence computed tomography.Scientific reports · 2026Article
- Deep learning based rapid X-ray fluorescence signal extraction and image reconstruction for preclinical benchtop X-ray fluorescence computed tomography applications.Scientific reports · 2025Article
- Enabling Low-Dose In Vivo Benchtop X-ray Fluorescence Computed Tomography through Deep-Learning-Based Denoising.Journal of imaging · 2024Article
- Monte Carlo study of x-ray detection configurations for benchtop x-ray fluorescence computed tomography of gold nanoparticle-loaded objects.Physics in medicine and biology · 2020Article
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6 authors.
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Abstract
In this study, we developed a detector's eye view (DEV)-based ordered subsets expectation maximization (OSEM) algorithm for more accurate reconstruction of benchtop x-ray fluorescence computed tomography (XFCT) images. The proposed approach was tested using two sets of benchtop XFCT imaging data derived from a newly performed gold nanoparticle (GNP)-containing phantom imaging study and a previously published postmortem benchtop XFCT imaging study of a tumor-bearing mouse injected with GNPs. DEV-based OSEM resulted in higher spatial resolution (up to ~20% decrease in the full width at half maximum values of the regions of interest), compared with filtered back-projection (FBP) and traditional OSEM. It also resulted in up to an order of magnitude smaller background noise in the reconstructed images than FBP, while producing consistently less background noise than traditional OSEM.
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