A detector's eye view (DEV)-based OSEM algorithm for benchtop x-ray fluorescence computed tomography (XFCT) image reconstruction

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 dat...

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Veröffentlicht in:Physics in medicine & biology Jg. 64; H. 8; S. 08NT02
Hauptverfasser: Deng, Luzhen, Ahmed, Md F, Jayarathna, Sandun, Feng, Peng, Wei, Biao, Cho, Sang Hyun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England 08.04.2019
Schlagworte:
Algorithms
Animals
Cell Transformation, Neoplastic
Fluorescence
Humans
Image Processing, Computer-Assisted - methods
Mice
Phantoms, Imaging
Tomography, X-Ray Computed
ISSN:1361-6560, 1361-6560
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:1361-6560
1361-6560
DOI:10.1088/1361-6560/ab0e9d