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Related Experiment Videos

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

Luzhen Deng1,2, Md F Ahmed1, Sandun Jayarathna1

  • 1Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America.

Physics in Medicine and Biology
|April 9, 2019
PubMed
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A new detector's eye view (DEV)-based ordered subsets expectation maximization (OSEM) algorithm improves X-ray fluorescence computed tomography (XFCT) image reconstruction. This method enhances spatial resolution and significantly reduces background noise compared to traditional techniques.

Area of Science:

  • Medical Imaging
  • Biomedical Engineering
  • Computational Imaging

Background:

  • X-ray fluorescence computed tomography (XFCT) is a powerful imaging technique.
  • Accurate image reconstruction is crucial for quantitative analysis in XFCT.
  • Traditional methods like filtered back-projection (FBP) and OSEM have limitations in resolution and noise.

Purpose of the Study:

  • To develop and evaluate a novel detector's eye view (DEV)-based ordered subsets expectation maximization (OSEM) algorithm for XFCT.
  • To improve the accuracy of XFCT image reconstruction, particularly for quantitative imaging of nanoparticles.
  • To compare the performance of the DEV-based OSEM algorithm against FBP and traditional OSEM.

Main Methods:

  • Implementation of a DEV-based OSEM algorithm tailored for XFCT data.

Related Experiment Videos

  • Testing the algorithm on benchtop XFCT data from a gold nanoparticle (GNP) phantom.
  • Validation using previously published XFCT data from a tumor-bearing mouse model injected with GNPs.

    • Main Results:

    • DEV-based OSEM achieved higher spatial resolution, reducing full width at half maximum (FWHM) by up to 20% compared to FBP and traditional OSEM.
    • The algorithm reduced background noise by up to an order of magnitude compared to FBP.
    • DEV-based OSEM consistently produced lower background noise than traditional OSEM.

    Conclusions:

    • The DEV-based OSEM algorithm offers superior performance for XFCT image reconstruction.
    • This advancement enables more accurate quantitative analysis and improved visualization in XFCT imaging.
    • The method shows significant potential for applications involving nanoparticle imaging in biological and medical research.