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Fast and accurate X-ray fluorescence computed tomography imaging with the ordered-subsets expectation maximization

Qun Yang1, Biao Deng, Weiwei Lv

  • 1Shanghai Synchrotron Radiation Facility, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201204, People's Republic of China.

Journal of Synchrotron Radiation
|February 18, 2012
PubMed
Summary
This summary is machine-generated.

The ordered-subsets expectation maximization algorithm (OSEM) improves X-ray fluorescence computed tomography (XFCT) imaging. OSEM offers enhanced accuracy and faster reconstruction times, making XFCT more efficient.

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Area of Science:

  • Medical Imaging
  • Computational Imaging
  • Spectroscopy

Background:

  • X-ray fluorescence computed tomography (XFCT) is an imaging modality.
  • Image reconstruction in XFCT often relies on algorithms like filtered back-projection (FBP).
  • Image quality and acquisition time are key considerations in XFCT.

Purpose of the Study:

  • To introduce and evaluate the ordered-subsets expectation maximization (OSEM) algorithm for XFCT.
  • To compare OSEM's performance against traditional methods like FBP.
  • To assess OSEM's impact on image quality, angular sampling, and reconstruction time in XFCT.

Main Methods:

  • Simulations were performed to evaluate OSEM's accuracy and performance under varying angular sampling intervals.
  • Experimental studies were conducted using an artificial phantom and a biological sample (cirrhotic liver).
  • OSEM's image reconstruction time was analyzed with an optimum number of subsets.

Main Results:

  • OSEM demonstrated superior accuracy compared to filtered back-projection in simulations.
  • OSEM effectively suppressed image quality degradation across a wide range of angular sampling intervals.
  • Experimental results showed that OSEM allows for improved angular sampling, reducing data acquisition time while maintaining satisfying image quality.
  • OSEM achieved approximately a 50% reduction in image reconstruction time with an optimal number of subsets.

Conclusions:

  • OSEM is a promising algorithm for XFCT imaging.
  • OSEM enables faster data acquisition and image reconstruction in XFCT.
  • OSEM offers a potential pathway for achieving both speed and accuracy in XFCT applications.