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Ordered subset expectation maximization algorithm for positron emission tomographic image reconstruction using belief

Yang-Ming Zhu1

  • 1Philips HealthTech, Advanced Molecular Imaging, Highland Heights, Ohio, United States.

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|March 7, 2019
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Summary
This summary is machine-generated.

Incorporating prior information into time-of-flight (TOF) positron emission tomography (PET) image reconstruction using ordered subset expectation maximization (OSEM) with belief kernels improves image quality. This method enhances lesion definition and reduces noise compared to standard TOF OSEM reconstruction.

Keywords:
belief kernelsordered subset expectation maximizationpositron emission tomographytime-of-flight

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Area of Science:

  • Medical Imaging
  • Nuclear Medicine
  • Image Reconstruction

Background:

  • Positron Emission Tomography (PET) is a crucial nuclear medicine imaging technique.
  • Ordered Subset Expectation Maximization (OSEM) is a common algorithm for PET image reconstruction.
  • Time-of-Flight (TOF) information improves PET image resolution and signal-to-noise ratio.

Purpose of the Study:

  • To investigate the benefits of incorporating prior information into list-mode TOF PET image reconstruction using OSEM.
  • To evaluate the impact of using a 'belief kernel' that combines prior activity profiles with TOF measurements.
  • To assess image quality improvements in both phantom and patient studies.

Main Methods:

  • Developed a novel OSEM algorithm incorporating a belief kernel derived from prior activity profiles and TOF measurements.
  • Performed list-mode TOF PET image reconstruction on an IEC phantom and patient data.
  • Smoothed activity profiles to control noise and evaluated different smoothness levels.
  • Assessed image quality using visual inspection, contrast recovery coefficients (CRC), and background variability.

Main Results:

  • Reconstruction using belief kernels demonstrated faster convergence and more visually appealing images.
  • Higher CRCs were observed for all region sizes in images reconstructed with belief kernels compared to the baseline.
  • Reduced background variability (coefficient of variation) was noted in images reconstructed using belief kernels.
  • Patient studies showed better defined lesions, improved contrast, and reduced noise with the belief kernel method.

Conclusions:

  • OSEM PET image reconstruction using belief kernels that integrate prior image information and TOF measurements is a promising technique.
  • This approach offers significant improvements in image quality, including enhanced lesion conspicuosness and reduced noise.
  • Further investigation into belief kernel-based reconstruction is warranted for clinical application.