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Algorithms for joint activity-attenuation estimation from positron emission tomography scatter.

Yannick Berker1,2,3, Volkmar Schulz4,5,6, Joel S Karp7

  • 1Division of X-ray Imaging and Computed Tomography, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg, 69120, Germany. yannick.berker@dkfz.de.

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Summary

Scatter-to-attenuation (S2A) reconstruction using scattered emission data improves positron emission tomography attenuation correction. Maximum-likelihood gradient ascent (MLGA) offers better convergence than MLEM-OSL, especially in high-attenuation scenarios.

Keywords:
Attenuation correctionCompton scatteringImage reconstructionPositron emission tomography

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Area of Science:

  • Medical Imaging
  • Nuclear Medicine
  • Image Reconstruction

Background:

  • Positron emission tomography (PET) attenuation correction is challenging without transmission scans.
  • Scattered emission data can provide valuable information for quantitative scatter-to-attenuation (S2A) reconstruction.
  • S2A reconstruction is a key component for joint estimation of activity and attenuation.

Purpose of the Study:

  • To evaluate two S2A reconstruction algorithms: MLEM-OSL and MLGA.
  • To analyze the convergence properties and speed of these algorithms.
  • To propose and assess joint estimation of activity and attenuation using scattered and true emission data.

Main Methods:

  • Studied MLEM-OSL and MLGA algorithms, focusing on convergence and speed.
  • Compared algorithm performance in simulations with varying spatial scales and object sizes.
  • Developed joint estimation methods combining S2A algorithms with scatter-MLEM, trues-MLEM, and MLTR.

Main Results:

  • MLEM-OSL struggles with high attenuation due to linearization issues; MLGA shows better convergence independence.
  • MLGA converges more reliably across different attenuation levels compared to MLEM-OSL.
  • Joint estimation benefits from scattered data, particularly in avoiding local minima.
  • Scattered data is crucial for accurate joint estimation in high-attenuation cases.

Conclusions:

  • S2A algorithm performance is scale-dependent.
  • MLGA offers computational advantages and broader convergence compared to MLEM-OSL.
  • Scattered emission data significantly enhances joint activity and attenuation estimation in PET.