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Related Concept Videos

Positron Emission Tomography01:29

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Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
One of the main requirements of a PET scan is a positron-emitting radioisotope, which is produced in a cyclotron and then attached to a substance used by the part of the body...
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Updated: Feb 25, 2026

Radiotracer Administration for High Temporal Resolution Positron Emission Tomography of the Human Brain: Application to FDG-fPET
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sMLACF: a generalized expectation-maximization algorithm for TOF-PET to reconstruct the activity and attenuation

Koen Salvo1, Michel Defrise1

  • 1Department of Medical Imaging and Physical Sciences, Vrije Universiteit Brussel, B-1090, Brussels, Belgium.

Physics in Medicine and Biology
|July 29, 2017
PubMed
Summary
This summary is machine-generated.

A new algorithm, simultaneous maximum-likelihood attenuation correction factors (sMLACF), estimates activity and attenuation in positron emission tomography using only emission data. It offers similar results to existing methods but converges slower.

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Area of Science:

  • Medical Imaging
  • Nuclear Medicine
  • Computational Science

Background:

  • Accurate attenuation correction is crucial in positron emission tomography (PET) for quantitative imaging.
  • Existing methods like MLACF require complex calculations or additional data.
  • Developing efficient algorithms for attenuation correction from emission data alone is an ongoing challenge.

Purpose of the Study:

  • To introduce and validate the simultaneous maximum-likelihood attenuation correction factors (sMLACF) algorithm.
  • To provide an alternative iterative method for attenuation correction in time-of-flight PET using emission data only.
  • To analyze the mathematical properties and performance of the sMLACF algorithm.

Main Methods:

  • The sMLACF algorithm was derived using the generalized expectation-maximization principle.
  • It involves an iterative, simultaneous update of activity (λ) and attenuation factors (a).
  • Constraints were incorporated to ensure the boundedness of reconstructed activities.

Main Results:

  • The sMLACF algorithm successfully estimates activity and attenuation factors from emission data alone.
  • Mathematical properties, including monotonic likelihood increase and asymptotic regularity, were proven for sMLACF.
  • Comparisons with the MLACF algorithm showed very similar quantitative results on two datasets.

Conclusions:

  • sMLACF is a viable alternative for attenuation correction in time-of-flight PET, utilizing only emission data.
  • While effective, the sMLACF algorithm demonstrates slower convergence compared to MLACF.
  • The algorithm's mathematical foundation and proven properties support its application in quantitative PET imaging.