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Positron Emission Tomography (PET) is a medical imaging technique that provides crucial insights into the body's physiological functions at a molecular level. It is an indispensable resource for diagnosing, staging, and monitoring various illnesses, notably cancer, neurological disorders, and cardiovascular conditions.
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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.
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A Basic Positron Emission Tomography System Constructed to Locate a Radioactive Source in a Bi-dimensional Space
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Feasibility study of image reconstruction for a forceps-type positron emission counter: a simulation-based algorithm

Ryotaro Ohashi1,2, Sodai Takyu1, Shigeki Ito3

  • 1National Institutes for Quantum Science and Technology, 4-9-1 Anagawa, Inage, Chiba, Japan.

Physics in Medicine and Biology
|June 26, 2026
PubMed
Summary

This study shows that a 16-line of response (LOR) positron emission counter (PEC) can reconstruct images for guiding surgery and quantifying 18F-fluorodeoxyglucose uptake in lymph nodes. Optimal algorithms enable accurate localization and quantification for intraoperative use.

Keywords:
Monte Carlo simulationcoincidence detectionintraoperative imagingiterative image reconstructionpenalized likelihoodradioguided surgery

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Area of Science:

  • Medical Imaging
  • Nuclear Medicine
  • Surgical Technology

Background:

  • A forceps-type positron emission counter (PEC) was developed for intraoperative quantification of 18F-fluorodeoxyglucose (FDG) uptake in lymph nodes.
  • The current PEC design uses 16 lines of response (LORs), posing challenges for image reconstruction and accurate quantification.

Purpose of the Study:

  • To investigate the feasibility of image reconstruction from 16 LORs for a forceps-type PEC.
  • To enable repositioning guidance and sensitivity-corrected quantification of lymph node uptake during surgery.

Main Methods:

  • Compared seven reconstruction algorithms using Geant4 Monte Carlo simulations and phantom experiments.
  • Evaluated algorithms based on data-fidelity models (least-squares, Poisson likelihood) and regularization priors (none, L2 Tikhonov, total variation).
  • Assessed reconstruction quality, count-rate dependence, localization accuracy, and dual-source separation.

Main Results:

  • Regularization significantly impacts reconstruction quality; the optimal algorithm depends on the specific task.
  • MAP-EM-TV achieved the highest structural similarity, while PLS-TV offered the best quantitative accuracy (21.5 pp CV reduction) with a 1.27 mm localization error.
  • Image quality is near-optimal at ~3,000 counts, retaining spatial information down to ~100 counts, but degrades below a 12:1 source-to-background ratio.

Conclusions:

  • The 16-LOR PEC can provide spatial information for repositioning guidance and sensitivity-corrected quantification in intraoperative settings.
  • Demonstrated the potential for enhanced surgical guidance and accurate metabolic assessment of lymph nodes.
  • Further validation with a physical prototype is essential to confirm these findings.