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Updated: Jun 12, 2026

Analysis of 18FDG PET/CT Imaging as a Tool for Studying Mycobacterium tuberculosis Infection and Treatment in Non-human Primates
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ML segmentation strategies for object interference compensation in FDG-PET lesion quantification.

E De Bernardi1, F Fiorani Gallotta, C Gianoli

  • 1Bioengineering Department, Politecnico di Milano, Piazza Leonardo da Vinci, 32, 20133 Milano, Italy.

Methods of Information in Medicine
|May 22, 2010
PubMed
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This study enhances lesion quantification in oncological PET scans by improving the AWOSEM-region algorithm. The improved method accurately measures lesion activity and volume, even with nearby interfering structures, aiding lung cancer diagnosis.

Area of Science:

  • Medical Imaging
  • Nuclear Medicine
  • Radiological Physics

Background:

  • Positron Emission Tomography (PET) using FDG uptake is crucial for oncological imaging.
  • Partial volume effects significantly limit accurate quantification of lesion activity.
  • The AWOSEM-region algorithm was previously developed for improved lesion segmentation and quantification.

Purpose of the Study:

  • To enhance the AWOSEM-region algorithm for analyzing lesions near interfering hot objects in lung PET-CT studies.
  • To address spill-out and partial volume effects in cases of lesions adjacent to organs like the chest wall or heart, and for closely located lesions.

Main Methods:

  • Utilized CT for pre-segmentation of anatomical structures, with PET data driving lesion identification.
  • Employed resolution recovery techniques for initial lesion segmentation.

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  • Applied a watershed algorithm to differentiate adjacent lesions.
  • Incorporated spill-out subtraction from nearby hot organs to improve lesion enhancement for quantification.

    • Main Results:

    • Simulations demonstrated the ability to separate structures as small as 2.25 mm.
    • The algorithm showed robustness against initial segmentation volume overestimates (up to 130%) but not underestimates.
    • A clinical case of a small lung lesion near the chest wall showed good recovery of activity and volume.

    Conclusions:

    • The refined AWOSEM-region algorithm, with appropriate initialization and spill-out modeling, is effective for lung oncological PET studies.
    • This advancement improves the accuracy of lesion quantification in challenging proximity scenarios.