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Image-derived input function derived from a supervised clustering algorithm: methodology and validation in a clinical

Chul Hyoung Lyoo1, Paolo Zanotti-Fregonara2, Sami S Zoghbi3

  • 1Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea.

Plos One
|March 4, 2014
PubMed
Summary

A new automated method using supervised clustering algorithm (SVCA) accurately derives the image-derived input function (IDIF) for [(11)C](R)-rolipram PET scans. This technique offers a reliable, less variable alternative to manual methods for major depressive disorder (MDD) research.

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Area of Science:

  • Nuclear Medicine
  • Neuroimaging
  • Radiochemistry

Background:

  • Manual image-derived input function (IDIF) extraction for [(11)C](R)-rolipram positron emission tomography (PET) is time-consuming and prone to operator variability.
  • Accurate IDIF is crucial for quantifying tracer kinetics, such as Logan-distribution volume (V T/f P), in neuroimaging studies.

Purpose of the Study:

  • To develop and validate a fully automated supervised clustering algorithm (SVCA) for deriving IDIF in [(11)C](R)-rolipram PET scans.
  • To compare the accuracy and reliability of automated cluster-IDIF with manual-IDIF and arterial input function.

Main Methods:

  • A supervised clustering algorithm (SVCA) was employed to automatically segment carotid arteries and surrounding tissues from MRI and PET data.
  • Cluster-IDIF was generated by creating template masks, applying SVCA for blood weighting, inverse normalization, and calculating time-activity curves (TACs).
  • Partial volume effects and radiometabolite corrections were performed using arterial data; Logan-distribution volume (V T/f P) was calculated and compared.

Main Results:

  • Cluster-IDIF derived V T/f P values were comparable to reference arterial data and manual-IDIF, with high accuracy (<5% error in 39/51 subjects).
  • Automated cluster-IDIF curves exhibited reduced noise and eliminated operator-related variability compared to manual-IDIF.
  • A significant ~20% decrease in [(11)C](R)-rolipram binding was observed in the major depressive disorder (MDD) group using cluster-IDIF.

Conclusions:

  • Automated cluster-IDIF using SVCA is a robust and reliable alternative to arterial input function for estimating Logan-V T/f P in [(11)C](R)-rolipram PET scans.
  • This automated technique streamlines IDIF extraction, reducing variability and enhancing efficiency for clinical and research applications.
  • The method shows potential for application with other radiotracers exhibiting similar kinetic properties.