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Functional segmentation of dynamic PET studies: Open source implementation and validation of a leader-follower-based

José María Mateos-Pérez1, María Luisa Soto-Montenegro2, Santiago Peña-Zalbidea3

  • 1Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain; Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain; Montreal Neurological Institute, McGill University, Montreal, Québec, Canada.

Computers in Biology and Medicine
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Summary

A new dynamic PET segmentation algorithm groups pixels by time-activity curve similarity. This novel method accurately replicates manual segmentation results in rodent studies, offering a reliable alternative for dynamic PET analysis.

Keywords:
ClusteringDynamic PETFunctional segmentationKinetic modelingOpen source

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Area of Science:

  • Nuclear Medicine
  • Radiochemistry
  • Biomedical Imaging

Background:

  • Dynamic Positron Emission Tomography (PET) studies are crucial for analyzing tracer kinetics in vivo.
  • Accurate segmentation of regions of interest is essential for quantitative analysis in dynamic PET imaging.
  • Current manual segmentation methods can be time-consuming and operator-dependent.

Purpose of the Study:

  • To introduce and validate a novel, automated segmentation algorithm for dynamic PET studies.
  • To compare the performance of the new algorithm against manual segmentation in a small animal model.
  • To assess the reliability of the algorithm for extracting regional activities and calculating the volume of distribution.

Main Methods:

  • A novel segmentation algorithm was developed, grouping pixels based on the similarity of their time-activity curves (TACs).
  • The algorithm was implemented within the jClustering framework and applied to dynamic PET-CT data from mice xenografted with human tumor cells.
  • Sixteen mice were imaged using three different Gallium-68 (68Ga)-DOTA-peptides (DOTANOC, DOTATATE, DOTATOC). Regional activities and tumor volume of distribution (using the Logan linear method) were computed and compared to manual delineations.

Main Results:

  • The novel segmentation algorithm successfully segmented all dynamic PET studies.
  • No significant differences were observed in tracer analysis between the automated algorithm and manual segmentation.
  • Significant differences in tumor uptake were identified between DOTANOC and the other two tracers (DOTATATE, DOTATOC), consistent with manual segmentation findings.

Conclusions:

  • The presented open-source segmentation algorithm is a reliable and effective tool for dynamic PET studies in small animals.
  • The algorithm accurately replicates results obtained through manual segmentation, serving as a viable automated alternative.
  • This method has the potential to be applied to other dynamic imaging segmentation tasks, improving efficiency and reproducibility.