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A 4D biomechanical lung phantom for joint segmentation/registration evaluation.

Daniel Markel1, Ives Levesque, Joe Larkin

  • 1Medical Physics Unit, University of McGill, Montreal, QC, Canada.

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Summary

A new quality assurance platform evaluates simultaneous segmentation and registration algorithms for adaptive radiotherapy tumor tracking. This multi-modality phantom provides accurate evaluation, improving clinical applications.

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Area of Science:

  • Medical Imaging
  • Computational Biology
  • Radiotherapy

Background:

  • Simultaneous segmentation and registration algorithms are crucial for tumor tracking in adaptive radiotherapy.
  • Existing evaluation methods for these algorithms are limited.
  • Accurate tumor tracking requires robust multi-modality imaging and validated algorithms.

Purpose of the Study:

  • To develop and validate a novel quality assurance (QA) platform for evaluating simultaneous segmentation and registration algorithms.
  • To assess the performance of different registration and segmentation algorithms using a multi-modality phantom.
  • To provide a reliable method for tracking tumors in complex scenarios like adaptive radiotherapy.

Main Methods:

  • A multi-modality QA platform was created using a preserved porcine lung compatible with positron emission tomography (PET), computed tomography (CT), and magnetic resonance imaging (MRI).
  • A computer-controlled respirator simulated human breathing patterns with high accuracy (2-2.2% mean error).
  • Ground truth for registration was established using an in-house bifurcation tracking pipeline, and for segmentation, synthetic lesions were employed.

Main Results:

  • The respirator accurately replicated breathing traces.
  • Bifurcation tracking error was sub-voxel for human CT data and approximately 1.5 voxel widths for porcine lungs.
  • Evaluated registration algorithms (Diffeomorphic demons, fast-symmetric forces demons, MiMVista) showed varying mean geometric errors, with the bifurcation tracking pipeline providing a robust evaluation metric.
  • Segmentation algorithms (Chan Vese, Hybrid, multi-valued level sets) were also assessed.

Conclusions:

  • The developed porcine lung phantom is sufficient for accurate evaluation of simultaneous segmentation and registration algorithms.
  • The QA platform enables reliable assessment of algorithms used in adaptive radiotherapy.
  • Automated landmark generation can reduce manual effort and potential inaccuracies in algorithm evaluation.