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A particle filter based autocontouring algorithm for lung tumor tracking using dynamic magnetic resonance imaging.

Alexandra E Bourque1, Stéphane Bedwani2, Édith Filion2

  • 1Département de physique, Université de Montréal, Pavillon Roger-Gaudry (D-428), 2900 Boulevard Édouard-Montpetit, Montréal, Québec H3T 1J4, Canada and Département de radio-oncologie, Centre hospitalier de l'Université de Montréal (CHUM), 1560 rue Sherbrooke est, Montréal, Québec H2L 4M1, Canada.

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|September 3, 2016
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Summary

This study presents a novel particle filter algorithm for automatic lung tumor contouring on dynamic MR images during MR-linac treatments. The algorithm demonstrated good agreement with expert contours, showing its potential for precise cancer therapy.

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Area of Science:

  • Medical Imaging
  • Radiotherapy
  • Computational Biology

Background:

  • Accurate tumor delineation is critical for effective lung cancer radiotherapy.
  • Dynamic magnetic resonance (MR) imaging offers real-time visualization of lung tumors during treatment.
  • Current autocontouring methods often require extensive pre-treatment training data.

Purpose of the Study:

  • To introduce a novel autocontouring algorithm for lung tumors using particle filters.
  • To validate the algorithm on dynamic MR images in the context of MR-linac treatments.
  • To assess the algorithm's performance against expert delineations.

Main Methods:

  • A particle filter algorithm combined with Otsu's thresholding was developed for lung tumor contouring.
  • The algorithm was tested on dynamic MR images from four non-small cell lung cancer (NSCLC) patients.
  • Performance was evaluated using Dice similarity coefficient (DSC), precision, recall, Hausdorff distance, and centroid difference.

Main Results:

  • The autocontouring algorithm demonstrated continuous adaptability without pre-treatment training.
  • Computational time was efficient, with contouring adding a constant 14 ms.
  • Mean DSC ranged from 0.89-0.91, with mean recall up to 0.95 and mean centroid difference of 0.6-2.0 mm.

Conclusions:

  • This study provides a proof of concept for a new autocontouring algorithm for NSCLC patients.
  • The particle filter-based algorithm shows good agreement with expert contours on dynamic MR images.
  • The developed method holds promise for enhancing precision in MR-linac treatments.