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Development and clinical implementation of eclipse scripting-based automated patient-specific collision avoidance

Thomas D Mann1,2, Nicolas P Ploquin1,2,3, William R Gill2

  • 1Department of Physics and Astronomy, University of Calgary, Calgary, AB, Canada.

Journal of Applied Clinical Medical Physics
|July 9, 2019
PubMed
Summary
This summary is machine-generated.

A new collision detection software for stereotactic radiosurgery (SRS) uses patient CT scans to prevent dangerous equipment collisions during treatment planning. This improves safety and efficiency in radiation therapy.

Keywords:
collision detectioneclipse scriptingnoncoplanar radiotherapypatient safety

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Area of Science:

  • Medical Physics
  • Radiation Oncology
  • Image-Guided Therapy

Background:

  • Stereotactic radiosurgery (SRS) increasingly uses noncoplanar gantry trajectories, demanding robust collision detection during treatment planning.
  • Current planning methods may lack efficient and accurate collision detection for complex SRS deliveries.
  • Integration with clinical workflow is crucial for adopting new planning tools.

Purpose of the Study:

  • To develop and clinically implement a patient-specific, computed tomography (CT) contour-based collision detection application for Linac-based SRS.
  • To ensure seamless integration into the existing clinical workflow using Eclipse Scripting.
  • To enhance safety and efficiency in SRS treatment planning.

Main Methods:

  • Developed a collision detection application using triangle mesh models of the gantry/couch and patient CT contours.
  • Employed Binary Tree Hierarchy detection methods for virtual treatment simulations.
  • Validated algorithm accuracy using calibration and anthropomorphic phantoms, assessing sensitivity with ROC analysis.

Main Results:

  • The application accurately predicted minimum distances between structures to within 3 cm.
  • A 1.5 cm safety margin achieved 100% sensitivity; 94.2% accuracy was obtained with a 5 cm margin.
  • Successfully tested on 88 noncoplanar SRS patients, detecting one collision with no false negatives; average testing time was under 3 minutes.

Conclusions:

  • A novel, CT contour-based collision detection application was successfully developed and implemented for clinical SRS.
  • Early collision detection during planning prevents treatment delays and optimizes resource use.
  • This tool enhances the safety and efficiency of noncoplanar SRS delivery.