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Development of Workflow and Tools for Collision Detection Using a Handheld 3D Mapping Camera

L Padilla1*, m meltsner2 , (1) Virginia Commonwealth University, Richmond, VA , (2) Philips Radiation Oncology Systems, Fitchburg, WI


(Sunday, 7/29/2018) 5:05 PM - 6:00 PM

Room: Karl Dean Ballroom A1

Purpose: To develop processes and automated tools for detecting and preventing collisions in external beam radiotherapy during the simulation and treatment planning stages using a handheld, 3D mapping camera.

Methods: A Varian (Palo Alto, CA) TrueBeam linac and couch were mapped as detailed mesh models using the Structure Sensor (Occipital, Inc.) 3D mapping device attached to an iPad. This device is a handheld scanner which requires no mounting or calibration. A fully articulated mannequin with non-metallic joints was placed supine with arms-up mimicking a lung/liver SBRT treatment including a Vac-Lok (Civco, Orange City, IA) bag and wing board. The mannequin was scanned with the 3D camera prior to obtaining the planning CT. All models were imported as meshes into the Philips (Fitchburg, WI) Pinnacle3 TPS using the MBS (Model Based Segmentation) package. The mannequin model was automatically fused/registered with the planning CT. The other models were registered based on the isocenter position. A custom spatial detection algorithm calculated collisions and clearance between the patient model and linac. The mannequin was set up physically in the treatment room where the clearance distance of the devices was compared to those calculated. Lastly, another workflow was explored whereby only the 3D mapping of the patient is imported. This is done prior to obtaining the planning CT, using an approximate isocenter, to investigate the potential of eliminating multiple re-scans by detecting collisions earlier in the simulation stage.

Results: The auto-registration between the planning CT and the 3D mapping showed good agreement. Accuracy of the clearance calculated was consistent with the physically measured to within 1 cm.

Conclusion: The 3D patient model supplements the limited FOV Sup-Inf and L-R that the CT scan may not provide. The presented workflows have the potential to eliminate collisions that may be found late in the planning process.

Funding Support, Disclosures, and Conflict of Interest: This project was funded by Philips Radiation Oncology Systems, Fitchburg, WI. The presenting author is an employee of Philips Radiation Oncology Systems.


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