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A Framework for Collision Detection and Safety Using RayStation and Align-RT

E Klein1 , M Schwer2 , R Munbodh3 , C Liu4 , Z Saleh5*, (1) Rhode Island Hospital / Warren Alpert Medical, Providence, RI, (2) Rhode Island Hospital / Warren Alpert Medical, Warwick, RI, (3) Alpert Medical School of Brown University/Rhode Island Hospital, Providence, RI, (4) Rhode Island Hospital, Providence, RI, (5) Rhode Island Hospital / University Of Rhode Island, Providence, RI


(Sunday, 7/14/2019) 1:00 PM - 2:00 PM

Room: Stars at Night Ballroom 1

Purpose: Current treatment planning systems lack the capability to detect a potential collision between the linear accelerator (LINAC) and patient. This is primarily caused by the lack of volumetric data obtained during the patient simulation. We propose a framework for collision detection of a patient and LINAC gantry in RayStation planning system utilizing surface imaging provided by Align-RT and volumetric CT data obtained at simulation.

Methods: An anthropomorphic phantom was scanned on a Philips Big Bore CT scanner. The Align-RT camera system, in the CT room, was used to capture the surface images of the phantom. Moreover, the surface images for a human volunteer were acquired with different arm positions and immobilization devices. The DICOM images of the phantom were imported into RayStation in order to generate treatment plans with different iso-centers and non-coplanar beams. Using the scripting capability in RayStation, the point cloud from the surface images will be converted into a DICOM RT-Structure and superimposed on the planning CT. Visual inspection using the “Room View� in RayStation was utilized to detect collisions.

Results: Phantom measurements showed that the 3D model in “Room View� accurately models the treatment couch but overestimates the clearance between patient and the gantry by 4cm. The surface images acquired with Align-RT provided additional anatomical information that was not captured during CT scan (mainly elbows). However, the surface images suffered from some artifacts such as missing surface areas and required further processing. Visual inspection using “Room View� identified collisions between the patient and gantry for treatment plans with iso-centers placed laterally to cause intentional collisions.

Conclusion: The proposed framework provides a mechanism to detect and avoid any potential collision during treatment planning process. However, this methodology cannot anticipate collisions that might result from setup deviations between CT simulation and treatment.


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