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Title: MO-FG-CAMPUS-TeP1-03: Pre-Treatment Surface Imaging Based Collision Detection

Abstract

Purpose: Modern radiotherapy increasingly employs large immobilization devices, gantry attachments, and couch rotations for treatments. All of which raise the risk of collisions between the patient and the gantry / couch. Collision detection is often achieved by manually checking each couch position in the treatment room and sometimes results in extraneous imaging if collisions are detected after image based setup has begun. In the interest of improving efficiency and avoiding extra imaging, we explore the use of a surface imaging based collision detection model. Methods: Surfaces acquired from AlignRT (VisionRT, London, UK) were transferred in wavefront format to a custom Matlab (Mathworks, Natick, MA) software package (CCHECK). Computed tomography (CT) scans acquired at the same time were sent to CCHECK in DICOM format. In CCHECK, binary maps of the surfaces were created and overlaid on the CT images based on the fixed relationship of the AlignRT and CT coordinate systems. Isocenters were added through a graphical user interface (GUI). CCHECK then compares the inputted surfaces to a model of the linear accelerator (linac) to check for collisions at defined gantry and couch positions. Note, CCHECK may be used with or without a CT. Results: The nominal surface image field ofmore » view is 650 mm × 900 mm, with variance based on patient position and size. The accuracy of collision detections is primarily based on the linac model and the surface mapping process. The current linac model and mapping process yield detection accuracies on the order of 5 mm, assuming no change in patient posture between surface acquisition and treatment. Conclusions: CCHECK provides a non-ionizing method to check for collisions without the patient in the treatment room. Collision detection accuracy may be improved with more robust linac modeling. Additional gantry attachments (e.g. conical collimators) can be easily added to the model.« less

Authors:
; ; ; ; ;  [1]
  1. Cone Health Cancer Center, Greensboro, NC (United States)
Publication Date:
OSTI Identifier:
22653894
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 43; Journal Issue: 6; Other Information: (c) 2016 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 61 RADIATION PROTECTION AND DOSIMETRY; ACCURACY; BIOMEDICAL RADIOGRAPHY; CAT SCANNING; COLLISIONS; COMPUTER CODES; IMAGES; LINEAR ACCELERATORS; PATIENTS; PRODUCTIVITY; SIMULATION

Citation Formats

Wiant, D, Maurer, J, Liu, H, Hayes, T, Shang, Q, and Sintay, B. MO-FG-CAMPUS-TeP1-03: Pre-Treatment Surface Imaging Based Collision Detection. United States: N. p., 2016. Web. doi:10.1118/1.4957345.
Wiant, D, Maurer, J, Liu, H, Hayes, T, Shang, Q, & Sintay, B. MO-FG-CAMPUS-TeP1-03: Pre-Treatment Surface Imaging Based Collision Detection. United States. doi:10.1118/1.4957345.
Wiant, D, Maurer, J, Liu, H, Hayes, T, Shang, Q, and Sintay, B. 2016. "MO-FG-CAMPUS-TeP1-03: Pre-Treatment Surface Imaging Based Collision Detection". United States. doi:10.1118/1.4957345.
@article{osti_22653894,
title = {MO-FG-CAMPUS-TeP1-03: Pre-Treatment Surface Imaging Based Collision Detection},
author = {Wiant, D and Maurer, J and Liu, H and Hayes, T and Shang, Q and Sintay, B},
abstractNote = {Purpose: Modern radiotherapy increasingly employs large immobilization devices, gantry attachments, and couch rotations for treatments. All of which raise the risk of collisions between the patient and the gantry / couch. Collision detection is often achieved by manually checking each couch position in the treatment room and sometimes results in extraneous imaging if collisions are detected after image based setup has begun. In the interest of improving efficiency and avoiding extra imaging, we explore the use of a surface imaging based collision detection model. Methods: Surfaces acquired from AlignRT (VisionRT, London, UK) were transferred in wavefront format to a custom Matlab (Mathworks, Natick, MA) software package (CCHECK). Computed tomography (CT) scans acquired at the same time were sent to CCHECK in DICOM format. In CCHECK, binary maps of the surfaces were created and overlaid on the CT images based on the fixed relationship of the AlignRT and CT coordinate systems. Isocenters were added through a graphical user interface (GUI). CCHECK then compares the inputted surfaces to a model of the linear accelerator (linac) to check for collisions at defined gantry and couch positions. Note, CCHECK may be used with or without a CT. Results: The nominal surface image field of view is 650 mm × 900 mm, with variance based on patient position and size. The accuracy of collision detections is primarily based on the linac model and the surface mapping process. The current linac model and mapping process yield detection accuracies on the order of 5 mm, assuming no change in patient posture between surface acquisition and treatment. Conclusions: CCHECK provides a non-ionizing method to check for collisions without the patient in the treatment room. Collision detection accuracy may be improved with more robust linac modeling. Additional gantry attachments (e.g. conical collimators) can be easily added to the model.},
doi = {10.1118/1.4957345},
journal = {Medical Physics},
number = 6,
volume = 43,
place = {United States},
year = 2016,
month = 6
}
  • Purpose: To demonstrate an efficient and clinically relevant patient specific QA method by reconstructing 3D patient dose from 2D EPID images for IMRT plans. Also to determine the usefulness of 2D QA metrics when assessing 3D patient dose deviations. Methods: Using the method developed by King et al (Med Phys 39(5),2839–2847), EPID images of IMRT fields were acquired in air and converted to dose at 10 cm depth (SAD setup) in a flat virtual water phantom. Each EPID measured dose map was then divided by the corresponding treatment planning system (TPS) dose map calculated with an identical setup, to derivemore » a 2D “error matrix”. For each field, the error matrix was used to adjust the doses along the respective ray lines in the original patient 3D dose. All field doses were combined to derive a reconstructed 3D patient dose for quantitative analysis. A software tool was developed to efficiently implement the entire process and was tested with a variety of IMRT plans for 2D (virtual flat phantom) and 3D (in-patient) QA analysis. Results: The method was tested on 60 IMRT plans. The mean (± standard deviation) 2D gamma (2%,2mm) pass rate (2D-GPR) was 97.4±3.0% and the mean 2D gamma index (2D-GI) was 0.35±0.06. The 3D PTV mean dose deviation was 1.8±0.8%. The analysis showed very weak correlations between both the 2D-GPR and 2D-GI when compared with PTV mean dose deviations (R2=0.3561 and 0.3632 respectively). Conclusion: Our method efficiently calculates 3D patient dose from 2D EPID images, utilising all of the advantages of an EPID-based dosimetry system. In this study, the 2D QA metrics did not predict the 3D patient dose deviation. This tool allows reporting of the 3D volumetric dose parameters thus providing more clinically relevant patient specific QA.« less
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