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Technical Note: Unified imaging and robotic couch quality assurance
Author(s) -
Cook Molly C.,
Roper Justin,
Elder Eric S.,
Schreibmann Eduard
Publication year - 2016
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4960369
Subject(s) - isocenter , imaging phantom , quality assurance , software , computer science , medical imaging , image quality , orientation (vector space) , computer vision , artificial intelligence , medical physics , nuclear medicine , engineering , medicine , mathematics , operations management , external quality assessment , geometry , image (mathematics) , programming language
Purpose: To introduce a simplified quality assurance (QA) procedure that integrates tests for the linac's imaging components and the robotic couch. Current QA procedures for evaluating the alignment of the imaging system and linac require careful positioning of a phantom at isocenter before image acquisition and analysis. A complementary procedure for the robotic couch requires an initial displacement of the phantom and then evaluates the accuracy of repositioning the phantom at isocenter. We propose a two‐in‐one procedure that introduces a custom software module and incorporates both checks into one motion for increased efficiency. Methods: The phantom was manually set with random translational and rotational shifts, imaged with the in‐room imaging system, and then registered to the isocenter using a custom software module. The software measured positioning accuracy by comparing the location of the repositioned phantom with a CAD model of the phantom at isocenter, which is physically verified using the MV port graticule. Repeatability of the custom software was tested by an assessment of internal marker location extraction on a series of scans taken over differing kV and CBCT acquisition parameters. Results: The proposed method was able to correctly position the phantom at isocenter within acceptable 1 mm and 1° SRS tolerances, verified by both physical inspection and the custom software. Residual errors for mechanical accuracy were 0.26 mm vertically, 0.21 mm longitudinally, 0.55 mm laterally, 0.21° in pitch, 0.1° in roll, and 0.67° in yaw. The software module was shown to be robust across various scan acquisition parameters, detecting markers within 0.15 mm translationally in kV acquisitions and within 0.5 mm translationally and 0.3° rotationally across CBCT acquisitions with significant variations in voxel size. Agreement with vendor registration methods was well within 0.5 mm; differences were not statistically significant. Conclusions: As compared to the current two‐step approach, the proposed QA procedure streamlines the workflow, accounts for rotational errors in imaging alignment, and simulates a broad range of variations in setup errors seen in clinical practice.