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Updated: Mar 15, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
Published on: August 29, 2025
Technical Note: Unified imaging and robotic couch quality assurance.
Molly C Cook1, Justin Roper2, Eric S Elder2
1Medical Physics Program, Georgia Institute of Technology, 770 State Street, Atlanta, Georgia 30332.
A new quality assurance (QA) procedure integrates linac imaging and robotic couch tests. This streamlined process ensures accurate patient positioning for stereotactic radiosurgery (SRS) with enhanced efficiency.
Area of Science:
- Medical Physics
- Radiotherapy Technology
- Quality Assurance in Radiation Oncology
Background:
- Current quality assurance (QA) for linear accelerator (linac) imaging and robotic couches involves separate, time-consuming procedures.
- Evaluating imaging system alignment requires precise phantom positioning at isocenter.
- Robotic couch QA necessitates initial phantom displacement and subsequent repositioning accuracy checks.
Purpose of the Study:
- To introduce a simplified, integrated quality assurance (QA) procedure for linac imaging components and robotic couch.
- To enhance efficiency by combining imaging and couch QA into a single workflow.
- To develop a custom software module for streamlined QA testing.
Main Methods:
- A custom software module was developed to register a phantom with known shifts to the isocenter.
- Positioning accuracy was measured by comparing the repositioned phantom's location to a CAD model.
- Physical verification was performed using the MV port graticule.
- Software robustness was tested across various kV and CBCT acquisition parameters.
Main Results:
- The integrated QA procedure achieved phantom positioning within 1 mm and 1° SRS tolerances.
- Residual mechanical accuracy errors were minimal (e.g., 0.26 mm vertically, 0.21° pitch).
- The software demonstrated robustness, accurately detecting markers across diverse imaging parameters and agreeing with vendor methods.
Conclusions:
- The proposed QA procedure streamlines workflow compared to traditional two-step methods.
- It effectively accounts for rotational errors in imaging alignment.
- The method simulates a wide range of clinical setup error variations, improving QA accuracy.
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