(Sunday, 7/12/2020) [Eastern Time (GMT-4)]
Room: AAPM ePoster Library
Purpose: CBCT monthly image quality assurance (QA) is prescribed by TG-142, which recommends testing multiple image quality metrics. TG-142 suggests a tolerance of “baseline” for all metrics with the exception of geometric distortion. There is limited published data investigating how to establish tolerances across multiple machines of the same model or across multiple CBCT techniques used clinically. The purpose of this work was to determine if machine-independent and/or technique-independent baselines could be generated. Additionally this work quantified the month-to-month variability in the baseline to generate tolerances and explored the impact of multiple CBCT acquisitions on monthly QA results.
Methods: CBCTs were completed on five Truebeam linear accelerators over the course of fifteen months. CBCTs were completed using a Catphan 604 phantom and analyzed for fifteen image quality metrics using the SunCheck Machine software. Four CBCT techniques were investigated: Spotlight, Head, Pelvis, and Thorax. Over 200 CBCTs were analyzed to (1) determine if image quality metrics differed between machine and technique (two-way ANOVA test), (2) to quantify the effect of re-calibrating a CBCT mode on image quality baselines (T-test), and (3) to analyze the impact on image quality of taking multiple CBCTs back-to-back.
Results: Image quality metrics varied with both machine and technique (P < 0.05). In addition, re-calibrating a CBCT mode caused statistically significant changes to some image quality metrics (P<0.05). When CBCTS were taken in rapid succession, as may be done during monthly QA, a cylindrical artifact appeared that caused the measured uniformity to be out of manufacturer's tolerance. The artifact disappeared when the number of CBCTs taken in succession was reduced.
Conclusion: This work used institutional data to provide guidelines on setting baselines and tolerances for a quantitative CBCT image quality program, while also taking into account scenarios that alter or effect the measurement of image quality metrics.
Keywords
Cone-beam CT, Quality Assurance
Taxonomy
TH- External Beam- Photons: onboard imaging (development and applications)
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