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Session: Professional General ePoster Viewing [Return to Session]

An Automatic CT Quality Assurance Program

E Eastman*, D Zhang, Y Zhou, Cedars-Sinai Medical Center, Los Angeles, CA


PO-GePV-P-4 (Sunday, 7/25/2021)   [Eastern Time (GMT-4)]

Purpose: The purpose of this study was to develop a digital, self-regulating program to monitor CT quality assurance (QA) results. There are two main goals of the study. The first was to transition from physical to digital records. The second was to reduce the QA review time spent by medical physicists.

Methods: The program consists of two elements. The first element is a set of digital forms used to record daily, weekly, and monthly quality assurance results for all CT scanners. The daily test includes CT number, noise, and uniformity. The weekly test includes SMPTE pattern check. The monthly test includes visual inspections, laser alignment, and vendor-recommended quantitative tests such as spatial resolution, contrast scale, and slice thickness. These Excel forms are stored on a cloud drive and incorporate limits specified by vendors and the American College of Radiology (ACR). The second element is a Python-based program that searches the forms for out of tolerance results and blank entries, generates weekly summaries, and sends email alerts when they are found. Pandas library was used for all data manipulation, while Pywin32 library was used for sending out email alerts. This program automatically runs daily using Windows Task Scheduler on a desktop computer. The accuracy of the program was evaluated for 2 months.

Results: A defined group of users received an email alert whenever there were failures, warnings, or missing records. The results of the manual review all agreed with the program results. In addition, the program detected missing entries that the human review did not capture. For our enterprise health system with 20 CT scanners, it saves 100 minutes of a medical physicist’s time per week.

Conclusion: We successfully implemented an automated method for CT QA documentation and review. This method not only avoids human errors, but also can increase productivity.



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