Related Experiment Video
Updated: Sep 29, 2025
A High-content Imaging Workflow to Study Grb2 Signaling Complexes by Expression Cloning
Published on: October 30, 2012
Using eclipse scripting to fully automate in-vivo image analysis to improve treatment quality and safety
Ananta Raj Chalise1, Casey Bojechko1
1Department of Radiation Medicine and Applied Sciences, University of California San Diego, San Diego, California, USA.
Purpose:
An automated, in-vivo system to detect patient anatomy changes and machine output was developed using novel analysis of in-vivo electronic portal imaging device (EPID) images for every fraction of treatment on a Varian Halcyon. In-vivo approach identifies errors that go undetected by routine quality assurance (QA) to compliment daily machine performance check (MPC), with minimal physicist workload.
Methods:
Images for all fractions treated on a Halcyon were automatically downloaded and analyzed at the end of treatment day. For image analysis, compared to first fraction, the mean difference of high-dose region of interest is calculated. This metric has shown to predict changes in planning treatment volume (PTV) mean dose. Flags are raised for: (Type-A) treatment fraction whose mean difference exceeds 10%, to protect against large errors, and (Type-B) patients with three consecutive fractions with mean exceeding ±3%, to protect against systematic trends. If a threshold is exceeded, a physicist is e-mailed, a report for flagged patients, for investigation. To track machine output changes, for all patients treated on a day, the average and standard deviations are uploaded to a QA portal, along with the reviewed MPC, ensuring comprehensive QA for the Halcyon. To guide clinical implementation, a retrospective study from November 2017 till December 2020 was conducted, which grouped errors by treatment site. This framework has been used prospectively since January 2021.
Results:
From retrospective data of 1633 patients (35 759 fractions), no Type-A errors were found and only 45 patients (2.76%) had Type-B errors. These Type-B deviations were due to head-and-neck weight loss. For 6 months of prospective use (345 patients), 13 patients (3.7%) had Type-B errors and no Type-A errors.
Conclusions:
This automated system protects against errors that can occur in vivo to provide a more comprehensive QA. This fully automated framework can be implemented in other centers with a Halcyon, requiring a desktop computer and analysis scripts.
