Click here to


Are you sure ?

Yes, do it No, cancel

Automated Verification Plan Preparation and 2D-3D Gamma Analysis for Proton Patient-Specific Quality Assurance

D Hernandez Morales1*, J Shan2 , W Liu1 , K Augustine1 , M Bues1 , M Davis1 , M Fatyga1 , J Johnson3 , D Mundy3 , J Shen1 , J Younkin1 , J Stoker1 , (1) Mayo Clinic Arizona, Phoenix, AZ, (2) Arizona State University, Scottsdale, AZ, (3) Mayo Clinic, Rochester, MN


(Saturday, 3/30/2019)  

Room: Exhibit Hall

Purpose: Physician-approved treatment plans undergo patient-specific quality assurance (PSQA) prior to beginning of treatment. For pencil beam scanning proton therapy, quality assurance is complex and time consuming, involving multiple measurements per field. We evaluated the PSQA process to identify routine steps that could be automated for a comprehensive and efficient workflow.

Methods: We used the treatment planning system’s (TPS) capability to support C# scripts to develop an Eclipse Application Programming Interface (API) script to automate the preparation of the verification-phantom plan. The API script evaluated the gradient in the target volume of each verification field based on established criteria to identify adequate depth-dose profiles and depths for PSQA measurements. A local area network (LAN) connection between our measurement equipment and shared database was established to facilitate equipment control, measurement data transfer and storage. To improve measurement data analysis, a Python script was developed to automatically perform a 2D-3D γ-index analysis between the measurement plane and the corresponding TPS in-water volume for each acquired measurement. We evaluated a subset of patient plans representing the various disease sites treated at our clinic with the previous and automated methods to quantify changes in efficiency.

Results: The device connection via LAN granted immediate access to the plan and measurement information for analysis using an online software suite. Automated verification plan preparation reduced the task time by more than 50%, decreasing the time from 5-20 minutes per field to 1-3 minutes per field. The γ-index analysis time reduction is more pronounced, being reduced by an order of magnitude for all disease sites. With these automations we observed an average overall PSQA time savings of 57% per patient plan.

Conclusion: Automating routine PSQA workflow elements improves time efficiency, reduces user fatigue and focuses efforts on evaluation of key quality metrics.

Funding Support, Disclosures, and Conflict of Interest: Dr. Wei Liu and Jie Shan were supported by the National Cancer Institute (NCI) Career Development Award K25CA168984, Arizona Biomedical Research Investigator Award, the Lawrence W. and Marilyn W. Matteson Fund for Cancer Research, and the Kemper Marley Foundation.


Contact Email