Automation of routine elements for spot‐scanning proton patient‐specific quality assurance. Issue 1 (20th November 2018)

• Record Type: Journal Article
• Authors: Hernandez Morales, Danairis; Shan, Jie; Liu, Wei; Augustine, Kurt E.; Bues, Martin; Davis, Michael J.; Fatyga, Mirek; Johnson, Jedediah E.; Mundy, Daniel W.; Shen, Jiajian; Younkin, James E.; Stoker, Joshua B.
• Is Part Of: Medical physics. Volume 46:Issue 1(2019)
• Page Start: 5
• Page End: 14
• Publication Date: 2018-11-20
• Subjects: IMPT -- patient‐specific quality assurance -- proton therapy -- PSQA; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.13246
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: At our institution, all proton patient plans undergo patient‐specific quality assurance (PSQA) prior to treatment delivery. For intensity‐modulated proton beam therapy, quality assurance is complex and time consuming, and it may involve multiple measurements per field. We reviewed our PSQA workflow and identified the steps that could be automated and developed solutions to improve efficiency. Methods: We used the treatment planning system's (TPS) capability to support C# scripts to develop an Eclipse scripting application programming interface (ESAPI) script and automate the preparation of the verification phantom plan for 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 the analysis of the measurement data, a Python script was developed to automatically perform a 2D–3D γ ‐index analysis comparing measurements in the plane of a two‐dimensional detector array with TPS predictions in a water phantom for each acquired measurement. Results: Device connection via LAN granted immediate access to the plan and measurement information for downstream analysis using an online software suite. Automated scripts applied to verification plans reduced time from preparation steps by at least 50%; time reduction from automating γ ‐index analysis was even more pronounced, dropping by a factor of 10. On average, we observed an overall time savings of 55% in completion of the PSQA per patient plan. Conclusions: The automation of the routine tasks in the PSQA workflow significantly reduced the time required per patient, reduced user fatigue, and frees up system users from routine and repetitive workflow steps allowing increased focus on evaluating key quality metrics.