Literature DB >> 33070006

Commissioning and clinical implementation of Mobius3D and MobiusFX: Experience on multiple linear accelerators.

Jihun Kim1, Min Cheol Han1, Kwangwoo Park1, Kyung Hwan Chang1, Dong Wook Kim1, Chae-Seon Hong2, Jin Sung Kim3.   

Abstract

PURPOSE: To provide practical guidelines for Mobius3D commissioning based on experiences of commissioning/clinical implementation of Mobius3D and MobiusFX as patient-specific quality assurance tools on multiple linear accelerators.
METHODS: The vendor-suggested Mobius3D commissioning procedures, including beam model adjustment and dosimetric leaf gap (DLG) optimization, were performed for 6 MV X-ray beams of six Elekta linear accelerators. For the beam model adjustment, beam data, such as the percentage depth dose, off-axis ratio (OAR), and output factor (OF), were measured using a water phantom and compared to the vendor-provided reference values. DLG optimization was performed to determine an optimal DLG correction factor to minimize the mean difference between Mobius3D-calculated and measured doses for multiple volumetric modulated arc therapy (VMAT) plans. Small-field VMAT plans, in which Mobius3D has dose calculate uncertainties, were initially included in the DLG optimization, but excluded later.
RESULTS: The measured beam data were consistent across the six linear accelerators. Relatively large differences between the reference and measured values were observed for the OAR at large off-axis distances (>5 cm) and for the OF for small fields (<3 × 3 cm2). The optimal DLG correction factor was 0.6 ± 0.3 (range: 0.3-1.0) with small-field plans and 0.2 ± 0.2 (0.0-0.5) without them.
CONCLUSIONS: A reasonable agreement was found between the vendor-provided reference and measured beam models. DLG optimization results were dependent on the selection of the VMAT plans, requiring careful attention to the known dose calculation uncertainties of Mobius3D when determining a DLG correction factor.
Copyright © 2020. Published by Elsevier Ltd.

Entities:  

Keywords:  Clinical implementation; Commissioning; Mobius3D; Patient-specific quality assurance; Volumetric-modulated arc therapy

Mesh:

Year:  2020        PMID: 33070006     DOI: 10.1016/j.ejmp.2020.10.004

Source DB:  PubMed          Journal:  Phys Med        ISSN: 1120-1797            Impact factor:   2.685


  2 in total

1.  Deep Learning for Patient-Specific Quality Assurance: Predicting Gamma Passing Rates for IMRT Based on Delivery Fluence Informed by log Files.

Authors:  Ying Huang; Yifei Pi; Kui Ma; Xiaojuan Miao; Sichao Fu; Zhen Zhu; Yifan Cheng; Zhepei Zhang; Hua Chen; Hao Wang; Hengle Gu; Yan Shao; Yanhua Duan; Aihui Feng; Weihai Zhuo; Zhiyong Xu
Journal:  Technol Cancer Res Treat       Date:  2022 Jan-Dec

2.  Assessment of log-based fingerprinting system of Mobius3D with Elekta linear accelerators.

Authors:  Yu-Yun Noh; Jihun Kim; Jin Sung Kim; Han-Back Shin; Min Cheol Han; Tae Suk Suh
Journal:  J Appl Clin Med Phys       Date:  2021-11-27       Impact factor: 2.102

  2 in total

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