An empirical method for automatic determination of maximum number of segments in DMPO-based IMRT for Head and Neck cases

• Published in: Reports of practical oncology and radiotherapy 2016-11, Vol.21 (6), p.571-578
• Main Authors: Ranganathan, Vaitheeswaran, Maria Das, K. Joseph
• Format: Article
• Language: English
• Subjects: Anatomy-guided segments; Aperture-based IMRT; DMPO; Hematology, Oncology and Palliative Medicine; IMRT; Number of segments; Original; Radiology
• ISSN: 1507-1367; 2083-4640
• DOI: 10.1016/j.rpor.2016.09.004

Abstract Aim An empirical scheme called “anatomy-guided segment counting (AGSC)” is proposed for automatic selection of maximum number of segments (NOS) for direct machine parameter optimization (DMPO). Background Direct machine parameter optimization (DMPO) requires the user to define the maximum number of segments (NOS) in order to proceed with an optimization process. Till date there is no established approach to arrive at an optimal and case-specific maximum NOS in DMPO, and this step is largely left to the planner's experience. Materials and methods The AGSC scheme basically uses the Beam's-eye views (BEVs) and other planning parameters to decide on appropriate number of segments for the beam. The proposed algorithm was tested in eight H&N cases. We used Auto Plan feature available in Pinnacle3 (version 9.10.0) for driving the DMPO optimization. Results There is about 13% reduction in the composite objective value in AGSC plans as compared to the plans employing 6 NOS per beam and 10% increase in the composite objective value in AGSC plans as compared to the plans employing 8 NOS per beam. On the delivery efficiency front, there is about 10% increase in NOS in AGSC plans as compared to the plans employing 6 NOS per beam specification. Similarly, there is about 19% reduction in NOS in AGSC plans as compared to the plans employing 8 NOS per beam specification. Conclusion The study demonstrates that the AGSC method allows specifying appropriate number of segments into the DMPO module accounting for the complexity of a given case.