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Technical Note: A fast online adaptive replanning method for VMAT using flattening filter free beams. Med Phys 2016 Jun;43(6):2756-2764

Date

06/10/2016

Pubmed ID

27277022

DOI

10.1118/1.4948676

Scopus ID

2-s2.0-84969900567 (requires institutional sign-in at Scopus site)   12 Citations

Abstract

PURPOSE: To develop a fast replanning algorithm based on segment aperture morphing (SAM) for online replanning of volumetric modulated arc therapy (VMAT) with flattening filter free (FFF) beams.

METHODS: A software tool was developed to interface with a VMAT research planning system, which enables the input and output of beam and machine parameters of VMAT plans. The SAM algorithm was used to modify multileaf collimator positions for each segment aperture based on the changes of the target from the planning (CT/MR) to daily image [CT/CBCT/magnetic resonance imaging (MRI)]. The leaf travel distance was controlled for large shifts to prevent the increase of VMAT delivery time. The SAM algorithm was tested for 11 patient cases including prostate, pancreatic, and lung cancers. For each daily image set, three types of VMAT plans, image-guided radiation therapy (IGRT) repositioning, SAM adaptive, and full-scope reoptimization plans, were generated and compared.

RESULTS: The SAM adaptive plans were found to have improved the plan quality in target and/or critical organs when compared to the IGRT repositioning plans and were comparable to the reoptimization plans based on the data of planning target volume (PTV)-V100 (volume covered by 100% of prescription dose). For the cases studied, the average PTV-V100 was 98.85% ± 1.13%, 97.61% ± 1.45%, and 92.84% ± 1.61% with FFF beams for the reoptimization, SAM adaptive, and repositioning plans, respectively. The execution of the SAM algorithm takes less than 10 s using 16-CPU (2.6 GHz dual core) hardware.

CONCLUSIONS: The SAM algorithm can generate adaptive VMAT plans using FFF beams with comparable plan qualities as those from the full-scope reoptimization plans based on daily CT/CBCT/MRI and can be used for online replanning to address interfractional variations.

Author List

Ates O, Ahunbay EE, Moreau M, Li XA

Author

Ergun Ahunbay PhD Professor in the Radiation Oncology department at Medical College of Wisconsin




MESH terms used to index this publication - Major topics in bold

Algorithms
Cone-Beam Computed Tomography
Humans
Lung Neoplasms
Magnetic Resonance Imaging
Male
Organs at Risk
Pancreatic Neoplasms
Prostatic Neoplasms
Radiotherapy Planning, Computer-Assisted
Radiotherapy, Intensity-Modulated
Software