Technical Note: A fast online adaptive replanning method for VMAT using flattening filter free beams. Issue 6 (10th May 2016)

• Record Type: Journal Article
• Authors: Ates, Ozgur; Ahunbay, Ergun E.; Moreau, Michel; Li, X. Allen
• Is Part Of: Medical physics. Volume 43:Issue 6(2016)Part 1
• Page Start: 2756
• Page End: 2764
• Publication Date: 2016-05-10
• Subjects: biomedical MRI -- cancer -- collimators -- computerised tomography -- lung -- optimisation -- radiation therapy; Dose‐volume analysis -- Collimators -- Cancer -- Computed tomography -- Clinical applications; Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging -- Computerised tomographs -- Radiation therapy -- Biological material, e.g. blood, urine; Haemocytometers -- Using diaphragms, collimators; segment aperture morphing -- adaptive radiotherapy -- online replanning -- interfractional variations -- flattening filter free beam; Self assembly -- Cancer -- Medical imaging -- Magnetic resonance imaging -- Image guided radiation therapy -- Intensity modulated radiation therapy -- Multileaf collimators -- Lungs -- Dosimetry -- Computed tomography; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4948676
• Languages: English
• ISSNs: 0094-2405

Abstract

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.