title: Segmentation and leaf sequencing for intensity modulated arc therapy.
abstract: A common method in generating intensity modulated radiation therapy (IMRT) plans consists of a three step process: an optimized fluence intensity map (IM) for each beam is generated via inverse planning, this IM is then segmented into discrete levels, and finally, the segmented map is translated into a set of MLC apertures via a leaf sequencing algorithm. To date, limited work has been done on this approach as it pertains to intensity modulated arc therapy (IMAT), specifically in regards to the latter two steps. There are two determining factors that separate IMAT segmentation and leaf sequencing from their IMRT equivalents: (1) the intrinsic 3D nature of the intensity maps (standard 2D maps plus the angular component), and (2) that the dynamic multileaf collimator (MLC) constraints be met using a minimum number of arcs. In this work, we illustrate a technique to create an IMAT plan that replicates Tomotherapy deliveries by applying IMAT specific segmentation and leaf-sequencing algorithms to Tomotherapy output sinograms. We propose and compare two alternative segmentation techniques, a clustering method, and a bottom-up segmentation method (BUS). We also introduce a novel IMAT leaf-sequencing algorithm that explicitly takes leaf movement constraints into consideration. These algorithms were tested with 51 angular projections of the output leaf-open sinograms generated on the Hi-ART II treatment planning system (Tomotherapy Inc.). We present two geometric phantoms and 2 clinical scenarios as sample test cases. In each case 12 IMAT plans were created, ranging from 2 to 7 intensity levels. Half were generated using the BUS segmentation and half with the clustering method. We report on the number of arcs produced as well as differences between Tomotherapy output sinograms and segmented IMAT intensity maps. For each case one plan for each segmentation method is chosen for full Monte Carlo dose calculation (NumeriX LLC) and dose volume histograms (DVH) are calculated. In all cases, the BUS method outperformed the clustering, method. We recommend using the BUS algorithm and discuss potential improvements to the clustering algorithms.
authors.affiliation: Department of Physics, London Regional Cancer Program, London, Canada.
authors.forename: Adam
authors.initials: A
authors.lastname: Gladwish
authors.forename: Mike
authors.initials: M
authors.lastname: Oliver
authors.forename: Jeff
authors.initials: J
authors.lastname: Craig
authors.forename: Jeff
authors.initials: J
authors.lastname: Chen
authors.forename: Glenn
authors.initials: G
authors.lastname: Bauman
authors.forename: Barbara
authors.initials: B
authors.lastname: Fisher
authors.forename: Eugene
authors.initials: E
authors.lastname: Wong
mesh_terms: D006801:Humans; D008297:Male; D009369:Neoplasms / Q000532:radiotherapy*; D019047:Phantoms, Imaging; D011467:Prostate / Q000000981:diagnostic imaging; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D012007:Rectum / Q000000981:diagnostic imaging; D014057:Tomography, X-Ray Computed; D001743:Urinary Bladder / Q000000981:diagnostic imaging