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Intensity modulated arc deliveries approximated by a large number of fixed gantry position sliding window dynamic multileaf collimator fields
Author(s) -
MacKenzie Marc A.,
Robinson Donald M.
Publication year - 2002
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.1508110
Subject(s) - multileaf collimator , tomotherapy , intensity modulation , sliding window protocol , intensity (physics) , collimator , imaging phantom , arc (geometry) , position (finance) , dosimetry , projection (relational algebra) , mathematics , linear particle accelerator , computer science , optics , algorithm , physics , window (computing) , nuclear medicine , beam (structure) , radiation therapy , medicine , surgery , geometry , finance , phase modulation , phase noise , economics , operating system
The intensity modulated arc has been proposed as an alternative to tomotherapy. Treatment planing systems more typically model the conventional step and shoot or sliding window dynamic multileaf collimator (DMLC) deliveries, and may not support intensity modulated arc therapy (IMAT). As well, another potential drawback to this technique is that increasing the number of intensity levels required to achieve certain dose distributions necessitates increasing the number of gantry passes, as may occur if the desired dose distribution is complex (e.g., concave or bifurcated), potentially increasing the overall treatment time. A technique is presented here for the delivery of tomotherapy like dose distributions in a single gantry pass by the use of a large number of fields modulated by a sliding window DMLC technique from fixed equally spaced gantry positions. This serves as a good approximation to either IMAT or tomotherapy deliveries. The planning of these fields is achieved using iterative filtered back projection. Measured results of deliveries of varying degrees of complexity on a homogeneous phantom are compared to desired distributions.