Literature DB >> 12094990

Incorporating multi-leaf collimator leaf sequencing into iterative IMRT optimization.

Jeffrey V Siebers1, Marc Lauterbach, Paul J Keall, Radhe Mohan.   

Abstract

Intensity modulated radiation therapy (IMRT) treatment planning typically considers beam optimization and beam delivery as separate tasks. Following optimization, a multi-leaf collimator (MLC) or other beam delivery device is used to generate fluence patterns for patient treatment delivery. Due to limitations and characteristics of the MLC, the deliverable intensity distributions often differ from those produced by the optimizer, leading to differences between the delivered and the optimized doses. Objective function parameters are then adjusted empirically, and the plan is reoptimized to achieve a desired deliverable dose distribution. The resulting plan, though usually acceptable, may not be the best achievable. A method has been developed to incorporate the MLC restrictions into the optimization process. Our in-house IMRT system has been modified to include the calculation of the deliverable intensity into the optimizer. In this process, prior to dose calculation, the MLC leaf sequencer is used to convert intensities to dynamic MLC sequences, from which the deliverable intensities are then determined. All other optimization steps remain the same. To evaluate the effectiveness of deliverable-based optimization, 17 patient cases have been studied. Compared with standard optimization plus conversion to deliverable beams, deliverable-based optimization results show improved isodose coverage and a reduced dose to critical structures. Deliverable-based optimization results are close to the original nondeliverable optimization results, suggesting that IMRT can overcome the MLC limitations by adjusting individual beamlets. The use of deliverable-based optimization may reduce the need for empirical adjustment of objective function parameters and reoptimization of a plan to achieve desired results.

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Year:  2002        PMID: 12094990     DOI: 10.1118/1.1477230

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  17 in total

1.  Coverage-based treatment planning: optimizing the IMRT PTV to meet a CTV coverage criterion.

Authors:  J J Gordon; J V Siebers
Journal:  Med Phys       Date:  2009-03       Impact factor: 4.071

2.  Dose-mass inverse optimization for minimally moving thoracic lesions.

Authors:  I B Mihaylov; E G Moros
Journal:  Phys Med Biol       Date:  2015-04-24       Impact factor: 3.609

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

Authors:  Vaitheeswaran Ranganathan; K Joseph Maria Das
Journal:  Rep Pract Oncol Radiother       Date:  2016-09-30

4.  Helical tomotherapy setup variations in canine nasal tumor patients immobilized with a bite block.

Authors:  Lyndsay N Kubicek; Songwon Seo; Richard J Chappell; Robert Jeraj; Lisa J Forrest
Journal:  Vet Radiol Ultrasound       Date:  2012-06-25       Impact factor: 1.363

5.  The effect of statistical noise on IMRT plan quality and convergence for MC-based and MC-correction-based optimized treatment plans.

Authors:  Jeffrey V Siebers
Journal:  J Phys Conf Ser       Date:  2008-04-04

6.  Incorporating deliverable monitor unit constraints into spot intensity optimization in intensity-modulated proton therapy treatment planning.

Authors:  Wenhua Cao; Gino Lim; Xiaoqiang Li; Yupeng Li; X Ronald Zhu; Xiaodong Zhang
Journal:  Phys Med Biol       Date:  2013-07-09       Impact factor: 3.609

7.  New approach in lung cancer radiotherapy offers better normal tissue sparing.

Authors:  Ivaylo B Mihaylov
Journal:  Radiother Oncol       Date:  2016-09-28       Impact factor: 6.280

8.  Pre-segmented 2-Step IMRT with subsequent direct machine parameter optimisation - a planning study.

Authors:  Klaus Bratengeier; Jürgen Meyer; Michael Flentje
Journal:  Radiat Oncol       Date:  2008-11-06       Impact factor: 3.481

9.  Calculated organ doses from selected prostate treatment plans using Monte Carlo simulations and an anatomically realistic computational phantom.

Authors:  Bryan Bednarz; Cindy Hancox; X George Xu
Journal:  Phys Med Biol       Date:  2009-08-11       Impact factor: 3.609

10.  Evaluation of dose prediction errors and optimization convergence errors of deliverable-based head-and-neck IMRT plans computed with a superposition/convolution dose algorithm.

Authors:  I B Mihaylov; J V Siebers
Journal:  Med Phys       Date:  2008-08       Impact factor: 4.071

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