Literature DB >> 21081826

Fluence-convolution broad-beam (FCBB) dose calculation.

Weiguo Lu1, Mingli Chen.   

Abstract

IMRT optimization requires a fast yet relatively accurate algorithm to calculate the iteration dose with small memory demand. In this paper, we present a dose calculation algorithm that approaches these goals. By decomposing the infinitesimal pencil beam (IPB) kernel into the central axis (CAX) component and lateral spread function (LSF) and taking the beam's eye view (BEV), we established a non-voxel and non-beamlet-based dose calculation formula. Both LSF and CAX are determined by a commissioning procedure using the collapsed-cone convolution/superposition (CCCS) method as the standard dose engine. The proposed dose calculation involves a 2D convolution of a fluence map with LSF followed by ray tracing based on the CAX lookup table with radiological distance and divergence correction, resulting in complexity of O(N(3)) both spatially and temporally. This simple algorithm is orders of magnitude faster than the CCCS method. Without pre-calculation of beamlets, its implementation is also orders of magnitude smaller than the conventional voxel-based beamlet-superposition (VBS) approach. We compared the presented algorithm with the CCCS method using simulated and clinical cases. The agreement was generally within 3% for a homogeneous phantom and 5% for heterogeneous and clinical cases. Combined with the 'adaptive full dose correction', the algorithm is well suitable for calculating the iteration dose during IMRT optimization.

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Year:  2010        PMID: 21081826     DOI: 10.1088/0031-9155/55/23/003

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  6 in total

1.  Using deep learning to predict beam-tunable Pareto optimal dose distribution for intensity-modulated radiation therapy.

Authors:  Gyanendra Bohara; Azar Sadeghnejad Barkousaraie; Steve Jiang; Dan Nguyen
Journal:  Med Phys       Date:  2020-08-02       Impact factor: 4.071

2.  Technical Note: A feasibility study on deep learning-based radiotherapy dose calculation.

Authors:  Yixun Xing; Dan Nguyen; Weiguo Lu; Ming Yang; Steve Jiang
Journal:  Med Phys       Date:  2019-12-25       Impact factor: 4.071

Review 3.  GPU-based high-performance computing for radiation therapy.

Authors:  Xun Jia; Peter Ziegenhein; Steve B Jiang
Journal:  Phys Med Biol       Date:  2014-02-03       Impact factor: 3.609

4.  A GPU-based finite-size pencil beam algorithm with 3D-density correction for radiotherapy dose calculation.

Authors:  Xuejun Gu; Urszula Jelen; Jinsheng Li; Xun Jia; Steve B Jiang
Journal:  Phys Med Biol       Date:  2011-05-10       Impact factor: 3.609

5.  Deep learning-based inverse mapping for fluence map prediction.

Authors:  Lin Ma; Mingli Chen; Xuejun Gu; Weiguo Lu
Journal:  Phys Med Biol       Date:  2020-11-27       Impact factor: 3.609

6.  Dosimetric evaluation of intensity-modulated radiotherapy, volumetric modulated arc therapy, and helical tomotherapy for hippocampal-avoidance whole brain radiotherapy.

Authors:  Yi Rong; Josh Evans; Meng Xu-Welliver; Cadron Pickett; Guang Jia; Quan Chen; Li Zuo
Journal:  PLoS One       Date:  2015-04-20       Impact factor: 3.240

  6 in total

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