Please use this identifier to cite or link to this item: https://physrep.ff.bg.ac.rs/handle/123456789/1178
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dc.contributor.authorEl Naqa, Ien_US
dc.contributor.authorKawrakow, Ien_US
dc.contributor.authorFippel, Men_US
dc.contributor.authorSiebers, J Ven_US
dc.contributor.authorLindsay, P Een_US
dc.contributor.authorWickerhauser, M Ven_US
dc.contributor.authorVićić, Milošen_US
dc.contributor.authorZakarian, Ken_US
dc.contributor.authorKauffmann, Nen_US
dc.contributor.authorDeasy, J Oen_US
dc.date.accessioned2022-07-12T18:35:34Z-
dc.date.available2022-07-12T18:35:34Z-
dc.date.issued2005-03-07-
dc.identifier.issn0031-9155en
dc.identifier.urihttps://physrep.ff.bg.ac.rs/handle/123456789/1178-
dc.description.abstractRecent studies have demonstrated that Monte Carlo (MC) denoising techniques can reduce MC radiotherapy dose computation time significantly by preferentially eliminating statistical fluctuations ('noise') through smoothing. In this study, we compare new and previously published approaches to MC denoising, including 3D wavelet threshold denoising with sub-band adaptive thresholding, content adaptive mean-median-hybrid (CAMH) filtering, locally adaptive Savitzky-Golay curve-fitting (LASG), anisotropic diffusion (AD) and an iterative reduction of noise (IRON) method formulated as an optimization problem. Several challenging phantom and computed-tomography-based MC dose distributions with varying levels of noise formed the test set. Denoising effectiveness was measured in three ways: by improvements in the mean-square-error (MSE) with respect to a reference (low noise) dose distribution; by the maximum difference from the reference distribution and by the 'Van Dyk' pass/fail criteria of either adequate agreement with the reference image in low-gradient regions (within 2% in our case) or, in high-gradient regions, a distance-to-agreement-within-2% of less than 2 mm. Results varied significantly based on the dose test case: greater reductions in MSE were observed for the relatively smoother phantom-based dose distribution (up to a factor of 16 for the LASG algorithm); smaller reductions were seen for an intensity modulated radiation therapy (IMRT) head and neck case (typically, factors of 2-4). Although several algorithms reduced statistical noise for all test geometries, the LASG method had the best MSE reduction for three of the four test geometries, and performed the best for the Van Dyk criteria. However, the wavelet thresholding method performed better for the head and neck IMRT geometry and also decreased the maximum error more effectively than LASG. In almost all cases, the evaluated methods provided acceleration of MC results towards statistically more accurate results.en
dc.language.isoenen
dc.relation.ispartofPhysics in medicine and biologyen
dc.subject.meshHead and Neck Neoplasmsen
dc.subject.meshLung Neoplasmsen
dc.subject.meshRadiometryen
dc.subject.meshRadiotherapy Planning, Computer-Assisteden
dc.subject.meshRadiotherapy, Conformalen
dc.titleA comparison of Monte Carlo dose calculation denoising techniquesen_US
dc.typeJournal Articleen_US
dc.identifier.doi10.1088/0031-9155/50/5/014-
dc.identifier.pmid15798264-
dc.identifier.scopus2-s2.0-20144387841-
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/20144387841-
dc.relation.issue5en
dc.relation.volume50en
dc.relation.firstpage909-22en
dc.relation.lastpage922en
item.openairetypeJournal Article-
item.cerifentitytypePublications-
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
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