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dc.contributor.authorPrice, Gareth J
dc.contributor.authorMoore, Christopher J
dc.date.accessioned2009-06-09T16:20:48Z
dc.date.available2009-06-09T16:20:48Z
dc.date.issued2007-04-07
dc.identifier.citationA method to calculate coverage probability from uncertainties in radiotherapy via a statistical shape model. 2007, 52 (7):1947-65 Phys Med Biolen
dc.identifier.issn0031-9155
dc.identifier.pmid17374921
dc.identifier.doi10.1088/0031-9155/52/7/012
dc.identifier.urihttp://hdl.handle.net/10541/70053
dc.description.abstractIn this paper we describe a technique that may be used to model the geometric uncertainties that accrue during the radiotherapy process. Using data from in-treatment cone beam CT scans, we simultaneously analyse non-uniform observer delineation variability and organ motion together with patient set-up errors via the creation of a point distribution model (PDM). We introduce a novel method of generating a coverage probability matrix, that may be used to determine treatment margins and calculate uncertainties in dose, from this statistical shape model. The technique does not assume rigid body motion and can extrapolate shape variability in a statistically meaningful manner. In order to construct the PDM, we generate corresponding surface points over a set of delineations. Correspondences are established at a set of points in parameter space on spherically parameterized and canonical aligned outlines. The method is demonstrated using rectal delineations from serially acquired in-treatment cone beam CT image volumes of a prostate patient (44 image volumes total), each delineated by a minimum of two observers (maximum six). Two PDMs are constructed, one with set-up errors included and one without. We test the normality assumptions of the PDMs and find the distributions to be Gaussian in nature. The rectal PDM variability is in general agreement with data in the literature. The two resultant coverage probability matrices show differences as expected.
dc.language.isoenen
dc.subjectProstatic Canceren
dc.subject.meshDiffusion
dc.subject.meshHumans
dc.subject.meshImage Processing, Computer-Assisted
dc.subject.meshMale
dc.subject.meshModels, Anatomic
dc.subject.meshModels, Statistical
dc.subject.meshProbability
dc.subject.meshProstate
dc.subject.meshProstatic Neoplasms
dc.subject.meshRadiation Oncology
dc.subject.meshRadiotherapy, Conformal
dc.subject.meshReproducibility of Results
dc.subject.meshSurface Properties
dc.subject.meshTime Factors
dc.subject.meshTomography, X-Ray Computed
dc.titleA method to calculate coverage probability from uncertainties in radiotherapy via a statistical shape model.en
dc.typeArticleen
dc.contributor.departmentDeveloping Technologies Radiotherapy, North Western Medical Physics, Christie Hospital NHS Trust, Wilmslow Road, Manchester M20 4BX, UK. gareth.price@physics.cr.man.ac.uken
dc.identifier.journalPhysics in Medicine and Biologyen
html.description.abstractIn this paper we describe a technique that may be used to model the geometric uncertainties that accrue during the radiotherapy process. Using data from in-treatment cone beam CT scans, we simultaneously analyse non-uniform observer delineation variability and organ motion together with patient set-up errors via the creation of a point distribution model (PDM). We introduce a novel method of generating a coverage probability matrix, that may be used to determine treatment margins and calculate uncertainties in dose, from this statistical shape model. The technique does not assume rigid body motion and can extrapolate shape variability in a statistically meaningful manner. In order to construct the PDM, we generate corresponding surface points over a set of delineations. Correspondences are established at a set of points in parameter space on spherically parameterized and canonical aligned outlines. The method is demonstrated using rectal delineations from serially acquired in-treatment cone beam CT image volumes of a prostate patient (44 image volumes total), each delineated by a minimum of two observers (maximum six). Two PDMs are constructed, one with set-up errors included and one without. We test the normality assumptions of the PDMs and find the distributions to be Gaussian in nature. The rectal PDM variability is in general agreement with data in the literature. The two resultant coverage probability matrices show differences as expected.


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