liu.seSearch for publications in DiVA

CiteExport$(function(){PrimeFaces.cw("TieredMenu","widget_formSmash_upper_j_idt144",{id:"formSmash:upper:j_idt144",widgetVar:"widget_formSmash_upper_j_idt144",autoDisplay:true,overlay:true,my:"left top",at:"left bottom",trigger:"formSmash:upper:exportLink",triggerEvent:"click"});}); $(function(){PrimeFaces.cw("OverlayPanel","widget_formSmash_upper_j_idt145_j_idt147",{id:"formSmash:upper:j_idt145:j_idt147",widgetVar:"widget_formSmash_upper_j_idt145_j_idt147",target:"formSmash:upper:j_idt145:permLink",showEffect:"blind",hideEffect:"fade",my:"right top",at:"right bottom",showCloseIcon:true});});

Causes of multimodality of efficiency gain distributions in accelerated Monte Carlo based dose calculations for brachytherapy planning using correlated samplingPrimeFaces.cw("AccordionPanel","widget_formSmash_some",{id:"formSmash:some",widgetVar:"widget_formSmash_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_all",{id:"formSmash:all",widgetVar:"widget_formSmash_all",multiple:true});
function selectAll()
{
var panelSome = $(PrimeFaces.escapeClientId("formSmash:some"));
var panelAll = $(PrimeFaces.escapeClientId("formSmash:all"));
panelAll.toggle();
toggleList(panelSome.get(0).childNodes, panelAll);
toggleList(panelAll.get(0).childNodes, panelAll);
}
/*Toggling the list of authorPanel nodes according to the toggling of the closeable second panel */
function toggleList(childList, panel)
{
var panelWasOpen = (panel.get(0).style.display == 'none');
// console.log('panel was open ' + panelWasOpen);
for (var c = 0; c < childList.length; c++) {
if (childList[c].classList.contains('authorPanel')) {
clickNode(panelWasOpen, childList[c]);
}
}
}
/*nodes have styleClass ui-corner-top if they are expanded and ui-corner-all if they are collapsed */
function clickNode(collapse, child)
{
if (collapse && child.classList.contains('ui-corner-top')) {
// console.log('collapse');
child.click();
}
if (!collapse && child.classList.contains('ui-corner-all')) {
// console.log('expand');
child.click();
}
}
PrimeFaces.cw("AccordionPanel","widget_formSmash_responsibleOrgs",{id:"formSmash:responsibleOrgs",widgetVar:"widget_formSmash_responsibleOrgs",multiple:true}); 2009 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
##### Abstract [en]

##### Place, publisher, year, edition, pages

2009. , p. 48
##### Keyword [en]

Monte Carlo simulations, brachytherapy, efficiency gain
##### National Category

Medical Laboratory and Measurements Technologies
##### Identifiers

URN: urn:nbn:se:liu:diva-56468ISRN: LiTH-IMT/MI30-A-EX--09/487--SEOAI: oai:DiVA.org:liu-56468DiVA, id: diva2:319414
##### Subject / course

Medical Informatics
##### Presentation

2009-12-17, Läderbaggen, Linköping, 13:15 (English)
##### Uppsok

Physics, Chemistry, Mathematics

#####

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt432",{id:"formSmash:j_idt432",widgetVar:"widget_formSmash_j_idt432",multiple:true});
##### Supervisors

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt438",{id:"formSmash:j_idt438",widgetVar:"widget_formSmash_j_idt438",multiple:true});
##### Examiners

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt444",{id:"formSmash:j_idt444",widgetVar:"widget_formSmash_j_idt444",multiple:true});
Available from: 2010-05-25 Created: 2010-05-17 Last updated: 2012-09-27Bibliographically approved

Fixed-collision correlated sampling for Monte Carlo (MC) simulations is a method which can be used in order to shorten the simulation time for brachytherapy treatment planning in a 3D patient geometry. The increased efficiency compared to conventional MC simulation is measured by efficiency gain. However, a previous study showed that, in some cases, PDFs (probability density functions) of estimates of the efficiency gain, simulated using resampling and other MC methods, were multimodal with values below 1. This means that the method was less effective than conventional sampling for these cases. The aims of this thesis were to trace the causes of the multimodal distributions and to propose techniques to mitigate the problem caused by photons with high statistical weights.Two simulation environments were used for the study case, a homogeneous and a heterogeneous environment. The homogenous environment consisted of a water sphere with the radius 100mm. For the heterogeneous environment a cylindrical block of tungsten alloy (diameter 15 mm, height 2.5 mm) was placed in the water sphere. The sphere was divided into an array of cubic voxels of size 2.5 mm x 2.5 mm x 2.5 mm for dose calculations. A photon source was positioned in the middle of the water sphere and emitted photons with the energy 400 keV.It was found that the low values and multimodal PDFs for the efficiency gain estimates originated from photons depositing high values of energy in some voxels in the heterogeneous environment. The high energy deposits were due to extremely high statistical weights of photons interacting repeatedly in the highly attenuating tungsten cylinder. When photon histories contributing to the rare events of high energy deposits (outliers) were removed, the PDFs became uni-modal and efficiency gain increased. However, removing outliers will cause results to be biased calling for other techniques to handle the problem with high statistical weights.One way to resolve the problem in the current implementation of the fixed-collision correlated sampling scheme in PTRAN (the MC code used) could be to split photons with high statistical weights into several photons with the same sum weight as the initial photon. The splitting of photons will result in more time consuming simulations in areas with high attenuation coefficients, which may not be the areas of interest. This could be resolved by using Russian roulette, eliminating some of the photons with high statistical weight in such areas.Fixed-collision correlated sampling for Monte Carlo (MC) simulations is a method which can be used in order to shorten the simulation time for brachytherapy treatment planning in a 3D patient geometry. The increased efficiency compared to conventional MC simulation is measured by efficiency gain. However, a previous study showed that, in some cases, PDFs (probability density functions) of estimates of the efficiency gain, simulated using resampling and other MC methods, were multimodal with values below 1. This means that the method was less effective than conventional sampling for these cases. The aims of this thesis were to trace the causes of the multimodal distributions and to propose techniques to mitigate the problem caused by photons with high statistical weights.Two simulation environments were used for the study case, a homogeneous and a heterogeneous environment. The homogenous environment consisted of a water sphere with the radius 100mm. For the heterogeneous environment a cylindrical block of tungsten alloy (diameter 15 mm, height 2.5 mm) was placed in the water sphere. The sphere was divided into an array of cubic voxels of size 2.5 mm x 2.5 mm x 2.5 mm for dose calculations. A photon source was positioned in the middle of the water sphere and emitted photons with the energy 400 keV.It was found that the low values and multimodal PDFs for the efficiency gain estimates originated from photons depositing high values of energy in some voxels in the heterogeneous environment. The high energy deposits were due to extremely high statistical weights of photons interacting repeatedly in the highly attenuating tungsten cylinder. When photon histories contributing to the rare events of high energy deposits (outliers) were removed, the PDFs became uni-modal and efficiency gain increased. However, removing outliers will cause results to be biased calling for other techniques to handle the problem with high statistical weights.One way to resolve the problem in the current implementation of the fixed-collision correlated sampling scheme in PTRAN (the MC code used) could be to split photons with high statistical weights into several photons with the same sum weight as the initial photon. The splitting of photons will result in more time consuming simulations in areas with high attenuation coefficients, which may not be the areas of interest. This could be resolved by using Russian roulette, eliminating some of the photons with high statistical weight in such areas.

urn-nbn$(function(){PrimeFaces.cw("Tooltip","widget_formSmash_j_idt1141",{id:"formSmash:j_idt1141",widgetVar:"widget_formSmash_j_idt1141",showEffect:"fade",hideEffect:"fade",showDelay:500,hideDelay:300,target:"formSmash:altmetricDiv"});});

CiteExport$(function(){PrimeFaces.cw("TieredMenu","widget_formSmash_lower_j_idt1194",{id:"formSmash:lower:j_idt1194",widgetVar:"widget_formSmash_lower_j_idt1194",autoDisplay:true,overlay:true,my:"left top",at:"left bottom",trigger:"formSmash:lower:exportLink",triggerEvent:"click"});}); $(function(){PrimeFaces.cw("OverlayPanel","widget_formSmash_lower_j_idt1195_j_idt1197",{id:"formSmash:lower:j_idt1195:j_idt1197",widgetVar:"widget_formSmash_lower_j_idt1195_j_idt1197",target:"formSmash:lower:j_idt1195:permLink",showEffect:"blind",hideEffect:"fade",my:"right top",at:"right bottom",showCloseIcon:true});});