title: Analytical Models for Probabilistic Inverse Treatment Planning in Intensity-modulated Proton Therapy
creator: Wahl, Niklas
subject: ddc-500
subject: 500 Natural sciences and mathematics
subject: ddc-530
subject: 530 Physics
description: The sensitivity of intensity-modulated proton therapy to uncertainties requires case-specific uncertainty  assessment and mitigation. As an alternative to scenario-based methods, this thesis describes  the implementation, application and conceptual extension of the Analytical Probabilistic  Modeling (APM) framework introduced by Bangert, Hennig, and Oelfke (2013). APM represents  moments of the probability distribution over dose in closed-form, providing a probabilistic analog  to nominal pencil-beam dose calculation subject to range and setup uncertainties that further  enables probabilistic optimization.  First, APM was implemented in MITKrad, a treatment planning plugin for MITK built completely  from scratch. APM’s computations were validated against sample statistics, showing nearly perfect  agreement. Run-times within minutes could be realized for uncertainty assessment and probabilistic  optimization on patient data.  Reformulation of APM enabled linear separation of the computations into random and systematic  uncertainty components. Uncertainty over the full fractionation spectrum could then be  modeled and optimized with a single pre-computation. It could be shown that fractionation is  exploited in optimization with APM for additional organ at risk sparing.  APM was then extended to propagation of uncertainties from dose to clinically relevant plan  quality metrics. Expectation and variance could be modeled accurately for organ mean dose and  dose-volume histograms. However, approximations for equivalent uniform dose and minimum  and maximum dose values did not provide reliable results.  Finally, the closed-form plan metrics were used to conceptualize constrained probabilistic optimization.  Besides novel probabilistic objectives, confidence constraints could be established.  Due to increased computational complexity of the new models, the proof-of-concept was provided  through evaluations on a one-dimensional prototype anatomy.  In conclusion, the herein extended APM framework is able to provide probabilistic analogs  to established nominal concepts of dose calculation, plan quality metrics, and constrained optimization.  If computational hurdles can be overcome in the future, clinical application would be  within reach.
date: 2018
type: Dissertation
type: info:eu-repo/semantics/doctoralThesis
type: NonPeerReviewed
format: application/pdf
identifier: https://archiv.ub.uni-heidelberg.de/volltextserverhttps://archiv.ub.uni-heidelberg.de/volltextserver/25127/1/WahlN_dissertation_public.pdf
identifier: DOI:10.11588/heidok.00025127
identifier: urn:nbn:de:bsz:16-heidok-251273
identifier:   Wahl, Niklas  (2018) Analytical Models for Probabilistic Inverse Treatment Planning in Intensity-modulated Proton Therapy.  [Dissertation]     
relation: https://archiv.ub.uni-heidelberg.de/volltextserver/25127/
rights: info:eu-repo/semantics/openAccess
rights: http://archiv.ub.uni-heidelberg.de/volltextserver/help/license_urhg.html
language: eng