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Fast dose algorithm for generation of dose coverage probability for robustness analysis of fractionated radiotherapy
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Medical Radiation Science. (Anders Ahnesjö)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Medical Radiation Science. (Anders Ahnesjö)
2015 (English)In: Physics in Medicine and Biology, ISSN 0031-9155, E-ISSN 1361-6560, Vol. 60, no 14, 5439-5454 p.Article in journal (Refereed) Published
Abstract [en]

A fast algorithm is constructed to facilitate dose calculation for a large number of randomly sampled treatment scenarios, each representing a possible realisation of a full treatment with geometric, fraction specific displacements for an arbitrary number of fractions. The algorithm is applied to construct a dose volume coverage probability map (DVCM) based on dose calculated for several hundred treatment scenarios to enable the probabilistic evaluation of a treatment plan.For each treatment scenario, the algorithm calculates the total dose by perturbing a pre-calculated dose, separately for the primary and scatter dose components, for the nominal conditions. The ratio of the scenario specific accumulated fluence, and the average fluence for an infinite number of fractions is used to perturb the pre-calculated dose. Irregularities in the accumulated fluence may cause numerical instabilities in the ratio, which is mitigated by regularisation through convolution with a dose pencil kernel.Compared to full dose calculations the algorithm demonstrates a speedup factor of ~1000. The comparisons to full calculations show a 99% gamma index (2%/2 mm) pass rate for a single highly modulated beam in a virtual water phantom subject to setup errors during five fractions. The gamma comparison shows a 100% pass rate in a moving tumour irradiated by a single beam in a lung-like virtual phantom. DVCM iso-probability lines computed with the fast algorithm, and with full dose calculation for each of the fractions, for a hypo-fractionated prostate case treated with rotational arc therapy treatment were almost indistinguishable.

Place, publisher, year, edition, pages
2015. Vol. 60, no 14, 5439-5454 p.
Keyword [en]
Radiotherapy dose calculation
National Category
Bioinformatics (Computational Biology)
Research subject
Physics
Identifiers
URN: urn:nbn:se:uu:diva-258095DOI: 10.1088/0031-9155/60/14/5439ISI: 000357620400008PubMedID: 26118844OAI: oai:DiVA.org:uu-258095DiVA: diva2:841069
Available from: 2015-07-10 Created: 2015-07-10 Last updated: 2017-12-04Bibliographically approved
In thesis
1. Probabilistic treatment planning based on dose coverage: How to quantify and minimize the effects of geometric uncertainties in radiotherapy
Open this publication in new window or tab >>Probabilistic treatment planning based on dose coverage: How to quantify and minimize the effects of geometric uncertainties in radiotherapy
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Traditionally, uncertainties are handled by expanding the irradiated volume to ensure target dose coverage to a certain probability. The uncertainties arise from e.g. the uncertainty in positioning of the patient at every fraction, organ motion and in defining the region of interests on the acquired images. The applied margins are inherently population based and do not exploit the geometry of the individual patient. Probabilistic planning on the other hand incorporates the uncertainties directly into the treatment optimization and therefore has more degrees of freedom to tailor the dose distribution to the individual patient. The aim of this thesis is to create a framework for probabilistic evaluation and optimization based on the concept of dose coverage probabilities. Several computational challenges for this purpose are addressed in this thesis.

The accuracy of the fraction by fraction accumulated dose depends directly on the accuracy of the deformable image registration (DIR). Using the simulation framework, we could quantify the requirements on the DIR to 2 mm or less for a 3% uncertainty in the target dose coverage.

Probabilistic planning is computationally intensive since many hundred treatments must be simulated for sufficient statistical accuracy in the calculated treatment outcome. A fast dose calculation algorithm was developed based on the perturbation of a pre-calculated dose distribution with the local ratio of the simulated treatment’s fluence and the fluence of the pre-calculated dose. A speedup factor of ~1000 compared to full dose calculation was achieved with near identical dose coverage probabilities for a prostate treatment.

For some body sites, such as the cervix dataset in this work, organ motion must be included for realistic treatment simulation. A statistical shape model (SSM) based on principal component analysis (PCA) provided the samples of deformation. Seven eigenmodes from the PCA was sufficient to model the dosimetric impact of the interfraction deformation.

A probabilistic optimization method was developed using constructs from risk management of stock portfolios that enabled the dose planner to request a target dose coverage probability. Probabilistic optimization was for the first time applied to dataset from cervical cancer patients where the SSM provided samples of deformation. The average dose coverage probability of all patients in the dataset was within 1% of the requested.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2016. 51 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine, ISSN 1651-6206 ; 1264
Keyword
Radiotherapy, treatment simulation, probabilistic planning, dose calculation, probabilistic optimization, statistical shape model
National Category
Other Physics Topics
Research subject
Medical Radiophysics
Identifiers
urn:nbn:se:uu:diva-304180 (URN)978-91-554-9720-0 (ISBN)
Public defence
2016-11-25, Skoogsalen, Ing. 78-79, Akademiska Sjukhuset, Uppsala, 13:00 (English)
Opponent
Supervisors
Available from: 2016-11-03 Created: 2016-10-03 Last updated: 2016-11-16

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Tilly, DavidAhnesjö, Anders

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