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Dose mapping sensitivity to deformable registration uncertainties in fractionated radiotherapy – applied to prostate proton treatments
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Section of Medical Physics.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Section of Medical Physics.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Section of Medical Physics.
2013 (English)In: BMC Medical Physics, ISSN 1756-6649, E-ISSN 1756-6649, Vol. 13, no 2Article in journal (Refereed) Published
Abstract [en]

Background

Calculation of accumulated dose in fractionated radiotherapy based on spatial mapping of the dose points generally requires deformable image registration (DIR). The accuracy of the accumulated dose thus depends heavily on the DIR quality. This motivates investigations of how the registration uncertainty influences dose planning objectives and treatment outcome predictions.

A framework was developed where the dose mapping can be associated with a variable known uncertainty to simulate the DIR uncertainties in a clinical workflow. The framework enabled us to study the dependence of dose planning metrics, and the predicted treatment outcome, on the DIR uncertainty. The additional planning margin needed to compensate for the dose mapping uncertainties can also be determined. We applied the simulation framework to a hypofractionated proton treatment of the prostate using two different scanning beam spot sizes to also study the dose mapping sensitivity to penumbra widths.

Results

The planning parameter most sensitive to the DIR uncertainty was found to be the targetD95. We found that the registration mean absolute error needs to be ≤0.20 cm to obtain an uncertainty better than 3% of the calculated D95 for intermediate sized penumbras. Use of larger margins in constructing PTV from CTV relaxed the registration uncertainty requirements to the cost of increased dose burdens to the surrounding organs at risk.

Conclusions

The DIR uncertainty requirements should be considered in an adaptive radiotherapy workflow since this uncertainty can have significant impact on the accumulated dose. The simulation framework enabled quantification of the accuracy requirement for DIR algorithms to provide satisfactory clinical accuracy in the accumulated dose.

Place, publisher, year, edition, pages
2013. Vol. 13, no 2
Keyword [en]
Radiotherapy; Adaptive radiotherapy; Dose tracking; Dose mapping; Dose accumulation; Dose accumulation accuracy; Deformable image registration; Non-rigid image registration; Protons
National Category
Other Medical Engineering
Research subject
Medical Radiophysics
Identifiers
URN: urn:nbn:se:uu:diva-224059DOI: 10.1186/1756-6649-13-2OAI: oai:DiVA.org:uu-224059DiVA: diva2:715123
Available from: 2014-04-30 Created: 2014-04-30 Last updated: 2017-12-05Bibliographically 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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