Estimation of Delivered Dose in Radiotherapy: The Influence of Registration Uncertainty

Risholm, Petter; Balter, James M.; Wells, William M.

doi:10.1007/978-3-642-23623-5_69

Cited by 23 publications

(29 citation statements)

References 9 publications

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“…For this purpose we developed a simulation framework for dose accumulation where the uncertainty of the calculated treatment outcome (represented as dose, or as dose response determined with a biological models) as a function of the registration uncertainty could be investigated. The dosimetric impact of the registration uncertainty on the entire fractionated treatment has been studied by Risholm et al [12] who presented the uncertainty of the total physical dose for a single estimated registration uncertainty, while we study how the final dose and the related response would vary with the registration uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Dose mapping sensitivity to deformable registration uncertainties in fractionated radiotherapy – applied to prostate proton treatments

Tilly

Ahnesjö

2013

BMC Med Phys

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BackgroundCalculation 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.ResultsThe planning parameter most sensitive to the DIR uncertainty was found to be the target D95. 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.ConclusionsThe 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.

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Section: Introductionmentioning

confidence: 99%

Dose mapping sensitivity to deformable registration uncertainties in fractionated radiotherapy – applied to prostate proton treatments

Tilly

Ahnesjö

2013

BMC Med Phys

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“…Very recently, Iglesias et al [14] introduced an approach to hippocampal subfield segmentation, where registration uncertainty is integrated out of a combined registration/Gaussian mixture model approach to segmentation using MCMC. Risholm et al [15] proposed an approach to calculating the uncertainty of a delivered dose in radiotherapy under uncertain registration. Here, we follow in a similar fashion by estimating the variability in statistical predictors that is due to the uncertainty in registration.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Learning Incorporating Uncertain Registration

Simpson

Woolrich

Andersson

et al. 2013

IEEE Trans. Med. Imaging

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Abstract-This paper proposes a novel approach for improving the accuracy of statistical prediction methods in spatially normalised analysis. This is achieved by incorporating registration uncertainty into an ensemble learning scheme. A probabilistic registration method is used to estimate a distribution of probable mappings between subject and atlas space. This allows the estimation of the distribution of spatially normalised feature data, e.g. grey matter probability maps. From this distribution, samples are drawn for use as training examples. This allows the creation of multiple predictors, which are subsequently combined using an ensemble learning approach. Furthermore, extra testing samples can be generated to measure the uncertainty of prediction. This is applied to separating subjects with Alzheimer's disease from normal controls using a linear support vector machine on a region of interest in magnetic resonance images of the brain. We show that our proposed method leads to an improvement in discrimination using voxel based morphometry and deformation tensor based morphometry over bootstrap aggregating, a common ensemble learning framework. The proposed approach also generates more reasonable soft-classification predictions than bootstrap aggregating. We expect that this approach could be applied to other statistical prediction tasks where registration is important.

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“…In adaptive radiotherapy, monitoring the radiation dose delivered to critical tissue is important due to changes in anatomy over the treatment period. In Risholm et al (2011) we showed that the uncertainty in registration can induce significant uncertainty in the estimated cumulative dose delivered to critical tissue during head and neck radiotherapy. The estimated delivered dose and the associated uncertainty was visualized in terms of dose volume histograms.…”

Section: Discussionmentioning

confidence: 99%

Bayesian characterization of uncertainty in intra-subject non-rigid registration

Risholm

Janoos

Norton

et al. 2013

Medical Image Analysis

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In settings where high-level inferences are made based on registered image data, the registration uncertainty can contain important information. In this article, we propose a Bayesian non-rigid registration framework where conventional dissimilarity and regularization energies can be included in the likelihood and the prior distribution on deformations respectively through the use of Boltzmann’s distribution. The posterior distribution is characterized using Markov Chain Monte Carlo (MCMC) methods with the effect of the Boltzmann temperature hyper-parameters marginalized under broad uninformative hyper-prior distributions. The MCMC chain permits estimation of the most likely deformation as well as the associated uncertainty. On synthetic examples, we demonstrate the ability of the method to identify the maximum a posteriori estimate and the associated posterior uncertainty, and demonstrate that the posterior distribution can be non-Gaussian. Additionally, results from registering clinical data acquired during neurosurgery for resection of brain tumor are provided; we compare the method to single transformation results from a deterministic optimizer and introduce methods that summarize the high-dimensional uncertainty. At the site of resection, the registration uncertainty increases and the marginal distribution on deformations is shown to be multi-modal.

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Estimation of Delivered Dose in Radiotherapy: The Influence of Registration Uncertainty

Cited by 23 publications

References 9 publications

Dose mapping sensitivity to deformable registration uncertainties in fractionated radiotherapy – applied to prostate proton treatments

Dose mapping sensitivity to deformable registration uncertainties in fractionated radiotherapy – applied to prostate proton treatments

Ensemble Learning Incorporating Uncertain Registration

Bayesian characterization of uncertainty in intra-subject non-rigid registration

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