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  • 1.
    Tilly, David
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Medical Radiation Science.
    Ahnesjö, Anders
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Medical Radiation Science.
    Fast dose algorithm for generation of dose coverage probability for robustness analysis of fractionated radiotherapy2015In: Physics in Medicine and Biology, ISSN 0031-9155, E-ISSN 1361-6560, Vol. 60, no 14, p. 5439-5454Article in journal (Refereed)
    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.

  • 2.
    Tilly, David
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Section of Medical Physics.
    Tilly, Nina
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Section of Medical Physics.
    Ahnesjö, Anders
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Section of Medical Physics.
    Dose mapping sensitivity to deformable registration uncertainties in fractionated radiotherapy – applied to prostate proton treatments2013In: BMC Medical Physics, ISSN 1756-6649, E-ISSN 1756-6649, Vol. 13, no 2Article in journal (Refereed)
    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.

  • 3.
    Tilly, David
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Medical Radiation Science. Elekta Instruments AB, Stockholm, Sweden.
    van de Schoot, Agustinus J A J
    Academic Medical Center, Univesity of Amsterdam, Amsterdam, The Netherlands.
    Grusell, Erik
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Medical Radiation Science.
    Bel, Arjan
    Academic Medical Center, Univesity of Amsterdam, Amsterdam, The Netherlands.
    Ahnesjö, Anders
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Medical Radiation Science.
    Dose coverage calculation using a statistical shape model: applied to cervical cancer radiotherapy2017In: Physics in Medicine and Biology, ISSN 0031-9155, E-ISSN 1361-6560, Vol. 62, no 10, p. 4140-4159Article in journal (Refereed)
    Abstract [en]

    A comprehensive methodology for treatment simulation and evaluation of dose coverage probabilities is presented where a population based statistical shape model (SSM) provide samples of fraction specific patient geometry deformations.The learning data consists of vector fields from deformable image registration of repeated imaging giving intra-patient deformations which are mapped to an average patient serving as a common frame of reference. The SSM is created by extracting the most dominating eigenmodes through principal component analysis of the deformations from all patients. The sampling of a deformation is thus reduced to sampling weights for enough of the most dominating eigenmodes that describe the deformations.For the cervical cancer patient datasets in this work, we found seven eigenmodes to be sufficient to capture 90% of the variance in the deformations of the, and only three eigenmodes for stability in the simulated dose coverage probabilities. The normality assumption of the eigenmode weights was tested and found relevant for the 20 most dominating eigenmodes except for the first. Individualization of the SSM is demonstrated to be improved using two deformation samples from a new patient. The probabilistic evaluation provided additional information about the trade-offs compared to the conventional single dataset treatment planning.

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