Bruno Schnekenburger - Academia.edu (original) (raw)
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Papers by Bruno Schnekenburger
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology, 2007
The aim of this study was the clinical evaluation of an independent dose and monitor unit verific... more The aim of this study was the clinical evaluation of an independent dose and monitor unit verification (MUV) software which is based on sophisticated semi-analytical modelling. The software was developed within the framework of an ESTRO project. Finally, consistent handling of dose calculation deviations applying individual action levels is discussed. A Matlab-based software ("MUV") was distributed to five well-established treatment centres in Europe (Vienna, Graz, Basel, Copenhagen, and Umeå) and evaluated as a quality assurance (QA) tool in clinical routine. Results were acquired for 226 individual treatment plans including a total of 815 radiation fields. About 150 beam verification measurements were performed for a portion of the individual treatment plans, mainly with time variable fluence patterns. The deviations between dose calculations performed with a treatment planning system (TPS) and the MUV software were scored with respect to treatment area, treatment techni...
Title of Thesis: A Modified Extended Kalman Filter As A Parameter Estimator For Linear Discrete-T... more Title of Thesis: A Modified Extended Kalman Filter As A Parameter Estimator For Linear Discrete-Time Systems Bruno J. Schnekenburger Master of Science, 1988 Thesis directed by: Prof. Dr. Andrew U. Meyer Asst. Prof. Dr. B. Tank Oranc This thesis presents the derivation and implementation of a modified Extended Kalman Filter used for joint state and parameter estimation of linear discrete-time systems operating in a stochastic Gaussian environment. A novel derivation for the discrete-time Extended Kalman Filter is also presented. In order to eliminate the main deficiencies of the Extended Kalman Filter, which are divergence and biasedness of its estimates, the filter algorithm has been modified. The primary modifications are due to Ujung, who stated global convergence properties for the modified Extended Kalman Filter, when used as a parameter estimator for linear systems. Implementation of this filter is further complicated by the need to initialize the parameter estimate error covar...
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology, 2007
The aim of this study was the clinical evaluation of an independent dose and monitor unit verific... more The aim of this study was the clinical evaluation of an independent dose and monitor unit verification (MUV) software which is based on sophisticated semi-analytical modelling. The software was developed within the framework of an ESTRO project. Finally, consistent handling of dose calculation deviations applying individual action levels is discussed. A Matlab-based software ("MUV") was distributed to five well-established treatment centres in Europe (Vienna, Graz, Basel, Copenhagen, and Umeå) and evaluated as a quality assurance (QA) tool in clinical routine. Results were acquired for 226 individual treatment plans including a total of 815 radiation fields. About 150 beam verification measurements were performed for a portion of the individual treatment plans, mainly with time variable fluence patterns. The deviations between dose calculations performed with a treatment planning system (TPS) and the MUV software were scored with respect to treatment area, treatment techni...
Title of Thesis: A Modified Extended Kalman Filter As A Parameter Estimator For Linear Discrete-T... more Title of Thesis: A Modified Extended Kalman Filter As A Parameter Estimator For Linear Discrete-Time Systems Bruno J. Schnekenburger Master of Science, 1988 Thesis directed by: Prof. Dr. Andrew U. Meyer Asst. Prof. Dr. B. Tank Oranc This thesis presents the derivation and implementation of a modified Extended Kalman Filter used for joint state and parameter estimation of linear discrete-time systems operating in a stochastic Gaussian environment. A novel derivation for the discrete-time Extended Kalman Filter is also presented. In order to eliminate the main deficiencies of the Extended Kalman Filter, which are divergence and biasedness of its estimates, the filter algorithm has been modified. The primary modifications are due to Ujung, who stated global convergence properties for the modified Extended Kalman Filter, when used as a parameter estimator for linear systems. Implementation of this filter is further complicated by the need to initialize the parameter estimate error covar...