Optimization and control in deep hyperthermia: Clinical implementation of hyperthermia treatment planning in cervical cancer treatment to obtain a higher treatment quality

Deep hyperthermia is a treatment used in concurrence with radiation therapy or chemotherapy in the treatment of deep seated tumors. In hyperthermia, tumor temperatures are elevated 3 to 7oC above normal body temperature, up to a temperature of 44oC. In a randomized trial, the 3 year overall survival of cervical cancer patients was almost doubled by adding hyperthermia to radiotherapy. There is a clear dose-effect relation in hyperthermia, and therefore increasing the temperature in the tumor is an important factor to further increase survival rates in cervical cancer. Until recently, hyperthermia treatments in Rotterdam were performed by aiming a focus point that was calculated using a cylindrical representation of the patient. Because of the inhomogeneous nature of a patient, this representation is far from accurate. For the 4 antenna Sigma 60, the calculated focus point may still be close to the optimum, but for applicators with more antennas, and a high number of degrees of freedom, this approach will certainly be inadequate. Originating in the 1970’s, electromagnetic numerical and thermal modeling of 3D structures is currently possible with a precision and speed that is sufficient for routine use. When the electromagnetic and thermal properties of a patient are known, the energy and thermal distributions can be calculated for each antenna of the applicator. With this information, the interference pattern can be determined, dependent on phase and amplitude of the emitted signals by the antennas, and thus can be optimized. When performing these patient specific calculations, i.e. treatment planning, and optimizations, the resulting settings can be applied on-line in the clinic. This thesis covers the clinical introduction of hyperthermia treatment planning, the assessment of the various uncertainties that should be taken into account, and the results of clinical implementation. Optimization The successful application of hyperthermia treatment planning requires optimization routines that optimize the SAR distribution in such manner that the eventual dose in the tumor is maximized. In chapter 2, various SAR based goal functions were assessed. This assessment showed that a goal function taking into account hotspot minimization as well as maximization of the SAR in the tumor has the highest probability to lead to high tumor temperatures. Eventually, two goal functions were chosen for clinical assessment: average tumor SAR normalized on whole body average SAR (Opt1), and hotspot tumor quotient (HTQ), the ratio between SAR in the 0.1th percentile and the tumor SAR (Opt2). Further, the concept of complaint adaptive steering is tested, i.e. local reduction of SAR in case of patient discomfort by adapting the goal function. The phantom test and a sensitivity study in 10 patient models, show that complaint adaptive steering is most effective in peripheral complaint regions. Clinical evaluation in two groups

Use of multi-angle high-resolution imagery and 3D information for urban land-cover classification: a case study on Istanbul

The BELSPO-MAMUD project focuses on the use of Remote Sensing data for measuring and modelling urban dynamics. Remote sensing is a wonderful tool to produce long time-series of high resolution maps of sealed surface useful for this purpose. In the urban context of Istanbul, a very dynamic city, recent high resolution satellite images and medium resolution images from the past have been exploited to calibrate and validate a regression-based sub-pixel classification method allowing this production. In this context it’s a tricky task for several reasons: prominent occurrence of shadowed and occluded areas and urban canyons, spectral confusions between urban and non-urban materials at ground and roof levels, moderately hilly relief ... To cope with these difficulties the combined use of three types of data may be helpful: diachronic (i), multi-angle and 3D data. A master multispectral and panchromatic QuickBird image and a panchromatic Ikonos stereopair, all acquired in March 2002, were used in combination with a multispectral and panchromatic Ikonos image of May 2005. A DSM was generated from the Ikonos stereopair and building vector file. It was used for orthorectification, building height estimation and classification procedure. The area covered by the high resolution products was divided in 3 partitions and each one was classified independently. This application demonstrates that recent high resolution land-cover classification produced using multi-date, multi-angle and DSM can be used to produce sealed surface maps from longer timeseries of medium resolution images over large urban areas enabling so the analysis of urban dynamics

High-resolution simulations of population-density change with an activity-based cellular automata land-use model

The MOLAND model is a cellular automata (CA) land-use change model that has often been applied to simulate urban growth. A more recent alternative model makes the simulations more multifunctional by also computing different activities (population and employment) for every cell. However, the equation to update population density in time in this activity-based CA model could not deal with high population growth rates in some existing urban centres. Therefore, we experimented with two alternative equations. A semi-automated calibration routine was used to compare errors of the different model versions at a continuous range of resolutions in two study areas: the Greater Dublin Region, Ireland, and Flanders and Brussels, Belgium. The two new population density equations turn out to solve the particular problem of fast changes in high-density neighbourhoods and generally improve regional errors in the Belgian application, but can unfortunately introduce larger errors in low-density areas or in the land-use simulations

Improving distributed runoff prediction in urbanized catchments with remote sensing based estimates of impervious surface cover

The amount and intensity of runoff on catchment scale are strongly determined by the presence of impervious land-cover types, which are the predominant cover types in urbanized areas. This paper examines the impact of different methods for estimating impervious surface cover on the prediction of peak discharges, as determined by a fully distributed rainfall-runoff model (WetSpa), for the upper part of the Woluwe River catchment in the southeastern part of Brussels. The study shows that detailed information on the spatial distribution of impervious surfaces, as obtained from remotely sensed data, produces substantially different estimates of peak discharges than traditional approaches based on expert judgment of average imperviousness for different types of urban land use. The study also demonstrates that sub-pixel estimation of imperviousness may be a useful alternative for more expensive high-resolution mapping for rainfall-runoff modelling at catchment scale

A travel time-based variable grid approach for an activity-based cellular automata model

Urban growth and population growth are used in numerous models to determine their potential impacts on both the natural and the socio-economic systems. Cellular automata (CA) land-use models became popular for urban growth modelling since they predict spatial interactions between different land uses in an explicit and straightforward manner. A common deficiency of land-use models is that they only deal with abstract categories, while in reality, several activities are often hosted at one location (e.g. population, employment, agricultural yield, nature…). Recently, a multiple activity-based variable grid CA model was proposed to represent several urban activities (population and economic activities) within single model cells. The distance-decay influence rules of the model included both short- and long-distance interactions, but all distances between cells were simply Euclidean distances. The geometry of the real transportation system, as well as its interrelations with the evolving activities, were therefore not taken into account. To improve this particular model, we make the influence rules functions of time travelled on the transportation system. Specifically, the new algorithm computes and stores all travel times needed for the variable grid CA. This approach provides fast run times, and it has a higher resolution and more easily modified parameters than the alternative approach of coupling the activity-based CA model to an external transportation model. This paper presents results from one Euclidean scenario and four different transport network scenarios to show the effects on land-use and activity change in an application to Belgium. The approach can add value to urban scenario analysis and the development of transport- and activity-related spatial indicators, and constitutes a general improvement of the activity-based CA model