The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Nicholas Ayache - One of the best experts on this subject based on the ideXlab platform.
-
image guided personalization of reaction diffusion type tumor Growth Models using modified anisotropic eikonal equations
IEEE Transactions on Medical Imaging, 2010Co-Authors: Ender Konukoglu, Olivier Clatz, Bjoern H Menze, Bram Stieltjes, Marcandre Weber, Emmanuel Mandonnet, Herve Delingette, Nicholas AyacheAbstract:Reaction-diffusion based tumor Growth Models have been widely used in the literature for modeling the Growth of brain gliomas. Lately, recent Models have started integrating medical images in their formulation. Including different tissue types, geometry of the brain and the directions of white matter fiber tracts improved the spatial accuracy of reaction-diffusion Models. The adaptation of the general model to the specific patient cases on the other hand has not been studied thoroughly yet. In this paper, we address this adaptation. We propose a parameter estimation method for reaction-diffusion tumor Growth Models using time series of medical images. This method estimates the patient specific parameters of the model using the images of the patient taken at successive time instances. The proposed method formulates the evolution of the tumor delineation visible in the images based on the reaction-diffusion dynamics; therefore, it remains consistent with the information available. We perform thorough analysis of the method using synthetic tumors and show important couplings between parameters of the reaction-diffusion model. We show that several parameters can be uniquely identified in the case of fixing one parameter, namely the proliferation rate of tumor cells. Moreover, regardless of the value the proliferation rate is fixed to, the speed of Growth of the tumor can be estimated in terms of the model parameters with accuracy. We also show that using the model-based speed, we can simulate the evolution of the tumor for the specific patient case. Finally, we apply our method to two real cases and show promising preliminary results.
Frank Devlieghere - One of the best experts on this subject based on the ideXlab platform.
-
Growth no Growth Models of in vitro Growth of penicillium paneum as a function of thyme essential oil ph aw temperature
Food Microbiology, 2019Co-Authors: Els Debonne, An Vermeulen, Filip Van Bockstaele, Irena Soljic, Mia Eeckhout, Frank DevlieghereAbstract:Abstract The aims of this study were (i) screening of antifungal activity of thyme essential oil on Penicillium paneum; (ii) development of Growth/no-Growth Models (G/NG); and (iii) validation of the G/NG Models by performing bread baking trials. The screening method was based on the measurement of fungal Growth in a semi-solid medium through optical density. The combined influence of aw (0.88–0.97), pH (4.8–7.0), temperature (22 and 30 °C), time (0–144 h) and varying concentrations of thyme oil (0–2 μL/mL YES) were assessed. Growth of P. paneum at aw 0.88 was significantly reduced compared to aw 0.93–0.97. A slight pH effect was observed at aw 0.93; Growth was delayed at pH 6 compared to pH 4.8. The lowest concentration of thyme oil preventing Growth during 144 h of incubation was 1 μL/mL medium. According to the results of the shelf-life test of par-baked bread, fungal Growth was inhibited for more than 45 days using 0.3 mL thyme oil/100 g dough. To conclude, this study recognized the potential of using G/NG Models to develop better product formulations and to facilitate product innovation.
George Biros - One of the best experts on this subject based on the ideXlab platform.
-
where did the tumor start an inverse solver with sparse localization for tumor Growth Models
Inverse Problems, 2020Co-Authors: Shashank Subramanian, Klaudius Scheufele, Miriam Mehl, George BirosAbstract:We present a numerical scheme for solving an inverse problem for parameter estimation in tumor Growth Models for glioblastomas, a form of aggressive primary brain tumor. The Growth model is a reaction-diffusion partial differential equation (PDE) for the tumor concentration. We use a PDE-constrained optimization formulation for the inverse problem. The unknown parameters are the reaction coefficient (proliferation), the diffusion coefficient (infiltration), and the initial condition field for the tumor PDE. Segmentation of Magnetic Resonance Imaging (MRI) scans drive the inverse problem where segmented tumor regions serve as partial observations of the tumor concentration. Like most cases in clinical practice, we use data from a single time snapshot. Moreover, the precise time relative to the initiation of the tumor is unknown, which poses an additional difficulty for inversion. We perform a frozen-coefficient spectral analysis and show that the inverse problem is severely ill-posed. We introduce a biophysically motivated regularization on the structure and magnitude of the tumor initial condition. In particular, we assume that the tumor starts at a few locations (enforced with a sparsity constraint on the initial condition of the tumor) and that the initial condition magnitude in the maximum norm is equal to one. We solve the resulting optimization problem using an inexact quasi-Newton method combined with a compressive sampling algorithm for the sparsity constraint. Our implementation uses PETSc and AccFFT libraries. We conduct numerical experiments on synthetic and clinical images to highlight the improved performance of our solver over a previously existing solver that uses standard two-norm regularization for the calibration parameters. The existing solver is unable to localize the initial condition. Our new solver can localize the initial condition and recover infiltration and proliferation. In clinical datasets (for which the ground truth is unknown), our solver results in qualitatively different solutions compared to the two-norm regularized solver.
-
where did the tumor start an inverse solver with sparse localization for tumor Growth Models
arXiv: Medical Physics, 2019Co-Authors: Shashank Subramanian, Klaudius Scheufele, Miriam Mehl, George BirosAbstract:We present a numerical scheme for solving an inverse problem for parameter estimation in tumor Growth Models for glioblastomas, a form of aggressive primary brain tumor. The Growth model is a reaction-diffusion partial differential equation (PDE) for the tumor concentration. We use a PDE-constrained optimization formulation for the inverse problem. The unknown parameters are the reaction coefficient (proliferation), the diffusion coefficient (infiltration), and the initial condition field for the tumor PDE. Segmentation of Magnetic Resonance Imaging (MRI) scans from a single time snapshot drive the inverse problem where segmented tumor regions serve as partial observations of the tumor concentration. The precise time relative to tumor initiation is unknown, which poses an additional difficulty for inversion. We perform a frozen-coefficient spectral analysis and show that the inverse problem is severely ill-posed. We introduce a biophysically motivated regularization on the tumor initial condition. In particular, we assume that the tumor starts at a few locations (enforced with a sparsity constraint) and that the initial condition magnitude in the maximum norm equals one. We solve the resulting optimization problem using an inexact quasi-Newton method combined with a compressive sampling algorithm for the sparsity constraint. Our implementation uses PETSc and AccFFT libraries. We conduct numerical experiments on synthetic and clinical images to highlight the improved performance of our solver over an existing solver that uses a two-norm regularization for the calibration parameters. The existing solver is unable to localize the initial condition. Our new solver can localize the initial condition and recover infiltration and proliferation. In clinical datasets (for which the ground truth is unknown), our solver results in qualitatively different solutions compared to the existing solver.
Ender Konukoglu - One of the best experts on this subject based on the ideXlab platform.
-
image guided personalization of reaction diffusion type tumor Growth Models using modified anisotropic eikonal equations
IEEE Transactions on Medical Imaging, 2010Co-Authors: Ender Konukoglu, Olivier Clatz, Bjoern H Menze, Bram Stieltjes, Marcandre Weber, Emmanuel Mandonnet, Herve Delingette, Nicholas AyacheAbstract:Reaction-diffusion based tumor Growth Models have been widely used in the literature for modeling the Growth of brain gliomas. Lately, recent Models have started integrating medical images in their formulation. Including different tissue types, geometry of the brain and the directions of white matter fiber tracts improved the spatial accuracy of reaction-diffusion Models. The adaptation of the general model to the specific patient cases on the other hand has not been studied thoroughly yet. In this paper, we address this adaptation. We propose a parameter estimation method for reaction-diffusion tumor Growth Models using time series of medical images. This method estimates the patient specific parameters of the model using the images of the patient taken at successive time instances. The proposed method formulates the evolution of the tumor delineation visible in the images based on the reaction-diffusion dynamics; therefore, it remains consistent with the information available. We perform thorough analysis of the method using synthetic tumors and show important couplings between parameters of the reaction-diffusion model. We show that several parameters can be uniquely identified in the case of fixing one parameter, namely the proliferation rate of tumor cells. Moreover, regardless of the value the proliferation rate is fixed to, the speed of Growth of the tumor can be estimated in terms of the model parameters with accuracy. We also show that using the model-based speed, we can simulate the evolution of the tumor for the specific patient case. Finally, we apply our method to two real cases and show promising preliminary results.
Jacques Hébert - One of the best experts on this subject based on the ideXlab platform.
-
Distance-independent tree basal area Growth Models for Norway spruce, Douglas-fir and Japanese larch in Southern Belgium
European Journal of Forest Research, 2017Co-Authors: Jérôme Perin, Hugues Claessens, Philippe Lejeune, Yves Brostaux, Jacques HébertAbstract:This paper presents new harmonized distance-independent individual tree basal area Growth Models for Norway spruce, Douglas-fir and Japanese larch in pure even-aged stands in Southern Belgium. The selected model was originally developed for Norway spruce and Douglas-fir in neighboring France. New formulations are proposed for some of the model components in order to lower the number of fitted parameters and facilitate the fitting procedure. The resulting Models integrate the most recent corresponding top-height Growth Models and use four simple and usually collected explanatory variables: stand age, top-height, total basal area and tree girth at breast height. The modified formulations maintain similar fitting performances and make it easier to interpret the influence of the explanatory variables on tree Growth. Parameters estimates were fitted on thousands of Growth measurements gathered from several monitoring plots, forest management inventories and silvicultural field experiments that represent the wide range of site conditions and of forest management scenarios applied to coniferous stands in Southern Belgium. Cross-validation of the Models revealed no bias and highlighted their consistent behavior over the entire range of girth at breast height, age, top-height, site index and density represented in our dataset. Combining utility and robust performances, these Models represent useful forest management tools, purposely ideal for forest simulation software development. Moreover, the flexibility and generic capabilities of the model formulation should make it easily adjustable for other species in even-aged stands.
