Dynamic Model of Communicating Hydrocephalus for Surgery Simulation

We propose a dynamic model of cerebrospinal fluid circulation and intracranial pressure regulation. In this model, we investigate the coupling of biological parameters with a 3D model, to improve the mechanical behavior of the brain in surgical simulators. The model was assessed by comparing the simulated ventricular enlargement evolution with a patient case study of communicating hydrocephalus. In our model, cerebro-spinal fluid production-resorption system is coupled with a 3D representation of the brain parenchyma. We introduce a new bi-phasic model of the brain tissue allowing for fluid exchange between the brain extracellular space and the venous system. The time evolution of ventricular pressure has been recorded on a symptomatic patient after closing the ventricular shunt. A finite element model has been built based on a CT scan of this patient, and quantitative comparisons between measures and simulated data are proposed

Personalization of Reaction-Diffusion Tumor Growth Models in MR Images: Application to Brain Gliomas Characterization and Radiotherapy Planning

International audienceReaction-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, specifically anatomical and diffusion images, in their formulation. On the other hand, the adaptation of the general model to the specific patient cases has not been studied thoroughly yet. In this chapter we address this adaptation. This chapter is a short summary of the articles (Konukoglu 2009a), (Konukoglu 2009b) and the thesis (Konukoglu 2009c) that we have submitted recently. In the first part, we describe a parameter estimation method for reaction-diffusion tumor growth models using time series of medical (Magnetic Resonance) images. This method estimates the patient specific parameters of the model using the images of the patient taken at different successive time instances. In the second part of the chapter we focus on an application of the personalized models aimed to improve the tumor targeting in radiation therapy. Specifically we address the problem of limited visualization of medical images. We describe a method for extrapolating the invisible infiltration margins of gliomas in the MR images and the usage of these margins in constructing irradiation margins taking into account the growth dynamics of the tumor. Finally for both parts we show preliminary results demonstrating the power and the potential benefits of the personalizatio

Tumor Growth Parameters Estimation and Source Localization From a Unique Time Point: Application to Low-grade Gliomas

International audienceCoupling time series of MR Images with reaction-di usion-based models has provided interesting ways to better understand the proliferative-invasive as- pect of glial cells in tumors. In this paper, we address a di erent formulation of the inverse problem: from a single time point image of a non-swollen brain tumor, estimate the tumor source location and the di usivity ratio between white and grey matter, while exploring the possibility to predict the further extent of the observed tumor at later time points in low-grade gliomas. The synthetic and clinical results show the stability of the located source and its varying distance from the tumor barycenter and how the estimated ratio controls the spikiness of the tumor

Dynamic Model of Communicating Hydrocephalus for Surgery Simulation

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Reconstruction 3D des structures anatomiques des membres inférieurs

National audienceDans cet article, nous nous intéressons à la modélisation des structures anatomiques des membres inférieurs telles que les os, les muscles et les tendons. La méthode proposée commence par une acquisition d'images IRM durant laquelle les membres inférieurs d'un sujet sont scannés. Des modèles 3D sont ensuite générés après une segmentation manuelle des structures anatomiques. Cependant, la surface des modèles générés n'est pas lisse. De plus, les modèles ne sont pas attachés alors qu'ils devraient l'être anatomiquement. Nous décrivons donc les différentes étapes pour contraindre les modèles à être corrects au niveau anatomique et nous discutons de leur validation. L'objectif de cette méthode est de pouvoir réutiliser ces modèles dans des méthodes de segmentation automatique

Asclepios: a Research Project-Team at INRIA for the Analysis and Simulation of Biomedical Images

International audienceAsclepios1 is the name of a research project-team o cially launched on November 1st, 2005 at INRIA Sophia-Antipolis, to study the Analysis and Simulation of Biological and Medical Images. This research project-team follows a previous one, called Epidaure, initially dedicated to Medical Imaging and Robotics research. These two project teams were strongly supported by Gilles Kahn, who used to have regular scienti c in- teractions with their members. More generally, Gilles Kahn had a unique vision of the growing importance of the interaction of the Information Technologies and Sciences with the Biological and Medical world. He was one of the originators of the creation of a speci c BIO theme among the main INRIA research directions, which now regroups 16 di fferent research teams including Asclepios, whose research objectives are described and illustrated in this article

Vers un patient numérique personnalisé pour le diagnostic et la thérapie guidés par l'image [Towards a personalized digital patient for diagnosis and therapy guided by image].

National audienceRecent advances in computer science and medical imaging allow the design of new computational models of the patient which are used to assist physicians. These models, whose parameters are optimized to fit in vivo acquired images, from cells to an entire body, are designed to better quantify the observations (computer aided diagnosis), to simulate the evolution of a pathology (computer aided prognosis), to plan and simulate an intervention to optimize its effects (computer aided therapy), therefore addressing some of the major challenges of medicine of 21(st) century

Advancing Intra-operative Precision: Dynamic Data-Driven Non-Rigid Registration for Enhanced Brain Tumor Resection in Image-Guided Neurosurgery

During neurosurgery, medical images of the brain are used to locate tumors and critical structures, but brain tissue shifts make pre-operative images unreliable for accurate removal of tumors. Intra-operative imaging can track these deformations but is not a substitute for pre-operative data. To address this, we use Dynamic Data-Driven Non-Rigid Registration (NRR), a complex and time-consuming image processing operation that adjusts the pre-operative image data to account for intra-operative brain shift. Our review explores a specific NRR method for registering brain MRI during image-guided neurosurgery and examines various strategies for improving the accuracy and speed of the NRR method. We demonstrate that our implementation enables NRR results to be delivered within clinical time constraints while leveraging Distributed Computing and Machine Learning to enhance registration accuracy by identifying optimal parameters for the NRR method. Additionally, we highlight challenges associated with its use in the operating room

Comparison of Physics-Based Deformable Registration Methods for Image-Guided Neurosurgery

This paper compares three finite element-based methods used in a physics-based non-rigid registration approach and reports on the progress made over the last 15 years. Large brain shifts caused by brain tumor removal affect registration accuracy by creating point and element outliers. A combination of approximation- and geometry-based point and element outlier rejection improves the rigid registration error by 2.5 mm and meets the real-time constraints (4 min). In addition, the paper raises several questions and presents two open problems for the robust estimation and improvement of registration error in the presence of outliers due to sparse, noisy, and incomplete data. It concludes with preliminary results on leveraging Quantum Computing, a promising new technology for computationally intensive problems like Feature Detection and Block Matching in addition to finite element solver; all three account for 75% of computing time in deformable registration

Brain Tumor Growth Simulation

In the present report, we propose a new model to simulate the growth of glioblastomas multiforma (GBM), the most aggressive glial tumors. Because the GBM shows a preferential growth in the white fibers and have a distinct invasion speed with respect to the nature of the invaded tissue, we rely on an anatomical atlas to introduce this information into the model. This atlas includes a white fibers diffusion tensor information and the delineation of cerebral structures having a distinct response to the tumor aggression. We use the finite element method (FEM) to simulate both the invasion of the GBM in the brain parenchyma and its mechanical interaction (mass effect) with the invaded structures. The former effect is modeled with either a reaction-diffusion or a Gompertz equation depending on the considered tissue, while the latter is based on a linear elastic brain constitutive equation. In addition, we propose a new coupling equation taking into account the mechanical influence of the tumor cells on the invaded tissues. This tumor growth model is assessed by comparing the \textit{in-silico} GBM growth with the real GBM growth observed between two magnetic resonance images (MRIs) of a patient acquired with six months difference. The quality of the results shows the feasibility of modeling the complex behavior of brain tumors and will justify a further validation of this new conceptual approach