Showing papers by "Tommaso Mansi published in 2010"

LogDemons revisited: consistent regularisation and incompressibility constraint for soft tissue tracking in medical images

Abstract: Non-linear image registration is a standard approach to track soft tissues in medical images. By estimating spatial transformations between images, visible structures can be followed over time. For clinical applications themodel of transformationmust be consistentwith the properties of the biological tissue, such as incompressibility. LogDemons is a fast non-linear registration algorithm that provides diffusion-like diffeomorphic transformations parameterised by stationary velocity fields. Yet, its use for tissue tracking has been limited because of the ad-hoc Gaussian regularisation that prevents implementing other transformation models. In this paper, we propose a mathematical formulation of demons regularisation that fits into LogDemons framework. This formulation enables to ensure volume-preserving deformations by minimising the energy functional directly under the linear divergence-free constraint, yielding little computational overhead. Tests on synthetic incompressible fields showed that our approach outperforms the original logDemons in terms of incompressible deformation recovery.The algorithmshowed promising results on one patient for the automatic recovery of myocardium strain from cardiac anatomical and 3D tagged MRI.

Atlas-based reduced models of blood flows for fast patient-specific simulations

Abstract: Model-based interpretation of the complex clinical data now available (shape, motion, flow) can provide quantitative information for diagnosis as well as predictions. However such models can be extremely time consuming, which does not always fit with the clinical time constraints. The aim of this work is to propose a model reduction technique to perform faster patient-specific simulations with prior knowledge built from simulations on an average anatomy. Rather than simulating a full fluid problem on individual patients, we create a representative 'template' of the artery shape. A full flow simulation is carried out only on this template, and a reduced model is built from the results. Then this reduced model can be transported to the individual geometries, allowing faster computational analysis. Here we propose a preliminary validation of this idea. A well-posed framework based on currents representation of shapes is used to create an unbiased template of the pulmonary artery for 4 patients with Tetralogy of Fallot. Then, a reduced computational fluid dynamics model is built on this template. Finally, we demonstrate that this reduced model can represent a specific patient simulation.

Cardiac motion estimation using a proactive deformable model: evaluation and sensitivity analysis

Abstract: To regularize cardiac motion recovery from medical images, electromechanical models are increasingly popular for providing a priori physiological motion information. Although these models are macroscopic, there are still many parameters to be specified for accurate and robust recovery. In this paper, we provide a sensitivity analysis of a proactive electromechanical model-based cardiac motion tracking framework by studying the impacts of its model parameters. Our sensitivity analysis differs from other works by evaluating the motion recovery through a synthetic image sequence with known displacement field as well as cine and tagged MRI sequences. This analysis helps to identify which parameters should be estimated from patient-specific data and which ones can have their values set from the literature.

[In-silico models for the simulation and prediction of the cardiac function].

Abstract: 1 Modèles Numériques pour la Simulation et la Prédiction de la Fonction Cardiaque In-Silico Models for the Simulation and Prediction of the Cardiac Function T. Mansi*, M. Sermesant*, H. Delingette*, X. Pennec*, N. Ayache*, Y. Boudjemline** *INRIA Sophia Antipolis – Méditerranée, Projet de recherche Asclepios, 2004 Route des Lucioles – BP 93, 06902 Sophia Antipolis Cedex, Tel : +33.4.92.38.71.57, Fax : +33.4.92.38.76.69, Courriel : Tommaso.Mansi@sophia.inria.fr ** Service de cardiologie pédiatrique, Hôpital Necker-Enfants Malades, Paris, France.

Third generation disease models: Image analysis tools, pediatric heart diseases, inflammatory diseases and brain tumors

Abstract: Welcome to the Health-e-Child deliverable D11.4. Following the web-based format initiated for previous deliverables of WP11, this public deliverable is giving the last update on the state of our disease models from a high level point of view. These web-pages update and replace the ones of deliverable D11.3. The document is organised in 5 parts: the first three parts corresponding to the current status of each task of the work-package, organised by disease. Then, two part describe the more generic image registration and shape analysis tools developped in the project.

Statistical Analysis of the Anatomy: From Digital Patient to Digital Population

Abstract: During the past ten years, the biophysical modelling of the human body has been a topic of increasing interest in the field of biomedical image analysis. The aim of such modelling is to formulate personalized medicine where a digital model of an organ can be adjusted to a patient from clinical data. This virtual organ would enable to estimate the parameters which are difficult to quantify in clinical routine, such as pressure, and to test computer-based therapies that can predict the evolution of the organ over time and with therapy. Nevertheless, in order to be able to translate such an approach to clinical practice, there is a crucial demand for robust statistical methods for studying multiple cases in a patient population, in order to be able to understand the effect of different clinical factors on the anatomy and extract the significant phenomena. Such statistical analyses can both provide a predictive model and guide the biophysical approach. However, computing statistics on such complex objects (i.e. 3D shapes) is very challenging. It was traditionally relying on point based discretisation of the shapes where the point-to-point correspondence is an important limiting factor for the usability of the method. New approaches were recently developed to compute such statistics without this limitation [1], and we present in this paper an application of these along with an open source tool made available through the VPH Network of Excellence Toolkit that allows multiple patients to be compared and analysed using this statistical method. The tools can be downloaded from http://www-sop.inria.fr/asclepios/projects/Health-e-Child/ShapeAnalysis/index.php.

Image-driven computational models of the heart for tetralogy of fallot

Abstract: In the last decades, researchers have been striving to develop computational models of the beating heart. Shifting from model generality to patient specificity, recent studies are demonstrating the potential impacts of such models on the clinical workflow. This chapter introduces three image-driven computational models of the heart that combine statistical and physiological priors for diagnosis, prognosis and therapy planning in repaired tetralogy of Fallot (rToF), a severe congenital heart disease. We first illustrate how physiological priors about the cardiac mechanics make the estimation of myocardium strain more reli- able, thus improving disease diagnosis. An algorithm that automatically tracks the heart along image sequences is constrained to estimate elastic and incompressible deformations, two fundamental properties of the myocardium. Then, we estimate a generative model of the right ventricular (RV) remodelling in rToF patients for disease prognosis. Computed us- ing statistical shape analyses and partial least squares, the model suggested that the dilation, the basal bulging and the apical dilation typically observed in these patients appear progres- sively as the child grows. These findings could support the cardiologist in predicting the evolution of the pathology for planning pulmonary valve replacement (PVR), the current state-of-the-art therapy in rToF. Finally, we introduce an electromechanical (EM) model of the heart for personalised planning of PVR with RV volume reduction in two patients. The EM model simulates the main features of the beating heart. After personalisation, the virtual heart is used to simulate PVR. As expected, the predicted postoperative function significantly improved in both patients. As illustrated by these results, combining medical