Overall Objectives
Research Program
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
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Section: New Results

Computational Physiology

Biophysical Modeling and Simulation of Longitudinal Brain MRIs with Atrophy in Alzheimer's Disease

Participants : Bishesh Khanal [correspondent] , Nicholas Ayache, Xavier Pennec.

This work has been partly supported by the European Research Council through the ERC Advanced Grant MedYMA (on Biophysical Modeling and Analysis of Dynamic Medical Images).

Alzheimer's Disease (AD), modeling brain deformation, biophysical model, simulation

Figure 13. An example of obtained deformation field (top) from the model for the prescribed atrophy (bottom). From left to right: Axial, Coronal and Sagittal views.

Glioblastoma : Study of the vasogenic edema

Participants : Matthieu Lê [correspondent] , Hervé Delingette, Jan Unkelbach [Massachussetts General Hospital] , Nicholas Ayache.

This work is carried out between Ascelpios research group, Inria Sophia Antipolis, France and the Department of Radiation Oncology of the Massachusetts General Hospital, Boston, USA.

Glioblastoma, Vasogenic Edema, Radiotherapy, Target Delineation

Figure 14. Comparison of the dose distribution including the vasogenic edema (clinical practice) and the excluding the estimated vasogenic edema (proposed method).

Image-based Prediction of Cardiac Ablation Targets

Participants : Rocio Cabrera Lozoya [correspondent] , Maxime Sermesant, Nicholas Ayache.

Electrophysiology, ablation planning, machine learning

Ventricular radio-frequency ablation (RFA) can have a critical impact on preventing sudden cardiac arrest but is challenging due to a highly complex arrhythmogenic substrate. We aim at identifying local image characteristics capable of predicting the presence of local abnormal ventricular activities (LAVA). This could lead to pre-operatively and non-invasively improve and accelerate the procedure.

Figure 15. Pipeline showing the processing of our multimodal data. It includes an image feature extraction phase, followed by a classification with uncertainty assessment stage. The rightmost panel shows the preliminary output result format for a clinical environment.

Personalised Canine Electromechanical Model of the Heart

Participants : Sophie Giffard-Roisin [correspondent] , Maxime Sermesant, Hervé Delingette, Stéphanie Marchesseau, Nicholas Ayache.

This work has been supported by the European Project FP7 under grant agreement VP2HF (no 611823) and the ERC Advanced Grant MedYMA (on Biophysical Modeling and Analysis of Dynamic Medical Images).

Cardiac Modelling, Personalised Simulation, Electrical and Mechanical Simulation

Figure 16. Complete electromechanical pipeline used for the simulations of four healthy canine hearts.

Computational modeling of radiofrequency ablation for the planning and guidance of abdominal tumor treatment

Participants : Chloé Audigier [correspondent] , Hervé Delingette, Tommaso Mansi, Nicholas Ayache.

This PhD is carried out between Asclepios research group, Inria Sophia Antipolis and the Image Analytics and Informatics global field, Siemens Corporate Research, Princeton, USA.

Radio Frequency Abation, Patient-Specific Simulation, Lattice Boltzmann Method, Computational Fluid Dynamics, Heat Transfer, Therapy Planning, Liver

Radiofrequency abation (RFA) is a minimally invasive therapy suited for liver tumor ablation. However a patient-specific predicitive tool to plan and guide the treatment is required. We developed a computational framework for patient-specific planning of RFA (see Fig.17 ) :

Then this forward model is used to estimate patient-specific model parameters as presented in the ABDI workshop at MICCAI 2014 [12] . This work presented obtained the best paper award of the workshop.

Figure 17. Steps of the proposed method for parameters personnalisation (blue: input, green: processes, purple: output).
Figure 18. Predicted necrosis compared qualitatively well with ground truth (necrosis zone observed on a post-operative image).

Multi-channel patch-based glioma segmentation

Participants : Nicolas Cordier [correspondent] , Hervé Delingette, Nicholas Ayache.

Part of this work was funded by the European Research Council through the ERC Advanced Grant MedYMA (on Biophysical Modeling and Analysis of Dynamic Medical Images).

Brain, MRI, Glioma, Patch-based Segmentation, Tumor Simulation

The segmentation of glioblastoma, the most severe case of brain tumors, is a crucial step for diagnostic assessment and therapy planning. In order to perform the manual delineation of the tumor compartments, the clinicians have to concurrently screen multi-channel 3D MRI, which makes the process both time-consuming and subject to inter-expert delineation variability. We are building upon the patch-based segmentation framework, the state-of-the-art for the segmentation of healthy brain structures, to present automatic glioma segmentation algorithms. Our 2013 submission to the MICCAI Brain Tumor Segmentation Challenge has been improved by:

Figure 19. 2D slices of randomly sampled 3D multi-channel patches. From left to right: the different MR channels (T1, T2, T2-FLAIR, contrast enhanced T1). From top to bottom: cerbrospinal fluid (CSF), grey matter (GM), white matter (WM), necrotic tumor core, edema, non-enhancing tumor core (NETC), and active rim.