Section: New Results
Tumor Growth Modeling
Participants : Erin Stretton [Correspondant] , Emmanuel Mandonnet, Bjoern Menze, Hervé Delingette, Nicholas Ayache.
This work was funded by Care4me, EU program.
DTI, MRI, simulation, clinical, tumor, brain, glioma
We aim at developing image analysis methods and biophysical models in order to guide the planning of therapies (surgical removal and radiotherapy) for brain cancer (glioma) patients. Our work is focused on those objectives :
Predicting the location of glioma recurrence after a resection surgery  .
Generation of Synthetic but Visually Realistic Time Series of Cardiac Images Combining a Biophysical Model and Clinical Images
Participants : Adityo Prakosa [Correspondant] , Maxime Sermesant, Hervé Delingette, Stéphanie Marchesseau, Eric Saloux [CHU Caen] , Pascal Allain [Philips Healthcare] , Nicolas Villain [Philips Healthcare] , Nicholas Ayache.
This work was done in collaboration with Medisys, Philips Healthcare Suresnes, France, and the Cardiology Department of CHU Caen, France. This work was partially supported by the European Research Council through the ERC Advanced Grant MedYMA on Biophysical Modelling and Analysis of Dynamic Medical Images and the European project euHeart.
synthetic 4D cardiac sequences, cardiac electromechanical model, non-rigid registration
A pipeline to create visually realistic synthetic 4D cardiac sequence using the cardiac motion simulated by an electromechanical model is developed. This pipeline combines the simulated myocardium displacement field with the estimated myocardium displacement field from a registration method. This combined displacement field is then used to warp the original images in order to create the synthetic cardiac sequence.
In  ,we proposed a new approach based on Stationary Velocity Fields to combine the two motions (see Figure 8 ). We also proposed a new method that diffuses displacement fields in order to maintain the continuity between the simulation and the real image with minimal texture distortion. Thanks to the detailed interplay between image processing and biophysical modeling, we can fully use a complete sequence in order to generate several new ones. This method also gives better realism compared to traditional methods based on the deformation of an end-diastolic image, since the generated synthetic sequence will also contain the motion of surrounding tissues such as the motion of the mitral valve.
The new synthetic images are similar to the original ones except for the motion of the heart which is modified to follow the motion provided by a biophysical model. The parameters of the biophysical model can be modified to create variations around this motion. This pipeline has been applied to generate different synthetic sequences from different imaging modalities. It is generic and can be used with a different biophysical model or a different image registration algorithm, and it can be extended to other organs.
As these synthetic 4D cardiac sequences have kinematic ground truth information, those sequences represent in themselves a valuable resource to benchmark motion tracking methods or to train machine-learning algorithm.
Real-Time Cardiac Electrophysiology Computing for Training Simulator
Participants : Hugo Talbot [Correspondant] , Hervé Delingette, Stephane Cotin, Maxime Sermesant, Christian Duriez.
This work was funded by the ADT Sofa and is conducted in collaboration with project teams Shacra and Evasion.
Cardiac electrophysiology simulation, real-time, GPU computing, patient-specific study
Cardiac arrhythmia is a very frequent pathology related to an abnormal electrical activity in the myocardium. This work aims at developing a training simulator for interventional radiology and thermo-ablation of these arrhythmias.
The latest improvements lead on electrophysiology simulation (see Figure 9 ) using GPU computing allowed us to reach real-time performance . The issue of fast electrophysiology was a major bottleneck in the development of our simulator.
Coupling between the cardiac electrophysiology model with cardiac mechanical models has been achieved, thus leading to an interactive framework. Moreover, the electrophysiology simulation has been also coupled with a navigation simulation.
In the context of his work on cardiac electrophysiology, we initiated two different collaborations. Joint work has been performed with the team CARMEN from Inria Bordeaux on bidomain modeling for cardiac electrophysiology. This exchange targeted the implementation in SOFA of these models. Secondly, a collaboration with the MACS team in Saclay has been initiated to personalize the cardiac electrophysiology model based on the Verdandi library.
Personalized model of the heart for cardiac therapy planning
Participants : Stéphanie Marchesseau [Correspondant] , Hervé Delingette, Nicholas Ayache, Maxime Sermesant.
An award has been won for this work at the MICCAI 2012 Conference. It was partially funded by the European Community's euHeart project under grant agreement 224495 and by the ERC advanced Grant MedYMA 291080.
Cardiac simulation, sensitivity analysis, calibration algorithm, specificity study
We ran a complete sensitivity analysis to check its behaviour for healthy and pathological cases  .
Image-based glioma modeling for radiotherapy planning
Participants : Bjoern Menze [Correspondant] , Ender Konukoglu [MSR Cambridge] , Jan Unkelbach [Harvard MGH] .
Cardiac Arrhythmia Radio-frequency Ablation Planning
Participants : Rocio Cabrera Lozoya [Correspondant] , Maxime Sermesant, Hervé Delingette, Nicholas Ayache.
This work is performed in the context of the the PhD of Rocio Cabrera Lozoya in collaboration with the IHU LIRYC Bordeaux and is funded by ERC MedYMA.
Structural and functional characterization of target sites using 3D imaging and EP measurements through machine learning algorithms (see Figure 11 ).
Computational modeling of radiofrequency ablation for the planning and guidance of abdominal tumor treatment
Participants : Chloe Audigier [Correspondant] , Herve Delingette, Tommaso Mansi [Siemens Corporate Research] , Nicholas Ayache.
This PhD is carried out between Asclepios research group, Inria Sophia Antipolis, France and the Image Analytics and Informatics global field, Siemens Corporate Research, Princeton, USA.
Therapy planning, radio-frequecy ablation, Liver
The objective of this work is to develop a computational framework for patient-specific planning of radiofrequency ablation:
Tumor Growth Simulation for the creation of a database of virtual patients
Participants : Nicolas Cordier [Correspondant] , Nicholas Ayache, Hervé Delingette, Bjoern Menze, Ezequiel Geremia.
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, Tumor simulation.
Learning approach for the Mechanical personalization of cardiac models
Participants : Loic Le Folgoc [Correspondant] , Hervé Delingette, Antonio Criminisi, Nicholas Ayache.
This work was partly funded by Microsoft Research through its PhD Scholarship Programme and by the ERC Advanced Grant MedYMA.
Inverse problem, machine learning, patient-specific, current, kinematics
Published at the MICCAI 2012 Workshop on Medical Computer Vision
Brain Tumor Growth Modeling
Participants : Matthieu Lê [Correspondant] , Nicholas Ayache, Hervé Delingette.
Gliomas simulations, reaction-diffusion, brain tumors
In collaboration with the MC2 research team in Bordeaux, we developed a tumor growth model based on different types of cell : necrotic, proliferative and quiescent cells (see Figure 14 ). It is also based on the underlying vascularization of the brain.
Modeling of atrophy of the brain in Alzheimer's Disease
Participants : Bishesh Khanal [Correspondant] , Xavier Pennec, Nicholas Ayache.
Alzheimer's Disease (AD), modeling atrophy, biomechanical model
The idea is to have a model which produces deformation of the brain when a known distribution of local volume change (atrophy) is prescribed to the model. The study is to understand how brain deformation evolve in time with respect to temporal and spatial variation of atrophy.