Statistical evaluation and modeling of human brain variability

This study is part of the partnership between the Epidaure team and the LONI (Laboratory of Neuro Imaging) at Los Angeles. While our team has been focusing on developping tools adapted to the analysis and registration of multidimensional and multimodal medical images, the LONI has been leading studies to build cerebral atlases of specific diseases (e.g. Alzheimer, schizophrenia) and thus has been collecting anatomical images for years. Today, this amount of images consitutes a unique databasis of more than 500 T1 MRI. Moreover, each images comes with an accurate delineation of 32 pairs of sulci, each sulcal line being segmented by experts who followed a meticulous protocol.

Basically, this work aims at learning the global brain variability from the variability of sulcal lines. The size of the databasis being statistically significant, this work could lead to new results in neuro-anatomy. In the one hand, a better knowledge of the brain variability among a population could help to early detect neurological pathologies and in the other hand, providing a map of the variability could help to guide non-rigid registration algorithms. Indeed, if we are able to predict in which direction each position of the brain statiscally moves, we will be able to better constrain registration algorithms. This work makes an intensive use of a large panel of scientific domains: computer vision, image analysis, statistics and differential geometry to quote just a few.

See the collaboration Epidaure-LONI for further informations.

Pierre Fillard, Vincent Arsigny and Xavier Pennec.

Brain shift modeling

It is usually the case in neurosurgery that pre-operative planning is based on the assumption that anatomical structures do not move between the image acquisition time and the operation time. In reality, the position of brain tissues changes during the operation and significantly decreases the accuracy of the planning made on the pre-operative image.

We propose a biomechanical approach based on a finite element model (FEM) of the brain to model this deformation. It takes into account the patient specificity and anatomical cerebral structures to predict the brain deformation.

This study is based on the analysis of 7 cases of Parkinson operations, realized at La Pitié Salpétrière hospital (Paris). It focuses on modelling the static effects of gravity on the brain deformation after dura-mater opening, visible on the post-operative MRI

More details can be found here

Olivier Clatz

Tensor computing

The emergence of diffusion tensor imaging (DTI) in the medical imaging community led to challenges in mathematics in order to manipulate 2nd order symmetric positive-defined matrices, so called tensors. In DT-MRI, each voxel of the brain contains a tensor, which is the 2nd order approximation of the brownian motion of water at this specific location. As water tends to move along oriented tissues (such as white matter neural fibers) diffusion tensors are likely to be aligned with the underlying structures. Due to the noise corrupting the data, the tensor field needs adequate post-processing before any further analysis. However, one cannot manipulate tensors like scalars (the tensor space is not a vector space). We used results in differential geometry to manipulate tensors while ensuring to remain on the tensor space, i.e. to keep the positive-defined constrain verified at all time. Applications are: tensor field regularization (PDE, etc.) and shape statistics (see the collaboration Epidaure-LONI).

A research report is available here.

Pierre Fillard and Xavier Pennec.

In-silico tumor growth

We propose to model and simulate the growth of glioblastomas, the most aggressive of the glial tumors. The proposed simulation is based on a model coupling the invasion of the glioblastoma and its mechanical interaction with the invaded structures. This model uses a reaction-diffusion equation for the tumor expansion characterization and the usual continuum mechanics laws for the brain parenchyma behavior. In addition, we propose a new equation to couple these two equations to take into account the mechanical influence of the tumor cells on the invaded tissues.

Our model relies upon an anatomical atlas including cerebral structures having a distinct response to the tumor aggression. In addition, we included in this atlas the information from the Diffusion Tensor Images (DTI) to model the tumor preferential growth in the white fibers direction. Finally, the tumor growth model is used to simulate a virtual GBM grow into a patient brain. This in-silico growth is compared to the real GBM growth observed with a second patient MRI taken 6 months later.

More details can be found here

Olivier Clatz

Rigid and non-rigid registration for motion compensation and subject comparison in fMRI studies

This research theme is implemented within the framework of an INRIA "development action", a concerted action between different INRIA and other French teams aimed at producing new software. The goal is to adapt and evaluate the different non rigid registration algorithms already developed at INRIA, mainly at EPIDAURE, ODYSSEE and VISTA, in order to fulfill the needs of fMRI investigators, in our case the CEA/SHFJ/DRM and INSERM U494 (IMQ) teams.
Three main problems are investigated:

• Motion compensation during an fMRI acquisition sequence. It is important to compensate very accurately (or at least detect) for the subject motion between images as a very small registration error may lead to spurious activation detections.
• Inter-subject registration to fuse functional information from different subjects. There is currently no satisfying method to establish point to point correspondences between two brains. The main idea developed here is to enrich iconic (i.e. intensity-based) methods with geometric constraints based on anatomical sulcal correspondences.
• The validation of this inter-subject registration. As there is no ground truth, the evaluation of the previous algorithms is very difficult. One interesting idea is to estimate the variability of anatomically stable functional activations across subjects and to show the the inter-subject registration drastically reduce this variability, hopefully removing all the anatomical variability to leave only the functional one.

More information on the action IRMf home page.

Xavier Pennec

Elaboration of a three-dimensional, functional and registrable atlas of the human basal ganglia

In order to allow accurate pre-operative localisation of functional targets in functional neurosurgery, we aim at constructing a three dimensional registrable cartography of the basal ganglia, based on histology. For doing this, a {\em post mortem} MR study was conducted on a cadaver's head, and the brain was then extracted and processed for histology. The post mortem MR image will allow to report the cartography on the patient's anatomy, by its registration with the patient's MR image.
    Elaboration of this atlas consists in co-registering the histological and {\em post mortem} MR data of the same subject. First, realignment of the histological sections into a reliable three dimensional volume is performed. Then the reconstructed volume is registered with the post mortem MR image. To insure three dimensional integrity of the histological reconstructed volume, a reference volume is first constructed from photographs of the unstained surface of the frozen brain. This reference is then used as an intermediate volume for, on the one hand, independant alignment of each histological section with its corresponding optical section and on the other hand, three dimensional registration with the post mortem MR image.
    This work is part of a collaboration between the U.289 group at INSERM (Paris) and the Neuroradiology Dept. of La Salpetriere hospital (Paris). It was partially supported by Medtronic Inc. (Denver, USA).

Some publications are available.

Eric Bardinet

Augmented Reality for Abdominal Surgery

In order to improve surgical accuracy, radiologists and surgeons can use a new imaging tool : Augmented Reality. It deals with superimposing a virtual model built from the pre-operative image on a video image of the patient . In collaboration with IRCAD (Institut de Recherche contre les Cancers de l'Appareil Digestif), we aim at creating an augmented reality system for the destruction of liver tumors with the use of radio-fequency. The purpose is to superimpose on extern video images several tridimensional reconstructions of the liver, the tumors and some other organs. We have developed a new point-based 3D/2D registration method and a visualisation software which lead us to our first result (see image). The 3D models come from segmented CT-scans and the registration is made thanks to radio-opaque fiducials on the patient's skin. We now have to validate the efficiency and accuracy of this method on porcins and mannequin.

Stéphane Nicolau

Automated Segmentation of Anatomical Structures in Brain MRI

Effective identification and labeling of anatomical structures in Magnetic Resonance Imaging (MRI) proves to be challenging, given the wide variety of shapes and intensities each structure can present. Automated image segmentation can be used to assist medical analysis, and calls for high precision as the quality of the diagnosis often depends on how accurately those structures can be identified. Similarly, brain atlases, which can provide a precise quantitative framework for multimodality brain mapping, turn out to be rather tedious to build, as many components typically have to be interactively outlined. Thus, automated segmentation system are powerful tools to help in drawing consistent diagnosis from a number of images, to classify pictorial data, or collect statistical information on anatomical variability.
   In collaboration with the Laboratory of NeuroImaging, at the UCLA School of Medicine in Los Angeles, within the framework of "équipe associée à l'étranger", we propose to develop a fully automated segmentation system to extract deep and surface anatomical structures from brain MRI. We approach the issue of boundary finding as a process of fitting a series of statistically controlled deformable templates to the target contour.
   Our goal is to determine and analyze the features of template matching algorithms that play a determining role in terms of their efficiency and accuracy.

Alain Pitiot

Cardiac Inverse Problem

Creation of conductivity maps for the heart using electropotential recordings on the thorax.

sjaume

Registration of post mortem macroscopic optical brain slice images with MR scans of the same patient

In this study, we develop a method for registration of macroscopic optical images with MR images of the same patient. This forms a key part of a series of procedures to allow post mortem findings to be accurately registered with MR images, and more generally provides a method for 3D mapping of the distribution of pathological changes throughout the brain. The first stage of the method involves a 3D reconstruction of 2D brain slices. The second stage consists in the registration of the reconstructed volume with corresponding MR images.
    This work is part of the QAMRIC european project.

Some publications are available.

Eric Bardinet

Automatic Detection and Quantification of Evolving Processes in 3D Medical Images

This work has been conducted mainly for the analysis of MR images of patients with Multiple Sclerosis. The goal (de circonstance!) is the design of new tools to help the clinicians to detect and locate new lesions, and also to measure objectively their evolution through time. To achieve this, we develop several approches (cf. publications). This work is done in collaboration with Harvard Medical School (Pr. Ron Kikinis) and with CHU of Nice (Pr. Chatel, Dr. C. Lebrun). Our work on automatic detection and quantification of evolving lesions deals essentially with:

• preprocessings on MRI series (registration, intensity corrections, interpolation ...)
• analysis of MRI evolutions. 2 methods have been developped: one based on the computation and on the analysis of a vector field of apparent displacement between 2 MRIs; the other based on a statistical analysis of the intensity evolution of each anatomical point over time.

A demonstration is also available.

David Rey

Electromechanical Model of the Heart

We are working on an electromechanical model of the heart, simple enough to be used in the deformable model framework, in order to extract some ventricular function parameters from cardiac image sequences.
We compute the action potential electrical wave propagation using the FitzHugh-Nagumo approach and this potential triggers the muscle contraction, through an electromechanical coupling based on the Hill-Maxwell model.
This model interacts with 4D cardiac images through external forces applied on the surface nodes and computed from the image features. We expect that the a priori information provided by the model will help segment sparse and noisy images.
Moreover, this model could help simulate some electrical and mechanical pathologies.

A gallery and publications are available.

Maxime Sermesant

Motion Estimation and Analysis in Echocardiographic data

The purpose of this work is the estimation and analysis of displacement fields of the myocardium. The motion will be estimated in 2D and 3D echocardiographic images with variational techniques. The analysis is performed with level sets methods. First estimations were obtained with 2D echocardiographic images using Doppler Tissue Imaging. Then motion analysis will be improved by using a priori information such as a biomechanical model of the heart.

Valérie Moreau

A posteriori validation of pre-operative planning in functional neurosurgery by quantification of brain pneumocephalus

In MR image guided brain surgery, the techniques are often based on the use of volumetric pre-operative MR acquisitions. This implicitely assumes that a pre-operative acquisition gives a faithful and precise representation of the brain anatomy during the intervention. Thus a major limit of these techniques is the development of brain deformation during the surgical intervention, leading to anatomical differences, which can be significative, with the pre-operative MR images.
    One example of this type of procedures is functional neurosurgery for Parkinson's disease. This intervention is based on the stereotactic introduction of electrodes in a small, deeply located, nucleus of the brain, called the subthalamic nucleus, which is targeted on pre-operative stereotactic MR acquisitions. During the intervention, which is performed outside the MR unit, an electrophysiological and clinical study is performed with the electrodes to check the pre-operatively determined target position. This exploration is time-consuming and can lead to the development of a pneumocephalus (presence of air in the intracanial cavity) because of CSF leak. This induces a brain shift which can yield a significative deformation of the entire brain and thus cause potential errors in the pre-operatively determined position of the stereotactic targets.
    We have developped in collaboration with the Neuroradiology Dept. of La Salpetriere hospital (Paris) a method to quantify brain deformation from pre and immediate post-operative MR acquisitions, based on rigid and non rigid registrations. The novel non rigid registration algorithm used in this study is based on a true free form deformation modelisation coupled with a mixed regularization. This method will allow to quantify the deformation occurred around the stereotactic targets and lead to a posteriori validation of the pre-operative planning.

Some publications are available.

Eric Bardinet

Accuracy and robustness of point-surface rigid registration techniques - Application to computer guided oral implantology.

This work is part of an industrial computer-guided surgery project dedicated to oral implantology surgery. Briefly, The operation is planned on a pre-operative CT-Scan and the purpose of such a system is to help the dentist to drill the implant in the predefined position and orientation.

The DentalNavigator system, developed by AREALL, is a per-operative system based on registration. In the CT-Scan image, the teeth and jaw bone surfaces are easily segmented resulting in about 100000 triangulated points. Points on the same structures are measured on the patient resulting in about 10000 unstructured points. After the registration of the 2 surfaces, the system can visually guides the surgeon to the planned position and orientation for drilling.

We investigated several points-surfaces registration techniques and developped tools to compare their speed, robustness and accuracy. We formalised the well-known ICP algorithm and its numerous variants in a statistical framework, and finaly focused on an EM variant and proved it has a multi-scale behaviour. Using a coarse-to-fine approach we resolved robustness problems while improving its accuracy. Using a simple decimation technique, we down-sampled our sets of points and increased the algorithm speed while preserving its properties. Current work is going on the theoretical prediction of algorithm accuracy and robustness.

Another part of this work is about the probabilistic modeling of noisy curves and surfaces. We are currently trying to explain the tensor-voting techniques developped by Medioni and his team, and hope to develop a statistical framework useful for surface reconstruction, fusion and registration.

Sébastien Granger

Reconstruction of a 3-D volume from a series of 2-D images and fusion with a 3-D imaging modality

Within the EC funded project MAPAWAMO we have developed a set of tools that allows us to first reconstruct a 3-D volume (consistent in geometry and in intensity) from a series of 2-D sections (e.g. autoradiographs, histological sections, ...). The geometry of such a 3-D may not exactly match the true anatomy of the imaged material. To address this issue, we have also develop a specific methodology that allows to fuse this reconstruction with a 3-D image (e.g. a pre-mortem MR image).

Some related publications can be retrieved through our bibliography pages.

Contact: Grégoire Malandain

SPECT imaging in Alzheimer's Disease

Alzheimer's disease (AD) is a neuro-degenerative disease that produces among others memory loss, behavioral changes and cognitive impairment. It affects mainly elderly. Because of the aging populations the number of patients will probably rise in the coming years. Therefor powerful diagnostical tools are increasingly important for this disease. Single photon emission computed tomography (SPECT) is being largely used for the study of cerebral blood flow (CBF). These studies provide unique information for the identification of functional abnormalities relevant to Alzheimer's disease. In collaboration with Professor J. Darcourt of the University of Nice, we are working on the development of diagnosis assisting tools for this type of images. Most of our tools work by comparing images to images of which we already know the diagnosis, and are statistically based.

Jonathan Stoeckel

Soft Tissue Modeling for Surgery Simulation

In collaboration with IRCAD (Institut de Recherche Contre le Cancer de l'Appareil Digestif) located in Strasburg, we have developped over the past seven years different software technology for the simulation of soft tissue deformation for the simulation of surgery. Surgical simulation aims at training medical resident or surgeons for the practice of video-surgery by providing a software and hardware platform where essential surgical gestures such as suturing, cutting and resection can be simulated in real-time.
We have developped a simulator for the resection of the liver that includes soft tissue deformation and force-feedback. Among the different scientific difficulties raised by this project, we have focused on the real-time computation of linear and non-linear elastic soft tissue (with transversal anisotropy) but also on the geometric and mechanical aspects of tissue cutting simulation.

Clement Forest and Hervé Delingette

Performance evaluation and validation of registration algorithms

The performances of an automatic registration algorithm can be characterized by the accuracy of the result (when the algorithm has converged to the right solution), and the probability of convergence to this correct solution (robustness). Most often, the registration algorithm needs an initial transformation and one prefers to determine a convergence basin in the space of initial transformations where the probability of convergence to the right solution is close to 100%. Within the accuracy, one can further distinguish between repeatability (or precision) (the error due to the presence of multiple local minima in the immediate vicinity of the optimal solution) and the external error due to the noise on the data.

The purpose of this research theme is to construct the tools to evaluate the performances of registration algorithms on a database of images representative of a clinical application. One of the special aspects of this work is the development of methods to evaluate a lower bound on the registration accuracy without any gold standard, using consistency studies via registration loops.

Some publications are available.

Xavier Pennec

Quantitative parameters extraction for vascular networks in 3D images

The purpose of this study is to analyse vascular networks which are recorded using 3D confocal microscopy or other suitable 3D imaging techniques. This includes analysis of the network's topology, morphometry of individual vessels, and statistical analysis of morphometric properties on the network. To perform such analysis, we must extract a suitable discrete representation of the vascular network from the image data.

This research takes place in the Microvisu3D Project

Céline Fouard and Grégoire Malandain