Direction des Relations Européennes et Internationales (DREI)

Programme INRIA "Equipes Associées"

A printable PDF document of the proposal is available here 

NEW : Bilan 2008

I. DEFINITION

II. BILAN 2007

Rapport scientifique pour l'année 2007

1. Seminar and presentations

Abstract: We formalize the pair-wise registration problem in a maximum a posteriori (MAP) framework that employs a multinomial model of joint intensities with parameters for which we only have a prior distribution. To obtain an MAP estimate of the aligning transformation alone, we treat the multinomial parameters as nuisance parameters, and marginalize them out. If the prior on those is uninformative, the marginalization leads to registration by minimization of joint entropy. With an informative prior, the marginalization leads to minimization of the entropy of the data pooled with pseudo observations from the prior. In addition, we show that the marginalized objective function can be optimized by the Expectation-Maximization (EM) algorithm, which yields a simple and effective iteration for solving entropy-based registration problems. Experimentally, we demonstrate the effectiveness of the resulting EM iteration for rapidly solving a challenging

Abstract: We describe the Active Mean Fields algorithm, a new approach for estimating the posterior probability of compartments in images. Conventional likelihood models are combined with a curve length prior on boundaries, and an approximate posterior distribution on labels is sought via the mean field approach. Optimizing the resulting estimator by gradient descent leads to a level set style algorithm where the level set functions are the logarithm of odds encoding of the posterior label probabilities in an unconstrained linear vector space. Applications with more than two labels are easily accommodated. The label assignment is accomplished by the Maximum A Posteriori rule, so there are no problems of "overlap'' or "vacuum". We test the method on synthetic images with additive noise. In addition, we segment a magnetic resonance scan into the major brain compartments and subcortical structures.

3. Nicholas Ayache gave several talk in Boston during his scientific visit:

• 23 August: Harvard : Biorobotics Lab
• 7 September: CSAIL at MIT
• 28 September: Electrical Engineering Seminars at Harvard
• 01 October: Monthly Radiology Seminar at Brigham and Women's hospital
• 10 October: Martinos Center for Biomedical Imaging at Mass General Hospital

Abstract: Medical image analysis brings about a revolution to the medicine of the 21st century, introducing a collection of powerful new tools designed to better assist the clinical diagnosis and to model, simulate, and guide more efficiently the patient's therapy. A new discipline has emerged in computer science, closely related to others like computer vision, computer graphics, artificial intelligence and robotics. In this talk, I describe the increasing role of computational models of anatomy and physiology to guide the interpretation of complex series of medical images, and illustrate my presentation with three applications: 1) statistical modeling and analysis of sulcal lines on the brain cortex; 2) modeling and simulation of brain tumors evolution; 3) electro-mechanical modeling of the heart function for therapy planning and simulation. I conclude with some promising trends, including the analysis of in vivo confocal microscopic images.

Abstract: Computational models of the human body can simulate the behavior of organs, biological systems or pathologies. These models allow for a synthetic representation of the evolution of biological phenomena, in the form of a limited number of equations and parameters. In this presentation, we will show how computational model of brain tumors could be used to adapt existing therapeutic strategies. More specifically, we will present the macroscopic model of brain that have been developed in the Asclepios team at INRIA. After the description of the underlying partial differential equation driving the model, we will show how to couple this model with medical images in order to derive relevant information that could be used for personalized diagnosis and therapy.

•     DTI Processing

1. We propose an algorithm for the diffeomorphic non-linear registration of DTI. Previous diffusion tensor registration algorithms suffer from the difficulties in computing the differential of the Finite-Strain (FS) tensor-reorientation strategy and therefore the gradient of the objective function. In contrast, we borrow results from the projective geometry literature in computer vision to derive an analytical gradient of the registration objective function. By leveraging on the closed-form gradient and the velocity field representation of a subgroup of diffeomorphism, the resulting registration algorithm is an extension of the recently introduced fast diffeomorphic Demons algorithm to diffusion tensor images. Implemented under the Insight Toolkit (ITK) framework, registration of a pair of 128x128x60 diffusion tensor volumes takes about 20 minutes. To the best of our knowledge, this is the first fully non-linear and diffeomorphic registration of diffusion tensor images using the finite-strain reorientation model. Scientific cummunications are expected in MICCAI 2008 and IEEE TMI.
   
   
2. Several nonrigid registration algorithms have been proposed for inter-subject alignment, used to construct statistical atlases and to identify group differences. Assessment of the accuracy of nonrigid registration algorithms is a essential and complex issue due to its intricate framework and its application-dependent behavior. We demonstrate that the diffusion MRI provides an independent means of assessing the quality of alignment achieved on the structural MRI. Diffusion tensor MRI enables the comparison of the local position and orientation of regions that appear homogeneous in conventional MRI. We carried out inter-subject alignment of conventional T1-weighted MRI with three different registration algorithms. Consequently, we projected DT-MRI of each subject through the same inter-subject transformation. The quality of the inter-subject alignment is assessed by estimating the consistency of the aligned DT-MRI using the Log- Euclidean framework. More details ca be found in [6].

•     Image Guided Neurosurgery

1. In this initial pilot study, 11 neurosurgical procedures were prospectively enrolled. This registration scheme uses image features to establish correspondence between images. It estimates a smooth geometric distortion compensation field by regularizing the displacements estimated at the correspondences. A patient-specific linear elastic material model is employed to achieve the regularization. We compared the alignment between the pre-operative and intra-operative imaging using (1) only a rigid registration without correction of the geometric distortion, and (2) a rigid registration and compensation for the geometric distortion. We evaluated the success of the geometric distortion correction algorithm by measuring the Hausdorff distance between boundaries in the 3T MRI and the 0.5T MRI after rigid registration alone, and with the addition of geometric distortion correction of the 0.5T MRI. Overall, the mean magnitude of the geometric distortion measured on the intra-operative images is 10.3mm, with a minimum of 2.91mm and a maximum of 21.5mm. The measured accuracy of the geometric distortion compensation algorithm is 1.93mm. There is a statistically significant difference between the accuracy of the alignment of pre- with intra-operative images, with and without the correction of geometric distortion (p < 0.001). More details ca be found in [1].
   
   
2. The usefulness of neurosurgical navigation with current visualizations is seriously compromised by brain shift, which inevitably occurs during the course of the operation, significantly degrading the precise alignment between the pre-operative MR data and the intra-operative shape of the brain. Our objectives were (i) to evaluate the feasibility of non-rigid registration that compensates for the brain deformations within the time constraints imposed by neurosurgery, and (ii) to create augmented reality visualizations of critical structural and functional brain regions during neurosurgery using pre-operatively acquired fMRI and DT-MRI. MATERIALS AND METHODS: Eleven consecutive patients with supratentorial gliomas were included in our study. All underwent surgery at our intra-operative MR imaging-guided therapy facility and have tumors in eloquent brain areas (e.g. precentral gyrus and cortico-spinal tract). Functional MRI and DT-MRI, together with MPRAGE and T2w structural MRI were acquired at 3 T prior to surgery. SPGR and T2w images were acquired with a 0.5 T magnet during each procedure. Quantitative assessment of the alignment accuracy was carried out and compared with current state-of-the-art systems based only on rigid registration. RESULTS: Alignment between pre-operative and intra-operative datasets was successfully carried out during surgery for all patients. Overall, the mean residual displacement remaining after non-rigid registration was 1.82 mm. There is a statistically significant improvement in alignment accuracy utilizing our non-rigid registration in comparison to the currently used technology (p<0.001). CONCLUSIONS: We were able to achieve intra-operative rigid and non-rigid registration of (1) pre-operative structural MRI with intra-operative T1w MRI; (2) pre-operative fMRI with intra-operative T1w MRI, and (3) pre-operative DT-MRI with intra-operative T1w MRI. The registration algorithms as implemented were sufficiently robust and rapid to meet the hard real-time constraints of intra-operative surgical decision making. The validation experiments demonstrate that we can accurately compensate for the deformation of the brain and thus can construct an augmented reality visualization to aid the surgeon. More details ca be found in [3].

•     Tumor Growth Modeling

1. Tracking gliomas dynamics on MRI has became more and more important for therapeutic management. Powerful computational tools have been recently developed in this context enabling in silico growth on a virtual brain that can be matched with real 3D segmented evolution through registration between atlases and patient brain MRI data. In this work, we provide an extensive review of existing algorithms for the three computational tasks involved in patient-specific tumor modeling: image segmentation, image registration, and in silico growth modelling (with special emphasis on the proliferation-diffusion model). Accuracy and limits of the reviewed algorithms are systematically discussed. Finally applications of these methods for both clinical practice and fundamental research are also discussed. More details ca be found in [2].


2. In cancer treatment, understanding the aggressiveness of the tumor is essential in therapy planning and patient follow-up. In this work, we present a novel method for quantifying the speed of invasion of gliomas in white and grey matter from time series of magnetic resonance (MR) images. The proposed approach is based on mathematical tumor growth models using the reaction-diffusion formalism. The quantification process is formulated by an inverse problem and solved using anisotropic fast marching method yielding an efficient algorithm. It is tested on a few images to get a first proof of concept with promising new results. More details ca be found in [4].


3. Bridging the gap between clinical applications and mathematical models is one of the new challenges of medical image analysis. In this work, we propose an ecient and accurate algorithm to solve anisotropic Eikonal equations, in order to link biological models using reaction-diusion equations to clinical observations, such as medical images. The example application we use to demonstrate our methodology is tumor growth modeling. We simulate the motion of the tumor front visible in images and give preliminary results by solving the derived anisotropic Eikonal equation with the recursive fast marching algorithm. More details ca be found in [5].

1. Ender Konukoglu is involved in the writing of a book chapter with Killian Pohl "Meningiomas: A Comprehensive Text". The chapter will describe a state of the art software tool targeted towards the identification of tumor growth in meningioma patients. The tool detects growth by analysing differences between consecutive Magnetic Resonance (MR) scans of a tumor patient.

Rapport financier 2007

(*) Ajouter ou supprimer des lignes au tableau ci-dessus de façon à faire figurer tous les organismes ayant contribué au financement de l'équipe associée

Bilan des échanges effectués en 2007

1. Seniors

(1) DR / CR / professeur
(2)colloque, thèse, stage, visite....

2. Juniors

(1) post-doc / doctorant / stagiaire
(2)colloque, thèse, stage, visite....  

III. BILAN2008

Rapport scientifique de l'année 2008

•     DTI and image registration

1. We propose an algorithm for the diffeomorphic non-linear registration of DTI. Previous diffusion tensor registration algorithms suffer from the difficulties in computing the differential of the Finite-Strain (FS) tensor-reorientation strategy and therefore the gradient of the objective function. In contrast, we borrow results from the projective geometry literature in computer vision to derive an analytical gradient of the registration objective function. By leveraging on the closed-form gradient and the velocity field representation of a subgroup of diffeomorphism, the resulting registration algorithm is an extension of the recently introduced fast diffeomorphic Demons algorithm to diffusion tensor images. Implemented under the Insight Toolkit (ITK) framework, registration of a pair of 128x128x60 diffusion tensor volumes takes about 20 minutes. To the best of our knowledge, this is the first fully non-linear and diffeomorphic registration of diffusion tensor images using the finite-strain reorientation model. More details can be found in [34]. A journal article presenting this work is expected in 2009.


2. We present the fast Spherical Demons algorithm for registering two spherical images. By exploiting spherical vector spline interpolation theory, we show that a large class of regularizers for the modified demons objective function can be efficiently implemented on the sphere using convolution. Based on the one parameter subgroups of diffeomorphisms, the resulting registration is diffeomorphic and fast – registration of two cortical mesh models with more than 100k nodes takes less than 5 minutes, comparable to the fastest surface registration algorithms. Moreover, the accuracy of our method compares favorably to the popular FreeSurfer registration algorithm. We validate the technique in two different settings: (1) parcellation in a set of in-vivo cortical surfaces and (2) Brodmann area localization in ex-vivo cortical surfaces. More details can be found in [35].

•     Lesion growth assessment and modeling

1. The emergence of new modalities such as Diusion Tensor Imaging (DTI) is of great interest for the characterization and the temporal study of Multiple Sclerosis (MS). DTI indeed gives information on water diusion within tissues and could therefore reveal alterations in white matter bers before being visible in conventional MRI. However, recent studies generally rely on scalar measures derived from the tensors such as FA or MD instead of using the full tensor itself. Therefore, a certain amount of information is left unused. We present a framework to study the benets of using the whole diusion tensor information to detect statistically signicant dierences between each individual MS patient and a database of control subjects. This framework, based on the comparison of the MS patient DTI and a mean DTI atlas built from the control subjects, allows us to look for dierences both in normally appearing white matter but also in and around the lesions of each patient. We present a study on a database of 11 MS patients, showing the ability of the DTI to detect not only signicant dierences on the lesions but also in regions around them, enabling an early detection of an extension of the MS disease. More details can be found in [36].


2. Glioblastoma multiforma (GBM) is one of the most aggressive tumors of the central nervous system. It can be represented by two components: a proliferative component with a mass effect on brain structures and an invasive component. GBM has a distinct pattern of spread showing a preferential growth in the white fiber direction for the invasive component. By using the architecture of white matter fibers, we propose a new model to simulate the growth of GBM. This architecture is estimated by diffusion tensor imaging in order to determine the preferred direction for the diffusion component. It is then coupled with a mechanical component. To set up our growth model, we make a brain atlas including brain structures with a distinct response to tumor aggressiveness, white fiber diffusion tensor information and elasticity. In this atlas, we introduce a virtual GBM with a mechanical component coupled with a diffusion component. These two components are complementary, and can be tuned independently. Then, we tune the parameter set of our model with an MRI patient. We have compared simulated growth (initialized with the MRI patient) with observed growth six months later. The average and the odd ratio of image difference between observed and simulated images are computed. Displacements of reference points are compared to those simulated by the model. The results of our simulation have shown a good correlation with tumor growth, as observed on an MRI patient. Different tumor aggressiveness can also be simulated by tuning additional parameters. This work has demonstrated that modeling the complex behavior of brain tumors is feasible and will account for further validation of this new conceptual approach. More details can be found in [37].


3. The advent of magnetic resonance imaging (MRI) has allowed the follow-up of tumor growth by precise volumetric measurements. Such information about tumor dynamics is, however, usually not fully integrated in the therapeutic management, and the assessment of tumor evolution is still limited to qualitative description. In parallel, computational models have been developed to simulate in silico tumor growth and treatment efficacy. Nevertheless, direct clinical interest of these models remains questionable, and there is a gap between scientific advances and clinical practice. In this paper, WHO grade II glioma will serve as a paradigmatic example to illustrate that computational models allow characterizing tumor dynamics from serial MRIs. The role of these dynamics for both therapeutic management and biological research will be discussed. More details can be found in [38].


4. Change detection is a critical task in the diagnosis of many slowly evolving pathologies. We describe an approach that semi-automatically performs this task using longitudinal medical images. We are specifically interested in meningiomas, which experts often find difficult to monitor as the tumor evolution can be obscured by image artifacts. We test the method on synthetic data with known tumor growth as well as ten clinical data sets. We show that the results of our approach highly correlate with expert findings but seem to be less impacted by inter- and intra-rater variability. More details can be found in [39].

[34] Boon Thye Thomas Yeo, Tom Vercauteren, Pierre Fillard, Xavier Pennec, Polina Golland, Nicholas Ayache, and Olivier Clatz. DTI Registration with Exact Finite-Strain Differential. In Proceedings of the IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI'08), Paris, France, May 2008. IEEE.

[35] Boon Thye Thomas Yeo, Mert Sabuncu, Tom Vercauteren, Nicholas Ayache, Bruce Fischl, and Polina Golland. Spherical Demons: Fast Surface Registration. In Dimitris Metaxas, Leon Axel, Gabor Fichtinger, and Gábor Székely, editors, Proc. Medical Image Computing and Computer Assisted Intervention (MICCAI'08), volume 5241 of Lecture Notes in Computer Science, New York, USA, pages 745-753, September 2008. Springer-Verlag.

[36] Olivier Commowick, Pierre Fillard, Olivier Clatz, and Simon K. Warfield. Detection of DTI White Matter Abnormalities in Multiple Sclerosis Patients. In Dimitris Metaxas and Leon Axel, editors, Proc. Medical Image Computing and Computer Assisted Intervention (MICCAI'08), volume 5241 of Lecture Notes in Computer Science, New York, USA, pages 975-982, September 2008. Springer-Verlag.

[37] Pierre-Yves Bondiau, Olivier Clatz, Maxime Sermesant, Pierre-Yves Marcy, Hervé Delingette, Marc Frenay, and Nicholas Ayache. Biocomputing: numerical simulation of glioblastoma growth using diffusion tensor imaging.. Phys Med Biol, 53(4):879-93, February 2008.

[38] Emmanuel Mandonnet, Johan Pallud, Olivier Clatz, Hugues Duffau, and Laurent Capelle. Computational Modelling of the WHO Grade II Glioma Dynamics: Principles and Applications to Surgical Strategy. Neurosurgical Review, 2008.

[39] E. Konukoglu, W.M. Wells, S. Novellas, N. Ayache, R. Kikinis, Black P,M, and K.M. Pohl. Monitoring Slowly Evolving Tumors. In Proceedings of the IEEE Internation Symposium on Biomedical Imaging: From Nano to Macro (ISBI'08), Paris, France, May 2008.

Rapport financier 2008

Justifiez en quelques lignes l'utilisation des crédits et en particulier une utilisation partielle du budget alloué.

(*) Ajouter ou supprimer des lignes au tableau ci-dessus de façon à faire figurer tous les organismes ayant contribué au financement de l'équipe associée

Bilan des échanges effectués en 2008

1. Chercheurs Seniors

(1) DR / CR / professeur
(2)colloque, thèse, stage, visite....
(3) précisez l'unité (mois, semaine..)

2. Juniors

(1) post-doc / doctorant / stagiaire
(2)colloque, thèse, stage, visite....
(3) précisez l'unité (mois, semaine..)

IV. PREVISIONS 2009

Programme de travail

• Simulate the tumor growth on larger dataset. Through this series of experiments, we want:
  • to demonstrate the relevance of the model for the simulation of various diffusive tumors. The objective is to show that different tumor behaviors can be simulated by the proposed equation.
  • to evaluate the predictive power of the model. Based on 3 times steps evolution of the tumor on the same patient, we want to show that parameters that fit 2 times points could be used to predict the evolution of the tumor measured at the 3rd time point.
• Develop new algorithms for the evaluation of model parameters using multiple images. The idea here is to solve the inverse problem consisting in finding the optimal parameters that best match the growth observed in the data. Such algorithms could then be used to make statistics on the distribution of parameters among patients.
• Use new imaging modalities to assess the quality of the predicted tumor cell density, given by the model. In the perspective, MR spectroscopie seems to represent an important source of data for our model validation.

• The development of new brain constitutive equations taking into account the mechanical influence of blood vessels and the auto-regulation of blood pressure. We also want to study the influence of diuretics agents like Mannitol on the rheology of the brain tissue.
• The implementation of new resection method, taking advantage of the SOFA architecture to uncouple the mechanical computation from the visualization computation.

Budget prévisionnel 2009

1. Echanges

2. Cofinancement

Bjoern Menze will be partially funded by Harvard during his extended scientific at INRIA.

3. Demande budgétaire

[7] O. Clatz, H. Delingette, I.-F. Talos, A. J. Golby, R. Kikinis, F. A. Jolesz, N. Ayache, and S. K. Warfield. Robust Non-Rigid Registration to Capture Brain Shift from Intra-Operative MRI. IEEE Transactions on Medical Imaging, 24(11):1417-1427, Nov. 2005. 
[8] O. Clatz, H. Delingette, I.-F. Talos, A. J. Golby, N. Ayache, R. Kikinis, F. Jolesz, and S. K. Warfield. Hybrid Formulation of the Model-Based Non-Rigid Registration Problem to Improve Accuracy and Robustness. In J. Duncan and G. Gerig, editors, Proceedings of MICCAI'05, volume 3750 of LNCS, pages 295-302, October 2005. Springer Verlag.
[9] O. Clatz, H. Delingette, I.F. Talos, A. Golby, N. Ayache, R. Kikinis, F. Jolesz, and S. Warfield. Robust Nonrigid Registration to Capture Brain Shift from Intraoperative MRI. In 5th Interventional MRI Symposium, Cambridge, MA. USA, October 2004.
[10] O. Clatz, M. Sermesant, P.-Y. Bondiau, H. Delingette, S. K. Warfield, G. Malandain, and N. Ayache. Realistic Simulation of the 3D Growth of Brain Tumors in MR Images Coupling Diffusion with Mass Effect. IEEE Transactions on Medical Imaging, 24(10):1334-1346, October 2005. 
[11] O. Clatz, P.Y. Bondiau, H. Delingette, G. Malandain, M. Sermesant, S. K. Warfield, and N. Ayache. In Silico Tumor Growth: Application to Glioblastomas.. In C. Barillot, D.R. Haynor, and P. Hellier, editors, Proc. of the 7th Int. Conf on Medical Image Computing and Computer-Assisted Intervention - MICCAI 2004 (2), volume 3217 of LNCS, Saint-Malo, France, pages 337-345, September 2004. Springer Verlag.
[12] C. Wagner, O. Clatz, R. Feller, D. Perrin, H. Delingette, N. Ayache, and R. Howe. Integrating Tactile and Force Feedback with Finite Element Models. In International Conference on Robotics and Automation (ICRA'05), Barcelona, April 2005.
[13] H. J. Park, M. Kubicki, M. E. Shenton, A. Guimond, R. W. McCarley, S. E. Maier, R. Kikinis, F. A. Jolesz, C.-F. Westin Spatial Normalization of Diffusion Tensor MRI Using Multiple Channels Neuroimage 2003
[14] A. Guimond, C. R. G. Guttmann, S. K. Warfield, C.-F. Westin Deformable registration of DT-MRI data based on transformation invariant tensor characteristics  ISBI, Washington (DC), USA 2002
[15] K. M. Pohl, W. M. Wells III, A. Guimond, K. Kasai, Martha Elizabeth Shenton, Ron Kikinis, W. Eric L. Grimson, Simon K. Warfield: Incorporating Non-rigid Registration into Expectation Maximization Algorithm to Segment MR Images. MICCAI (1) 2002: 564-571
[16] S.K. Warfield, A. Guimond, A. Roche, A. Bharata, A. Tei, F. Talos, J. Rexilius, J. Ruiz-Alzola, C.-F. Westin, S. Haker, S. Angenent, A. Tennembaum, F. Jolesz, and R. Kikinis. Advanced Nonrigid Registration Algorithms for Image Fusion. A. Toga and J. Mazziotta (Eds.) Brain Warping (2nd ed.), Academic Press, pp 661-690, 2002.
[17] K. Krissian, J. Ellsmere, K. Vosburgh, R. Kikinis, and C. -F. Westin Multiscale Segmentation of the Aorta in 3D Ultrasound Images 25th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society, EMBS, Cancun Mexico, pages 638-641, Sep 2003.
[18] J. Dauguet, S. Peled, V. Berezovskii, T. Delzescaux, S. K. Warfield, R. Born, C.-F. Westin 3D Histological Reconstruction of Fiber Tracts and Direct Comparison with Diffusion Tensor MRI Tractography. In Proc. MICCAI 2006: Ninth International Conference Medical Image Computing and Computer-Assisted Intervention, Lecture Notes in Computer Science. Springer-Verlag, pp 109-116 2006
[19] F. Ségonne, J.P. Pons, E. Grimson and B. Fischl "Active Contours Under Topology Control - Genus Preserving Level Sets," biomedical imaging workshop, I.C.C.V. 2005
[20] Warfield SK, Guimond A, Roche A, Bharatha A, Tei A, Talos F, Rexilius J, Ruiz-Alzola J, Westin, CF, Haker S, Angenent S, Tannenbaum A, Jolesz FA, Kikinis R. Advanced Nonrigid Registration Algorithms for Image Fusion. In: Toga AW, Mazziotta JC. Brain Mapping: The Methods. San Diego: Academic Press;2002. p. 661-690.
[21] J.-P. Thirion. Image matching as a diffusion process: an analogy with Maxwell's demons. Medical Image Analysis, 2(3):243-260, 1998.
[22] Vincent Arsigny, Pierre Fillard, Xavier Pennec, and Nicholas Ayache. Log-Euclidean Metrics for Fast and Simple Calculus on Diffusion Tensors. Magnetic Resonance in Medicine, 56(2):411-421, August 2006. 
[23] L . Zöllei, E. Learned-Miller, E. Grimson, W.M. Wells III: "Efficient Population Registration of 3D Data" (Best Paper Award), ICCV 2005, Computer Vision for Biomedical Image Applications; Beijing, China
[24] Olivier Clatz. Modèles biomécaniques et physio-pathologiques pour l'analyse d'images cérébrales. Thèse de sciences, École des Mines de Paris, February 2006.
[25] E. Konukoglu, O.Clatz, P.-Y. Bondiau, H. Delingette, N. Ayache. Extrapolating Tumor Invasion Margins for Physiologically Determined Radiotherapy Regions, In Proc. MICCAI 2006: Ninth International Conference Medical Image Computing and Computer-Assisted Intervention, Lecture Notes in Computer Science. Springer-Verlag.
[26] N. Archip, O. Clatz, S. Whalen, D. Kacher, F. Jolesz, A. Golby, P. M. Black, S. K. Warfield. Non-rigid alignment of preoperative MRI, fMRI, and DT-MRI with intra-operative MRI for enhanced visualization and navigation in image-guided neurosurgery  Neuroimage. Note: under revision.
[27] P. Novotny, J. Stoll, N. Vasilyev, P. Del Nido, P. Dupont, R. Howe. GPU Based Real-Time Instrument Tracking with Three Dimensional Ultrasound. In Proc. MICCAI 2006: Ninth International Conference Medical Image Computing and Computer-Assisted Intervention, Lecture Notes in Computer Science. Springer-Verlag.
[28] S. Cotin. Modèles anatomiques déformables en temps réel : Application à la simulation de chirurgie avec retour d'effort. Thèse de sciences, Université de Nice Sophia-Antipolis, November 1997.
[29] G. Picinbono. Modèles géométriques et physiques pour la simulation d'interventions chirurgicales. Thèse de sciences, université de Nice Sophia-Antipolis, February 2001.
[30] C. Forest. Simulation de chirurgie par coelioscopie : contributions à l'étude de la découpe volumique, au retour d'effort et à la modélisation des vaisseaux sanguins. PhD thesis, École Polytechnique, March 2003.
[31] O. Clatz, S. Litrico, H. Delingette, and N. Ayache. Dynamic Model of Communicating Hydrocephalus for Surgery Simulation. IEEE Transactions on Biomedical Engineering, 2006. Note: Accepted.
[32] Wells WM, Viola P, Atsumi H, Nakajima S, Kikinis R. Multi-modal volume registration by maximization of mutual information. Medical Image Analysis. 1996;1:35--52.
[33] A Bayesian Model for Joint Segmentation and Registration. Pohl K, Fisher J, Grimson WEL, Kikinis R, Wells W. Neuroimage (to appear)

NIH P41 RR12318 (PI: Ron Kikinis) 08/01/03-07/31/08. NIH $2,741,685 Neuroimaging Analysis Center. The Neuroimaging Analysis Center (NAC) is a National Research Resource Center operating in an application-oriented, clinical environment with the mission of computer-science based technology research and development.

NIH U41RR019703 (PI: Ferenc Jolesz) 06/01/05-05/30/10. NIH $1,999,973 Image Guided Therapy Center This project will establish a NCRR Resource Center for Image-Guided therapy at Brigham and Women’s Hospital Harvard Medical School. The Center, based on the existing BWH Image-Guided Therapy Program, will develop, maintain, and make available innovative technologies in five Core Research Programs of Image-Guided Therapy: (1) Bioengineering and Imaging; (2) Surgical Planning (3) MRI-Guided Therapy; (4) Thermal Ablations; and (5) Focused Ultrasound Surgery.