Direction des Relations Internationales (DRI)

Programme INRIA "Equipes Associées"

 

BILAN TRIENNAL / THREE-YEAR REVIEW


Please fill in this review in ENGLISH


ASSOCIATE TEAM
 CompuTumor
selected in year
 2007
Projet INRIA : ASCLEPIOS Organisme étranger partenaire : HARVARD
Unité de recherche INRIA : INRIA SOPHIA ANTIPOLIS
Thème INRIA : BIO		 Pays : ETATS UNIS
 
 
Coordinateurs français
Coordinateur étranger
Nom, prénom Nicholas Ayache, PhD Olivier Clatz, PhD Simon Warfield, PhD
Grade/statut Directeur de Recherche Chargé de Recherche Associate Professor
Organisme d'appartenance
 INRIA Sophia Antipolis, Projet Asclepios Harvard Medical School, Computational Radiology Laboratory
Adresse postale INRIA Sophia Antipolis
Asclepios Research Project
2004 route des Lucioles - BP 93
06902 Sophia Antipolis Cedex
France 		Harvard Medical School
Computational Radiology Laboratory
Departments of Radiology, Children's Hospital
300 Longwood Avenue
Boston, MA 02115
USA
URL AYACHE CLATZ WARFIELD
Téléphone +33 4 92 38 76 61 +1-617-355-4566
Télécopie +33 4 92 38 76 69 +33 4 92 38 76 69 +1-617-582-6033
Courriel Nicholas.Ayache[at]sophia.inria.fr olivier.clatz[at]sophia.inria.fr warfield[at]crl.med.harvard.edu
Letter of support pdf

In addition to the two aforementioned research teams, this proposals aims at grouping together the effort in neuro-oncology of different laboratories in Boston and Nice. Details about all concerned teams can be found in Table I and II.

American Institution Brigham & Women's Hopital Harvard Mass General Hospital
Name William Wells, PhD Ron Kikinis, MD Robert Howe, PhD Thomas Deisboeck, MD
Academic Position Associate Professor Professor Gordon McKay Professor of Engineering Assistant Professor
Laboratory BWH, Surgical Planning Laboratory. Harvard, Biorobotics Laboratory MGH, Complex Biosystems Modeling Laboratory
Mailing adress Department of Radiology
Brigham and Women's Hospital
75 Francis St.
Boston, MA 02115
USA		 Biorobotics Laboratory
323 Pierce Hall, 29 Oxford Street
Cambridge, MA 02138,
USA		 Martinos Center for Biomedical Imaging,
MGH-East, Room 2301
Building 149, 13th Street
Charlestown, MA 02129
USA
URL WELLS KIKINIS HOWE DEISBOECK
Telephone 617-899-3772 617-732-7389 617-496-8359 617-724-1845
Fax 617-732-7963 617-732-7963 617-495-9837 617-726-5079
Email sw[at]csail.mit.edu kikinis[at]bwh.harvard.edu howe[at]deas.harvard.edu deisboec[at]helix.mgh.harvard.edu
Letter of support pdf pdf pdf
American Institution MIT CIMIT
Name Eric Grimson, PhD Polina Golland, PhD Steven Dawson, MD
Academic Position Bernard Gordon Professor of Medical Engineering Assistant professor Associate Professor
Laboratory MIT- Computer Science and Artificial Intelligence Laboratory CIMIT Simulation Group
Mailing adress MIT CSAIL
32 Vassar Street 32-D470
Cambridge, MA 02139
USA		 Simulation Group
65 Landsdowne Street
Cambridge MA 02139-4232
USA
URL GRIMSON GOLLAND DAWSON
Telephone 617-253-5346 617-253-8005 617-768-8781
Fax 617-258-6287 617-253-4640 617-768-8915
Email welg[at]csail.mit.edu polina[at]csail.mit.edu sdawson[at]partners.org
Letter of support pdf pdf
Table I. American partners involved in the proposal.
 
French Institution
CHU Pasteur
Centre Antoine Lacassagne - Nice
Name
 Stéphane Litrico, MD Philippe Paquis, MD Marc Frenay, MD Pierre-Yves Bondiau, MD, PhD
Academic Position
 Chef de clinique, PH Chef de service, PU PH Oncologue Radiothérapeute
Laboratory
 CHU Pasteur, service de Neurochirurgie Service d'Oncologie Médicale CAL - Departement de Radiothérapie
Mailing adress
 Service de Neurochirurgie - Hôpital Pasteur
30 avenue de la Voie Romaine 06000 Nice
France		 Service d'Oncologie Médicale
Centre Antoine Lacassagne
33 avenue de valombrose
06189, Nice, France		 Radiotherapy Department
Centre Antoine Lacassagne
33 avenue de valombrose
06189, Nice, France
URL LITRICO PAQUIS
Telephone +33 4 92 03 77 39 +33 4 92 03 84 50 +33 4 92 03 10 00 +33 4 92 03 12 61
Fax +33 4 92 03 85 28 +33 4 92 03 85 28 +33 4 92 03 10 46 +33 4 92 03 15 70
Email litrico[at]yahoo.com paquis.p[at]chu-nice.fr marc.frenay[at]nice.fnclcc.fr pierre-yves.bondiau[at]cal.nice.fnclcc.fr
Table II. French partners involved in the proposal.


La collaboration en bref / The Collaboration in brief

Titre de la thématique de collaboration / Title of the collaboration theme : Computational Brain Tumor

Summary : The CompuTumor project is dedicated to the study of brain tumor models and their coupling with medical images to better assist diagnosis and therapy. The project will strongly enhance the current collaborations between INRIA and a group of world leading teams with complementary technical and clinical expertise on these topics in Boston and Nice. More specifically, the project aims at (a) proposing new medical image processing method that could be used to better analize tumor images, (b) developping new brain tumor models and (c) developping algorithms to personalise these models with patient data.

BILAN SYNTHETIQUE DE LA COLLABORATION / SYNTHESIS OF THE COLLABORATION

  
INRIA
Nombre/Number
Partenaire(s)/ Partner
Nombre/Number
Chercheurs seniors impliqués Senior researchers involved  3 (Ayache, Clatz, Malandain) 4 (Golland, Wells, Deisboeck, Stieltjes) 
Post-doctorants Post-doctoral graduates    3 (Menze, Sabuncu, Pohl)
Doctorants PhD students 3 (Vercauteren, Konukoglu, Geremia)  2 (Yeo, Friztsche)
Stagiaires Interns  0  0
Thèses en co-tutelle soutenues Co-supervised defended PhD  0 (1 participation jury)  0 (1 participation jury)
Thèses en co-tutelle en cours Current co-supervised PhD  0  0
Total des thèses soutenues Global defended PhD  2 1 
Total des thèses en cours Global current PhD  1  1
Visites de l'équipe partenaire (hors colloques) Travels to the partners (conferences not included)  7  12
Nombre de Publications/Number of publications  16

 


In your opinion, what are the main results and what is the added value of the Associate Team ?

One of the major contribution of this associated team is the personalisation of reaction-diffusion type tumor growth models using time series of medical images of a patient. This estimation of parameters solving a co-called "inverse problem" allows for a better characterisation of the disease allowing for a better treatment. Another contribution of this associated team is the extensive work on the "demons" non-rigid registration algorithm to extend it to diffusion tensor images, spherical surfaces and aymetric problems.

The associate teams allowed a continuous collaboration between teams that have different strengths. It initiated fruitfull exchanges of students and researchers that lead to ongoing collaboration and productive research. A key aspect of our associated team is the time dedicated to visits between the different partners, in total more than 24 man-months. The majority of the co-authored publications have been made possible because of this intensive exchange program. 


How do you see the evolution of this cooperation (renewal of the Associate Team, european project or other-funding project, end of the cooperation...)


We are aiming at a renewal of the associated team. Because of the evolution of the collaboration, the involvment of some of the partners will decrease while some other will foster.

 



BILAN SCIENTIFIQUE / SCIENTIFIC REPORT

Please detail the Associate Team scientific activity as well as the results obtained in the last 3 years : publications, communications, organization of conferences, training, defended PhD, valorization, patents filing...

Seminar and presentation

September 26th 2007, William Wells gave a talk at INRIA entitled "A Marginalized MAP Approach and EM Optimization for Pair-Wise Registration"
August 30th 2007, Kilian Pohl gave a talk at INRIA entitled "Solving the Mean Field Approximation in the Level Set Framework via the Logarithm of Odds"
Nicholas Ayache gave several talk in Boston during his scientific visit in 2007: Olivier Clatz gave a talk at the 2007 REUSSI welcome seminar at INRIA Rocqencourt entitled "Modeling Brain Tumors for Patient-Specific Therapy"
June 20th 2007, Boon Thye Thomas Yeo presented his previous work and work planning to the Asclepios team.
Oct 9th 2008. N. Ayache gave an invited talk at the annual conference of the Brittish Cancer Research Institute in Birmingham.
Sept 14th 2008. B. Menze gave a talk at the International Society of Megnetic Resonance Imagin workshop entitled "Estimating the growth process of gliomas using physiological models"
June 18th 2009. O. Clatz gave a talk a the dkfz german cancer research center entitled "Towards personalised Brain Tumors models"

Publications

N. Archip, O. Clatz, S. Whalen, S. DiMaio, P.M. Black, F. Jolesz, A. Golby, S. Warfield. Compensation of geometric distortion effects on intra-operative MRI for enhanced visualization in image guided neurosurgery. Neurosurgery.
[abstract]
Preoperative magnetic resonance imaging (MRI), functional MRI, diffusion tensor MRI, magnetic resonance spectroscopy, and positron-emission tomographic scans may be aligned to intraoperative MRI to enhance visualization and navigation during image-guided neurosurgery. However, several effects (both machine- and patient-induced distortions) lead to significant geometric distortion of intraoperative MRI. Therefore, a precise alignment of these image modalities requires correction of the geometric distortion. We propose and evaluate a novel method to compensate for the geometric distortion of intraoperative 0.5-T MRI in image-guided neurosurgery. METHODS: In this initial pilot study, 11 neurosurgical procedures were prospectively enrolled. The scheme used to correct the geometric distortion is based on a nonrigid registration algorithm introduced by our group. 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 used to achieve the regularization. The geometry of intraoperative images (0.5 T) is changed so that the images match the preoperative MRI scans (3 T). RESULTS: We compared the alignment between preoperative and intraoperative imaging using 1) only rigid registration without correction of the geometric distortion, and 2) 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 3-T and 0.5-T MRIs after rigid registration alone and with the addition of geometric distortion correction of the 0.5-T MRI. Overall, the mean magnitude of the geometric distortion measured on the intraoperative images is 10.3 mm with a minimum of 2.91 mm and a maximum of 21.5 mm. The measured accuracy of the geometric distortion compensation algorithm is 1.93 mm. There is a statistically significant difference between the accuracy of the alignment of preoperative and intraoperative images, both with and without the correction of geometric distortion (P < 0.001). CONCLUSION: The major contributions of this study are 1) identification of geometric distortion of intraoperative images relative to preoperative images, 2) measurement of the geometric distortion, 3) application of nonrigid registration to compensate for geometric distortion during neurosurgery, 4) measurement of residual distortion after geometric distortion correction, and 5) phantom study to quantify geometric distortion.

E. Angelini, O. Clatz, E. Konukoglu, E. Mandonnet, L. Capelle, H. Duffau. Glioma dynamics and computational models: a review of segmentation, registration, and in silico growth algorithms and their clinical applications. Current Medical Imaging Reviews.
[abstract]
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 paper, 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.

N. Archip, O. Clatz, S. Whalen, D.  Kacher, A. Fedorov, A. Kot, N. Chrisochoides, F. Jolesz, A. Golby, P.M. Black, S. Warfield. Non-rigid alignment  of pre-operative MRI, fMRI, and DT-MRI with intra-operative MRI for  enhanced visualization and navigation in image-guided neurosurgery. Neuroimage. 2007 Apr 1;35(2):609-24.  Epub 2006 Dec 23.
[abstract]
OBJECTIVE: 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.

E. Konukoglu, O.Clatz, Pierre-Yves Bondiau, Maxime Sermesant, H. Delingette, and N. Ayache. Towards an Identification of Tumor Growth Parameters from Time Series of Images. In Proc. Medical Image Computing and Computer Assisted Intervention (MICCAI), Brisbane, Australia, October 2007.
[abstract]
In cancer treatment, understanding the aggressiveness of the tumor is essential in therapy planning and patient follow-up. In this article, 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.

E. Konukoglu, M. Sermesant, O. Clatz, J.-M. Peyrat, H. Delingette, and N. Ayache. A Recursive Anisotropic Fast Marching Approach to Reaction Diffusion Equation: Application to Tumor Growth Modeling. In Proceedings of the 20th International Conference on Information Processing in Medical Imaging (IPMI'07), volume 4584 of LNCS, pages 686-699, 2-6 July 2007.
[abstract]
Bridging the gap between clinical applications and mathematical models is one of the new challenges of medical image analysis. In this paper, we propose an efficient and accurate algorithm to solve anisotropic Eikonal equations, in order to link biological models using reaction-diffusion 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.

F.S.J. Castro , O. Clatz, J. Dauguet, N. Archip,J.-P. Thiran, S. Warfield. Evaluation of Brain Image NonRigid Registration Algorithms Based on Log-Euclidean MR-DTI Consistency Measures. ISBI 2007.
[abstract]
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.

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.
[abstract]
We propose an algorithm for the diffeomorphic registration of diffusion tensor images (DTI). Previous DTI registration algorithms using full tensor information suffer from difficulties in computing the differential of the Finite Strain tensor reorientation strategy. We borrow results from computer vision to derive an analytical gradient of the objective function. By leveraging on the closed-formgradient and the one-parameter subgroups of diffeomorphisms, the resulting registration algorithm is diffeomorphic and fast. Registration of a pair of 128 × 128 × 60 diffusion tensor volumes takes 15 minutes. We contrast the algorithm with a classic alternative that does not take into account the reorientation in the gradient computation. We show with 40 pairwise DTI registrations that using the exact gradient achieves significantly better registration.

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.
[abstract]
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.

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.
[abstract]
The emergence of new modalities such as Diffusion Tensor Imaging (DTI) is of great interest for the characterization and the temporal study of Multiple Sclerosis (MS). DTI indeed gives information on water diffusion within tissues and could therefore reveal alterations in white matter fibers 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. In this article, we present a framework to study the benefits of using the whole diffusion tensor information to detect statistically significant differences 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 differences 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 significant differences on the lesions but also in regions around them, enabling an early detection of an extension of the MS disease.

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.
[abstract]
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.

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.
[abstract]
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.

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.
[abstract]
Change detection is a critical task in the diagnosis of many slowly evolving pathologies proach 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.

Boon Thye Thomas Yeo, Mert Sabuncu, Tom Vercauteren, Nicholas Ayache, Bruce Fischl, and Polina Golland. Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration. IEEE Transactions on Medical Imaging, August 2009.
[abstract]
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.

Boon Thye Thomas Yeo, Tom Vercauteren, Pierre Fillard, Jean-Marc Peyrat, Xavier Pennec, Polina Golland, Nicholas Ayache, and Olivier Clatz. DT-REFinD: Diffusion Tensor Registration with Exact Finite-Strain Differential. IEEE Transactions on Medical Imaging, 2009. Note: In press.
[abstract]
In this paper, we propose the DT-REFinD algorithm for the diffeomorphic non-linear registration of diffusion tensor images. Unlike scalar images, deforming tensor images requires choosing both a reorientation strategy and an interpolation scheme. Current diffusion tensor registration algorithms that use full tensor information face difficulties in computing the differential of the tensor reorientation strategy and consequently, these methods often approximate the gradient of the objective function. In the case of the Finite-Strain reorientation strategy, we borrow results from the pose estimation literature in computer vision to derive an analytical gradient of the registration objective function. By utilizing the closed-form gradient and the velocity field representation of one parameter subgroups of diffeomorphisms, the resulting registration algorithm is diffeomorphic and fast. We contrast the algorithm with a traditional Finite-Strain alternative that ignores the reorientation in the gradient computation. We show that the exact gradient leads to significantly better registration at the cost of computation time. Independently of the choice of Euclidean or Log-Euclidean interpolation and sum of squared differences dissimilarity measure, the exact gradient achieves better alignment over an entire spectrum of deformation penalties. Alignment quality is assessed with a battery of metrics including tensor overlap, fractional anisotropy, inverse consistency and closeness to synthetic warps. The improvements persist even when a different reorientation scheme, Preservation of Principal Directions, is used to apply the final deformations.

Mert Sabuncu, Boon Thye Thomas Yeo, Tom Vercauteren, Koen Van Leemput, and Polina Golland. Asymmetric Image-Template Registration. In Proc. Medical Image Computing and Computer Assisted Intervention (MICCAI'09), Lecture Notes in Computer Science, London, UK, September 2009.
[abstract]
A natural requirement in pairwise image registration is that the resulting deformation is independent of the order of the images. This constraint is typically achieved via a symmetric cost function and has been shown to reduce the effects of local optima. Consequently, symmetric registration has been successfully applied to pairwise image registration as well as the spatial alignment of individual images with a template. However, recent work has shown that the relationship between an image and a template is fundamentally asymmetric. In this paper, we develop a method that reconciles the practical advantages of symmetric registration with the asymmetric nature of image-template registration by adding a simple correction factor to the symmetric cost function. We instantiate our model within a log-domain diffeomorphic registration framework. Our experiments show exploiting the asymmetry in imagetemplate registration improves alignment in the image coordinates.

Ender Konukoglu, Olivier Clatz, Bjoern H. Menze, Marc-André Weber, Bram Stieltjes, Emmanuel Mandonnet, Hervé Delingette, and Nicholas Ayache. Image Guided Personalization of Reaction-Diffusion Type Tumor Growth Models Using Modified Anisotropic Eikonal Equations. IEEE Transactions on Medical Imaging, 2009. Note: In press.
[abstract]
Reaction-diffusion based tumor growth models have been widely used in the literature for modeling the growth of brain gliomas. Lately, recent models have started integrating medical images in their formulation. Including different tissue types, geometry of the brain and the directions of white matter fiber tracts improved the spatial accuracy of reaction-diffusion models. The adaptation of the general model to the specific patient cases on the other hand has not been studied thoroughly yet. In this work we address this adaptation. We propose a parameter estimation method for reaction-diffusion tumor growth models using time series of medical images. This method estimates the patient specific parameters of the model using the images of the patient taken at successive time instances. The proposed method formulates the evolution of the tumor delineation visible in the images based on the reaction-diffusion dynamics therefore it remains consistent with the information available. We perform thorough analysis of the method using synthetic tumors and show important couplings between parameters of the reaction-diffusion model. We show that several parameters can be uniquely identified in the case of fixing one parameter, namely the proliferation rate of tumor cells. Moreover, regardless of the value the proliferation rate is fixed to, the speed of growth of the tumor can be estimated in terms of the model parameters with accuracy. We also show that using the model-based speed we can simulate the evolution of the tumor for the specific patient case. Finally we apply our method to 2 real cases and show promising preliminary results.

 Other

    A patent proposal submitted with Emmanuel Mandonnet, Jean-Luc Neau and Michel Thiebaut is currently under review.

© INRIA - mise à jour le 13/10/2009