Robust Cardiac Motion Estimation using Ultrafast Ultrasound Data: A Low-Rank-Topology-Preserving Approach

Cardiac motion estimation is an important diagnostic tool to detect heart diseases and it has been explored with modalities such as MRI and conventional ultrasound (US) sequences. US cardiac motion estimation still presents challenges because of the complex motion patterns and the presence of noise. In this work, we propose a novel approach to estimate the cardiac motion using ultrafast ultrasound data. -- Our solution is based on a variational formulation characterized by the L2-regularized class. The displacement is represented by a lattice of b-splines and we ensure robustness by applying a maximum likelihood type estimator. While this is an important part of our solution, the main highlight of this paper is to combine a low-rank data representation with topology preservation. Low-rank data representation (achieved by finding the k-dominant singular values of a Casorati Matrix arranged from the data sequence) speeds up the global solution and achieves noise reduction. On the other hand, topology preservation (achieved by monitoring the Jacobian determinant) allows to radically rule out distortions while carefully controlling the size of allowed expansions and contractions. Our variational approach is carried out on a realistic dataset as well as on a simulated one. We demonstrate how our proposed variational solution deals with complex deformations through careful numerical experiments. While maintaining the accuracy of the solution, the low-rank preprocessing is shown to speed up the convergence of the variational problem. Beyond cardiac motion estimation, our approach is promising for the analysis of other organs that experience motion.Comment: 15 pages, 10 figures, Physics in Medicine and Biology, 201

Constrained parameterization with applications to graphics and image processing.

Surface parameterization is to establish a transformation that maps the points on a surface to a specified parametric domain. It has been widely applied to computer graphics and image processing fields. The challenging issue is that the usual positional constraints always result in triangle flipping in parameterizations (also called foldovers). Additionally, distortion is inevitable in parameterizations. Thus the rigid constraint is always taken into account. In general, the constraints are application-dependent. This thesis thus focuses on the various constraints depended on applications and investigates the foldover-free constrained parameterization approaches individually. Such constraints usually include, simple positional constraints, tradeoff of positional constraints and rigid constraint, and rigid constraint. From the perspective of applications, we aim at the foldover-free parameterization methods with positional constraints, the as-rigid-as-possible parameterization with positional constraints, and the well-shaped well-spaced pre-processing procedure for low-distortion parameterizations in this thesis. The first contribution of this thesis is the development of a RBF-based re-parameterization algorithm for the application of the foldover-free constrained texture mapping. The basic idea is to split the usual parameterization procedure into two steps, 2D parameterization with the constraints of convex boundaries and 2D re-parameterization with the interior positional constraints. Moreover, we further extend the 2D re-parameterization approach with the interior positional constraints to high dimensional datasets, such as, volume data and polyhedrons. The second contribution is the development of a vector field based deformation algorithm for 2D mesh deformation and image warping. Many presented deformation approaches are used to employ the basis functions (including our proposed RBF-based re-parameterization algorithm here). The main problem is that such algorithms have infinite support, that is, any local deformation always leads to small changes over the whole domain. Our presented vector field based algorithm can effectively carry on the local deformation while reducing distortion as much as possible. The third contribution is the development of a pre-processing for surface parameterization. Except the developable surfaces, the current parameterization approaches inevitably incur large distortion. To reduce distortion, we proposed a pre-processing procedure in this thesis, including mesh partition and mesh smoothing. As a result, the resulting meshes are partitioned into a set of small patches with rectangle-like boundaries. Moreover, they are well-shaped and well-spaced. This pre-processing procedure can evidently improve the quality of meshes for low-distortion parameterizations

Development of acquisition system and algorithms for registration towards modeling displacement and deformation of the contour on the digital image

Centralna tema ovog rada je primena sistema za akviziciju slike u cilju procene i modelovanja deformacija i pomeranja objekata koji su snimljeni. Glavna metoda koja je pri tom korišćena je metoda registracije slika. Sam postupak registracije podrazumeva skup algoritama i metoda kojim se vrši pronalaženje transformacije koja preslikava prostor jedne slike u prostor druge. Ukoliko se radi o slikama istog objekta u različitim položajima ili konfiguracijama moguće je odrediti pomeranja i deformacije željene tačke poznavanjem ove transformacije. U radu su opisani već postojeći algoritmi, sa svojim najznačajnijim svojstvima. Na bazi ovih osobina razvijen je metod registracije baziran na rešavanju Laplasove jednačine za elektrostatičko polje. Ovakav pristup je moguć zahvaljujući činjenici da gradijent deformacija odgovara linijama elektrostatičkog polja, koje je dobijeno rešavanjem Laplasove jednačine i zadovoljava sva bitna svojstva koja treba da ima registraciona transformacija. Ove osobine se odnose na glatkost polja deformacije, postojanje inverzne funkcije i zabranu ukrštanja linija polja. Sam postupak rešavanja navedene jednačine i određivanje tražene transformaicje sproveden je primenom metode konačnih elemenata pri čemu je korišćena formulacija minimuma energija sistema. Jedna od inspiracija za rad na metodama registracije slike bio je i problem procene mehaničkih karakteristika tkiva aorte sa aneurizmom. U radu je opisana realizacija i način rada sistema koji je iskorišćen za karakterizaciju mehaničkih svojstava aorte, koji kao izlazne podatke daje informaciju o pomeranjima skupa tačaka tkiva kao i o vrednostima pritiska fluida koji izaziva ta pomeranja. Deformacije su procenjene primenom metoda segmentacije slike i izdvajanja ivica nakon čega je primenjen metod registracije slike kojom je određena deformacija tačaka tkiva u određenim vremenskim trenucima. Na osnovu ovih vrednosti primenom genetskog algoritma određena je vrednost Jangovog modula tkiva pri čemu je korišćen mehanički model deformacije tkiva. Analiza hoda upotrebom slika hoda je takođe jedan od izazova kada je u pitanju neinvazivna dijagnostika i praćenje stanja dijagnostifiko- vanih kao i zdravih subjekata. U ovom radu je prikazan postupak određivanja mehaničkog naprezanja hrskavice primenom slika snimljenih kamerom i vrednostima sile normalne reakcije podloge koja nastaje tokom hoda. Za procenu deformacija hrskavice korišćeni su algoritmi registracije slike između slika dobijenih sa kamere i slika dobijenih kompjuterizovanom tomografijom. Postupkom optimizacije procenjeni su i mehanički parametri hrskavice (Jangov modul i Poasonov koeficijent).The main aim of this thesis is the application of image acquisition system for the purpose of assessing and modeling the deformation and displacement of the objects acquired in digital images. The technique used in the study is method of image registration. The procedure of the registration includes a set of algorithms and methods which performs the assessment of transformation that maps the space of one image to another one. If there are images of the same object in different positions or configurations it is possible to determine the displacement and deformation of the desired point of understanding this transformation. The thesis describes the existing algorithms, along with their most important properties. The novel algorithms for image registration is developed based of solving the Laplace equation for electrostatic field. This approach is possible due to the fact that the transformation which corresponds to the deformation gradient field lines of the electrostatic field, which is obtained by solving the Laplace equation satisfies all essential features that should have the registration transformation. These properties are related to the smoothness of the deformation field, the existence of an inverse function of the prohibition of crossing the line field. The procedure for solving the above equation and determining the required transformation was conducted using finite element method with use of a formulation of minimum energy of the system. The motivation for this thesis was consideration problem of evaluation mechanical properties of tissues affected aortic aneurysm. The paper describes the implementation and operation of the system that was used to characterize the mechanical properties of the aorta, which as output data provides information about a set of deformation points on the tissue surface as well as the values of applied fluid pressure. Strains at the certain moment of time were estimated using the image segmentation method and edges extraction, and finally image registration is applied. Using strain values in the mechanical model of tissue, and genetic algorithm as optimization technique, the Young's modulus is assessment. Gait analysis based on the images data is also one of the challenges in non-invasive diagnosis and monitoring of both diagnosed patients and healthy subjects.. This thesis presents a method for determining the mechanical stress of the cartilage using the camera image, and the values of the normal ground reaction force, which is generated during the walk, for assessment of cartilage deformation algorithms were used image registration of images obtained from the camera and the images obtained by computed tomography. Mechanical parameters of cartilage (Young's modulus and Poisson's ratio) are evaluated in the optimization process

Estimating and understanding motion : from diagnostic to robotic surgery

Estimating and understanding motion from an image sequence is a central topic in computer vision. The high interest in this topic is because we are living in a world where many events that occur in the environment are dynamic. This makes motion estimation and understanding a natural component and a key factor in a widespread of applications including object recognition , 3D shape reconstruction, autonomous navigation and medica! diagnosis. Particularly, we focus on the medical domain in which understanding the human body for clinical purposes requires retrieving the organs' complex motion patterns, which is in general a hard problem when using only image data. In this thesis, we cope with this problem by posing the question - How to achieve a realistic motion estimation to offer a better clinical understanding? We focus this thesis on answering this question by using a variational formulation as a basis to understand one of the most complex motions in the human's body, the heart motion, through three different applications: (i) cardiac motion estimation for diagnostic, (ii) force estimation and (iii) motion prediction, both for robotic surgery. Firstly, we focus on a central topic in cardiac imaging that is the estimation of the cardiac motion. The main aim is to offer objective and understandable measures to physicians for helping them in the diagnostic of cardiovascular diseases. We employ ultrafast ultrasound data and tools for imaging motion drawn from diverse areas such as low-rank analysis and variational deformation to perform a realistic cardiac motion estimation. The significance is that by taking low-rank data with carefully chosen penalization, synergies in this complex variational problem can be created. We demonstrate how our proposed solution deals with complex deformations through careful numerical experiments using realistic and simulated data. We then move from diagnostic to robotic surgeries where surgeons perform delicate procedures remotely through robotic manipulators without directly interacting with the patients. As a result, they lack force feedback, which is an important primary sense for increasing surgeon-patient transparency and avoiding injuries and high mental workload. To solve this problem, we follow the conservation principies of continuum mechanics in which it is clear that the change in shape of an elastic object is directly proportional to the force applied. Thus, we create a variational framework to acquire the deformation that the tissues undergo due to an applied force. Then, this information is used in a learning system to find the nonlinear relationship between the given data and the applied force. We carried out experiments with in-vivo and ex-vivo data and combined statistical, graphical and perceptual analyses to demonstrate the strength of our solution. Finally, we explore robotic cardiac surgery, which allows carrying out complex procedures including Off-Pump Coronary Artery Bypass Grafting (OPCABG). This procedure avoids the associated complications of using Cardiopulmonary Bypass (CPB) since the heart is not arrested while performing the surgery on a beating heart. Thus, surgeons have to deal with a dynamic target that compromisetheir dexterity and the surgery's precision. To compensate the heart motion, we propase a solution composed of three elements: an energy function to estimate the 3D heart motion, a specular highlight detection strategy and a prediction approach for increasing the robustness of the solution. We conduct evaluation of our solution using phantom and realistic datasets. We conclude the thesis by reporting our findings on these three applications and highlight the dependency between motion estimation and motion understanding at any dynamic event, particularly in clinical scenarios.L’estimació i comprensió del moviment dins d’una seqüència d’imatges és un tema central en la visió per ordinador, el que genera un gran interès perquè vivim en un entorn ple d’esdeveniments dinàmics. Per aquest motiu és considerat com un component natural i factor clau dins d’un ampli ventall d’aplicacions, el qual inclou el reconeixement d’objectes, la reconstrucció de formes tridimensionals, la navegació autònoma i el diagnòstic de malalties. En particular, ens situem en l’àmbit mèdic en el qual la comprensió del cos humà, amb finalitats clíniques, requereix l’obtenció de patrons complexos de moviment dels òrgans. Aquesta és, en general, una tasca difícil quan s’utilitzen només dades de tipus visual. En aquesta tesi afrontem el problema plantejant-nos la pregunta - Com es pot aconseguir una estimació realista del moviment amb l’objectiu d’oferir una millor comprensió clínica? La tesi se centra en la resposta mitjançant l’ús d’una formulació variacional com a base per entendre un dels moviments més complexos del cos humà, el del cor, a través de tres aplicacions: (i) estimació del moviment cardíac per al diagnòstic, (ii) estimació de forces i (iii) predicció del moviment, orientant-se les dues últimes en cirurgia robòtica. En primer lloc, ens centrem en un tema principal en la imatge cardíaca, que és l’estimació del moviment cardíac. L’objectiu principal és oferir als metges mesures objectives i comprensibles per ajudar-los en el diagnòstic de les malalties cardiovasculars. Fem servir dades d’ultrasons ultraràpids i eines per al moviment d’imatges procedents de diverses àrees, com ara l’anàlisi de baix rang i la deformació variacional, per fer una estimació realista del moviment cardíac. La importància rau en que, en prendre les dades de baix rang amb una penalització acurada, es poden crear sinergies en aquest problema variacional complex. Mitjançant acurats experiments numèrics, amb dades realístiques i simulades, hem demostrat com les nostres propostes solucionen deformacions complexes. Després passem del diagnòstic a la cirurgia robòtica, on els cirurgians realitzen procediments delicats remotament, a través de manipuladors robòtics, sense interactuar directament amb els pacients. Com a conseqüència, no tenen la percepció de la força com a resposta, que és un sentit primari important per augmentar la transparència entre el cirurgià i el pacient, per evitar lesions i per reduir la càrrega de treball mental. Resolem aquest problema seguint els principis de conservació de la mecànica del medi continu, en els quals està clar que el canvi en la forma d’un objecte elàstic és directament proporcional a la força aplicada. Per això hem creat un marc variacional que adquireix la deformació que pateixen els teixits per l’aplicació d’una força. Aquesta informació s’utilitza en un sistema d’aprenentatge, per trobar la relació no lineal entre les dades donades i la força aplicada. Hem dut a terme experiments amb dades in-vivo i ex-vivo i hem combinat l’anàlisi estadístic, gràfic i de percepció que demostren la robustesa de la nostra solució. Finalment, explorem la cirurgia cardíaca robòtica, la qual cosa permet realitzar procediments complexos, incloent la cirurgia coronària sense bomba (off-pump coronary artery bypass grafting o OPCAB). Aquest procediment evita les complicacions associades a l’ús de circulació extracorpòria (Cardiopulmonary Bypass o CPB), ja que el cor no s’atura mentre es realitza la cirurgia. Això comporta que els cirurgians han de tractar amb un objectiu dinàmic que compromet la seva destresa i la precisió de la cirurgia. Per compensar el moviment del cor, proposem una solució composta de tres elements: un funcional d’energia per estimar el moviment tridimensional del cor, una estratègia de detecció de les reflexions especulars i una aproximació basada en mètodes de predicció, per tal d’augmentar la robustesa de la solució. L’avaluació de la nostra solució s’ha dut a terme mitjançant conjunts de dades sintètiques i realistes. La tesi conclou informant dels nostres resultats en aquestes tres aplicacions i posant de relleu la dependència entre l’estimació i la comprensió del moviment en qualsevol esdeveniment dinàmic, especialment en escenaris clínics.Postprint (published version

Recalage non rigide et segmentation automatique d'images de perfusion du foie

Contexte médicale -- Revue de littérature -- Recalage avec contrainte d'incompressibilité -- Segmentation basée sur le recalage de grandes déformations -- Cadre de travail unifié et efficace -- Discussion générale

Automatisation et optimisation de l’analyse de données de résonance magnétique pour les cerveaux de rongeur en développement

Les lésions de la matière blanche parfois observées chez les enfants prématurés peuvent avoir des conséquences lourdes sur le développement cognitif, comportemental et social de l’enfant. Il est important de réagir tôt pour éviter des conséquences irrémédiables. Malheureusement, à l’heure actuelle, la capacité d’un traitement à protéger les habilités cognitives ou comportementales ne peut être évaluée qu’à un stade développemental avancé, et il est alors généralement trop tard pour un traitement alternatif. L’établissement de biomarqueurs qui corrèlent avec l’issue neuro¬développementale et qui permettent d’évaluer en phase aiguë l’effet du traitement serait très bénéfique. À cet effet, l’imagerie par résonance magnétique (IRM) est un outil de choix. Son caractère non-invasif permet d’étudier sans risques additionnels cette population sensible. Le présent mémoire évalue la capacité des technologies de résonance magnétique à détecter les lésions diffuses de la matière blanche dans un modèle animal. Ce modèle animal reproduit les lésions observées chez l’humain prématuré en induisant une réaction inflammatoire par l’injection de lipopolysaccharides (LPS) directement dans le cerveau au troisième jour postnatal (P3). L’objectif principal du travail présenté est le développement d’outils opérateur-indépendants et optimaux pour l’analyse des données de résonance magnétique du raton. En particulier, ces outils sont conçus pour l’analyse des données d’imagerie du tenseur de diffusion (DTI) et pour l’analyse de données de spectroscopie (MRS). Ces outils sont ensuite appliqués à la caractérisation du modèle animal via l’analyse de deux jeux de données. Le premier est constitué de données DTI acquises à P24 ex vivo sur deux groupes, un sham (témoin) et un ayant subi une injection de LPS. Le second comprend des données MRS et DTI acquises en phase aiguë de la réaction inflammatoire (P4) in vivo dans trois groupes : sham, LPS et un troisième ayant reçu l’injection de LPS et un traitement neuroprotecteur par l’injection de l’antagoniste recombinant de l’IL1-ß (IL1-Ra), une cytokine pro-inflammatoire. Il existe différentes approches à l’analyse des données DTI : par région d’intérêt, par histogrammes, par tractographie et par comparaisons voxel-par-voxel. Principalement, dans ce mémoire, deux méthodes voxel-par-voxel ont été étudiées : "Voxel-Based Analysis" ou VBA et "Tract-Based Spatial Statistics" ou TBSS. VBA compare les populations en calculant une statistique pour chaque point de l’espace. TBSS, développé pour répondre à certaines limitations de VBA, compare les deux groupes en conduisant les tests sur un sous-ensemble des voxels de la matière blanche, le squelette de matière blanche. Ces deux méthodes reposent sur une étape préliminaire majeure : la normalisation spatiale. La normalisation permet de s’assurer, jusqu’à un certain degré, que pour l’ensemble de la population les voxels comparés correspondent à une même région anatomique. Pour ce projet, trois méthodes de normalisation spatiale utilisant chacune un algorithme de recalage différent ont été implémentées : Symmetric Group-Wise Normalization avec l’algorithme Symmetric Normalization (SyN, de la suite Advanced Normalization Tools ou ANTs), la normalisation spatiale non-biaisée du module Diffusion Tensor Toolkit (DTI-TK) et une normalisation spatiale sur base du sujet le plus représentatif de la population avec l’algorithme FMRIB Non-linear Image Registration Tool (FNIRT, de la suite FMRIB Software Library ou FSL). L’analyse automatique des données de diffusion peut donc se faire via différentes combinaisons de normalisation spatiale (ANTs, DTI-TK, FSL) et d’analyse voxel-par-voxel (VBA, TBSS). Il est généralement difficile de déterminer la meilleure combinaison et il n’y a pas de principes établis pour guider ce choix. Ceci est étudié dans l’article « Near equivalence of three automated diffusion tensor analysis pipelines in a neonate rat model of periventricular leukomalacia » où chacune des normalisations spatiales implémentées est testée en combinaison avec VBA et TBSS. L’étude démontre que les résultats sont très cohérents entre les différentes approches mais met en évidence des limitations de VBA et TBSS. Les résultats suggèrent qu’appliquer la normalisation DTI-TK en combinaison avec TBSS permet une analyse plus robuste, du moins pour les ratons et dans le cadre de ce modèle animal. Des modules d’analyse par histogramme et de parcellisation automatique de la matière blanche sous-corticale ont également été implémentés et testés. L’analyse des données de spectroscopie est plus directe, dans le sens où les paramètres de l’analyse sont moins influents sur le résultat. De ce fait, un pipeline unique et opérateur¬indépendant a été implémenté, incorporant le prétraitement des données et la quantification des métabolites à l’aide du Linear Combination Model (LCModel). Ces pipelines DTI/MRS ont été appliqués à l’étude du modèle animal et ont permis de démontrer la sensibilité des technologies de résonance magnétique à ce type de lésion. En effet, l’analyse des données de diffusion ex vivo a souligné une lésion persistante et diffuse de la matière blanche sous-corticale du côté ipsilatéral. En phase aiguë de l’inflammation, les données de diffusion in vivo indiquent une forte diminution de la diffusivité radiale et axiale. La spectroscopie a également permis de mettre en évidence des changements métaboliques avec notamment une réduction de N-acetylaspartate, glutamate, phosphorylethanolamine et une augmentation de lipides et de macromolécules. Le traitement à l’IL1-Ra a permis de modérer les changements observés en DTI et en MRS. En conclusion, différents outils « état-de-l’art » relatifs à l’analyse de données DTI et MRS ont été développés et appliqués avec succès à l’étude d’un modèle animal des lésions de la matière blanche de l’enfant prématuré. Les résultats permettent de considérer la DTI et la MRS comme technologies prometteuses pour la caractérisation et le suivi de ce type de lésion, celles-ci étant sensibles à la lésion en phase aiguë de la réaction inflammatoire ainsi qu’à un stade développemental plus avancé. Cependant, afin de permettre une interprétation solide des changements observés, il est nécessaire de confronter les observations IRM à d’autres méthodes d’imagerie telles que l’immuno-histologie, la microscopie électronique ou encore l’optique par cohérence tomographique.----------ABSTRACT White matter injuries observed in the preterm infant may have heavy consequences on the cognitive, behavioral and social development of the child and it is imperative to act early in order to avoid definitive repercussions. Unfortunately, for now, the efficacy of neuroprotective treatments can only be assessed at an advanced developmental stage when it is already too late to experiment with an alternative treatment. Finding biomarkers that correlate with the neuro¬developmental outcome and allow to assess the efficacy of the treatment at an early stage would be greatly beneficial. Magnetic resonance imaging (MRI) is a prominent technology with potential for establishing quantitative biomarkers. Moreover, its non-invasive nature allows to study this sensitive population without additional risks. This thesis assesses the use of MRI technologies for the study of diffuse white matter injury in an animal model. This animal model reproduces the lesions observed in human preterms by inducing an inflammatory reaction in the neonate rat brain by an injection of lipopolysaccharides (LPS) at postnatal day 3 (P3). The main goal of this project is to develop user-independent and optimal tools for the analysis of magnetic resonance data of the rat pup brain. Specifically, these tools are designed for the analysis of diffusion tensor imaging (DTI) and magnetic resonance spectroscopy (MRS) data. The secondary goal is to apply these tools to the study of the animal model of white matter injury. The data are made up of two different sets. The first one is constituted of DTI data only acquired ex vivo at P24 on two different groups: one sham and one that underwent an LPS injection. The second set of data comprises both DTI and MRS data. Data were acquired in vivo at the acute phase of injury (P4) on three different groups: sham, LPS and a third group that was injected with LPS and received a neuroprotective treatment by administration of the recombinant antagonist of IL1¬ß (IL1-Ra), a pro-inflammatory cytokine. There are several ways to conduct the analysis of DTI data: by region of interest, by histograms, by tractography or by conducting voxelwise comparisons. Primarily, in this thesis, two voxelwise methods were studied: “Voxel-Based Analysis” or VBA and “Tract-Based Spatial Statistics” or TBSS. VBA compares populations by computing a statistic at every voxel of the image. TBSS, which has been developed to alleviate some limitations of VBA, runs the statistical tests on a subset of voxels in the white matter, the white matter skeleton. Both these methods strongly rely on a specific image processing step: the spatial normalization. The normalization ensures, to a certain extent, that the voxels correspond to a same anatomical region across the subjects. Here, three normalization approaches, each using a different registration algorithm, were implemented: Symmetric Group-Wise Normalization using the Symmetric Normalization (SyN) algorithm of the Advanced Normalization Tools (ANTs) toolbox; the unbiased normalization of the Diffusion Tensor Toolkit (DTI-TK); and a normalization based on the population most representative subject using FMRIB Non-linear Image Registration Tool (FNIRT) algorithm of the FMRIB Software Library (FSL). Automatic diffusion data analysis can therefore be performed using combinations of a certain spatial normalization (ANTs, DTI-TK, FSL) and a voxelwise analysis (VBA, TBSS). Determining the best combination is not straight-forward and there are no principled ways to choose one combination over another. This was studied in the submitted paper “Near equivalence of three automated diffusion tensor analysis pipelines in a neonate rat model of periventricular leukomalacia” in which each of the implemented normalization methods were tested in combination with VBA and TBSS. Results demonstrate great coherence among the tested pipelines but also underlines both VBA and TBSS limitations. The study also suggests that, for the rat pup data of this animal model, combining DTI-TK normalization with TBSS might yield a more robust analysis. Other analysis modules implemented for the study of DTI data include analysis by histogram and by automatic parcelling of sub-cortical white matter. Magnetic spectroscopy data analysis does not depend as strongly to the processing pipeline as diffusion data. Therefore, a unique and user-independent pipeline was implemented. This pipeline incorporates data preprocessing operations and automatic metabolites quantification using the Linear Combination Model (LCModel). These pipelines were applied to the study of the animal model and the results demonstrated that magnetic resonance technologies are sensitive to these injuries. The ex vivo diffusion data exhibited a persistent and diffuse injury of the sub-cortical white matter on the ipsilateral side. At the acute phase, the in vivo diffusion data showed a strong decrease of axial and radial diffusivities. The spectroscopy data also underlined metabolic perturbations with essentially a decrease of N-acetylaspartate, glutamate, phosphorylethanolamine and an increase of lipids and macromolecules. The IL1-Ra neuroprotective treatment seemed effective and moderated the amplitude of these changes in both DTI and MRS. In conclusion, various state-of-the-art analysis tools for DTI and MRS data were developed and successfully applied to the study of an animal model of diffuse white matter injury of the preterm baby. Results indicate that DTI and MRS are potential tools for the characterisation and monitoring of this pathology, these being sensitive to the injury in the acute and chronic stages. However, in order to further strengthen the interpretation of these results, it is necessary that these be supported by other imaging technologies such as immuno-histology, electronic microscopy or optical coherence tomography