Quantitative comparison of reconstruction methods for intra-voxel fiber recovery from diffusion MRI.

Validation is arguably the bottleneck in the diffusion magnetic resonance imaging (MRI) community. This paper evaluates and compares 20 algorithms for recovering the local intra-voxel fiber structure from diffusion MRI data and is based on the results of the "HARDI reconstruction challenge" organized in the context of the "ISBI 2012" conference. Evaluated methods encompass a mixture of classical techniques well known in the literature such as diffusion tensor, Q-Ball and diffusion spectrum imaging, algorithms inspired by the recent theory of compressed sensing and also brand new approaches proposed for the first time at this contest. To quantitatively compare the methods under controlled conditions, two datasets with known ground-truth were synthetically generated and two main criteria were used to evaluate the quality of the reconstructions in every voxel: correct assessment of the number of fiber populations and angular accuracy in their orientation. This comparative study investigates the behavior of every algorithm with varying experimental conditions and highlights strengths and weaknesses of each approach. This information can be useful not only for enhancing current algorithms and develop the next generation of reconstruction methods, but also to assist physicians in the choice of the most adequate technique for their studies.

Representing diffusion MRI in 5D for segmentation of white matter tracts with a level set method.

We present a method for segmenting white matter tracts from high angular resolution diffusion MR. images by representing the data in a 5 dimensional space of position and orientation. Whereas crossing fiber tracts cannot be separated in 3D position space, they clearly disentangle in 5D position-orientation space. The segmentation is done using a 5D level set method applied to hyper-surfaces evolving in 5D position-orientation space. In this paper we present a methodology for constructing the position-orientation space. We then show how to implement the standard level set method in such a non-Euclidean high dimensional space. The level set theory is basically defined for N-dimensions but there are several practical implementation details to consider, such as mean curvature. Finally, we will show results from a synthetic model and a few preliminary results on real data of a human brain acquired by high angular resolution diffusion MRI.

Perfusion and diffusion MRI of glioblastoma progression in a four-year prospective temozolomide clinical trial.

PURPOSE: This study was performed to determine the impact of perfusion and diffusion magnetic resonance imaging (MRI) sequences on patients during treatment of newly diagnosed glioblastoma. Special emphasis has been given to these imaging technologies as tools to potentially anticipate disease progression, as progression-free survival is frequently used as a surrogate endpoint. METHODS AND MATERIALS: Forty-one patients from a phase II temolozomide clinical trial were included. During follow-up, images were integrated 21 to 28 days after radiochemotherapy and every 2 months thereafter. Assessment of scans included measurement of size of lesion on T1 contrast-enhanced, T2, diffusion, and perfusion images, as well as mass effect. Classical criteria on tumor size variation and clinical parameters were used to set disease progression date. RESULTS: A total of 311 MRI examinations were reviewed. At disease progression (32 patients), a multivariate Cox regression determined 2 significant survival parameters: T1 largest diameter (p < 0.02) and T2 size variation (p < 0.05), whereas perfusion and diffusion were not significant. CONCLUSION: Perfusion and diffusion techniques cannot be used to anticipate tumor progression. Decision making at disease progression is critical, and classical T1 and T2 imaging remain the gold standard. Specifically, a T1 contrast enhancement over 3 cm in largest diameter together with an increased T2 hypersignal is a marker of inferior prognosis.

l0-deconvolution for compressive diffusion MRI

Diffusion MRI is a well established imaging modality providing a powerful way to non-invasively probe the structure of the white matter. Despite the potential of the technique, the intrinsic long scan times of these sequences have hampered their use in clinical practice. For this reason, a wide variety of methods have been proposed to shorten acquisition times. [...] We here review a recent work where we propose to further exploit the versatility of compressed sensing and convex optimization with the aim to characterize the fiber orientation distribution sparsity more optimally. We re-formulate the spherical deconvolution problem as a constrained l0 minimization.

Accelerated Microstructure Imaging via Convex Optimization (AMICO) from diffusion MRI data.

Microstructure imaging from diffusion magnetic resonance (MR) data represents an invaluable tool to study non-invasively the morphology of tissues and to provide a biological insight into their microstructural organization. In recent years, a variety of biophysical models have been proposed to associate particular patterns observed in the measured signal with specific microstructural properties of the neuronal tissue, such as axon diameter and fiber density. Despite very appealing results showing that the estimated microstructure indices agree very well with histological examinations, existing techniques require computationally very expensive non-linear procedures to fit the models to the data which, in practice, demand the use of powerful computer clusters for large-scale applications. In this work, we present a general framework for Accelerated Microstructure Imaging via Convex Optimization (AMICO) and show how to re-formulate this class of techniques as convenient linear systems which, then, can be efficiently solved using very fast algorithms. We demonstrate this linearization of the fitting problem for two specific models, i.e. ActiveAx and NODDI, providing a very attractive alternative for parameter estimation in those techniques; however, the AMICO framework is general and flexible enough to work also for the wider space of microstructure imaging methods. Results demonstrate that AMICO represents an effective means to accelerate the fit of existing techniques drastically (up to four orders of magnitude faster) while preserving accuracy and precision in the estimated model parameters (correlation above 0.9). We believe that the availability of such ultrafast algorithms will help to accelerate the spread of microstructure imaging to larger cohorts of patients and to study a wider spectrum of neurological disorders.

Perfusion and diffusion MRI of glioblastoma progression in a four-year prospective temozolomide clinical trial

RESUME BUT Cette étude a été menée sur le suivi de patients traités pour un glioblastome nouvellement diagnostiqué. Son objectif a été de déterminer l'impact des séquences de perfusion et de diffusion en imagerie par résonance magnétique (IRM). Un intérêt particulier a été porté au potentiel de ces nouvelles techniques d'imagerie dans l'anticipation de la progression de la maladie. En effet, l'intervalle de temps libre de progression est une mesure alternative de pronostic fréquemment utilisée. MATERIEL ET METHODE L'étude a porté sur 41 patients participant à un essai clinique de phase II de traitement par temozolomide. Leur suivi radiologique a comporté un examen IRM dans les 21 à 28 jours après radiochimiothérapie et tous les 2 mois par la suite. L'évaluation des images s'est faite sur la base de l'évaluation de l'effet de masse ainsi que de la mesure de la taille de la lésion sur les images suivantes : T1 avec produit de contraste, T2, diffusion, perfusion. Afin de déterminer la date de progression de la maladie, les critères classiques de variation de taille adjoints aux critères cliniques habituels ont été utilisés. RESULAT 311 examens IRM ont été revus. Au moment de la progression (32 patients), une régression multivariée selon Cox a permis de déterminer deux paramètres de survie : diamètre maximal en T1 (p>0.02) et variation de taille en T2 (p<0.05). L'impact de la perfusion et de la diffusion n'a pas été démontré de manière statistiquement significative. CONCLUSION Les techniques de perfusion et de diffusion ne peuvent pas être utilisées pour anticiper la progression tumorale. Alors que la prise de décision au niveau thérapeutique est critique au moment de la progression de la maladie, l'IRM classique en T1 et en T2 reste la méthode d'imagerie de choix. De manière plus spécifique, une prise de contraste en T1 supérieure à 3 cm dans son plus grand diamètre associée à un hypersignal T2 en augmentation forment un marqueur de mauvais pronostic.

A connectome-based comparison of diffusion MRI schemes.

Diffusion MRI has evolved towards an important clinical diagnostic and research tool. Though clinical routine is using mainly diffusion weighted and tensor imaging approaches, Q-ball imaging and diffusion spectrum imaging techniques have become more widely available. They are frequently used in research-oriented investigations in particular those aiming at measuring brain network connectivity. In this work, we aim at assessing the dependency of connectivity measurements on various diffusion encoding schemes in combination with appropriate data modeling. We process and compare the structural connection matrices computed from several diffusion encoding schemes, including diffusion tensor imaging, q-ball imaging and high angular resolution schemes, such as diffusion spectrum imaging with a publically available processing pipeline for data reconstruction, tracking and visualization of diffusion MR imaging. The results indicate that the high angular resolution schemes maximize the number of obtained connections when applying identical processing strategies to the different diffusion schemes. Compared to the conventional diffusion tensor imaging, the added connectivity is mainly found for pathways in the 50-100mm range, corresponding to neighboring association fibers and long-range associative, striatal and commissural fiber pathways. The analysis of the major associative fiber tracts of the brain reveals striking differences between the applied diffusion schemes. More complex data modeling techniques (beyond tensor model) are recommended 1) if the tracts of interest run through large fiber crossings such as the centrum semi-ovale, or 2) if non-dominant fiber populations, e.g. the neighboring association fibers are the subject of investigation. An important finding of the study is that since the ground truth sensitivity and specificity is not known, the comparability between results arising from different strategies in data reconstruction and/or tracking becomes implausible to understand.

Mechanisms underlying functional recovery after in vivo focal cerebral ischemia : from preconditioning to diffusion MRI

Version abregée L'ischémie cérébrale est la troisième cause de mort dans les pays développés, et la maladie responsable des plus sérieux handicaps neurologiques. La compréhension des bases moléculaires et anatomiques de la récupération fonctionnelle après l'ischémie cérébrale est donc extrêmement importante et représente un domaine d'intérêt crucial pour la recherche fondamentale et clinique. Durant les deux dernières décennies, les chercheurs ont tenté de combattre les effets nocifs de l'ischémie cérébrale à l'aide de substances exogènes qui, bien que testées avec succès dans le domaine expérimental, ont montré un effet contradictoire dans l'application clinique. Une approche différente mais complémentaire est de stimuler des mécanismes intrinsèques de neuroprotection en utilisant le «modèle de préconditionnement» : une brève insulte protège contre des épisodes d'ischémie plus sévères à travers la stimulation de voies de signalisation endogènes qui augmentent la résistance à l'ischémie. Cette approche peut offrir des éléments importants pour clarifier les mécanismes endogènes de neuroprotection et fournir de nouvelles stratégies pour rendre les neurones et la glie plus résistants à l'attaque ischémique cérébrale. Dans un premier temps, nous avons donc étudié les mécanismes de neuroprotection intrinsèques stimulés par la thrombine, un neuroprotecteur «préconditionnant» dont on a montré, à l'aide de modèles expérimentaux in vitro et in vivo, qu'il réduit la mort neuronale. En appliquant une technique de microchirurgie pour induire une ischémie cérébrale transitoire chez la souris, nous avons montré que la thrombine peut stimuler les voies de signalisation intracellulaire médiées par MAPK et JNK par une approche moléculaire et l'analyse in vivo d'un inhibiteur spécifique de JNK (L JNK) .Nous avons également étudié l'impact de la thrombine sur la récupération fonctionnelle après une attaque et avons pu démontrer que ces mécanismes moléculaires peuvent améliorer la récupération motrice. La deuxième partie de cette étude des mécanismes de récupération après ischémie cérébrale est basée sur l'investigation des bases anatomiques de la plasticité des connections cérébrales, soit dans le modèle animal d'ischémie transitoire, soit chez l'homme. Selon des résultats précédemment publiés par divers groupes ,nous savons que des mécanismes de plasticité aboutissant à des degrés divers de récupération fonctionnelle sont mis enjeu après une lésion ischémique. Le résultat de cette réorganisation est une nouvelle architecture fonctionnelle et structurelle, qui varie individuellement selon l'anatomie de la lésion, l'âge du sujet et la chronicité de la lésion. Le succès de toute intervention thérapeutique dépendra donc de son interaction avec la nouvelle architecture anatomique. Pour cette raison, nous avons appliqué deux techniques de diffusion en résonance magnétique qui permettent de détecter les changements de microstructure cérébrale et de connexions anatomiques suite à une attaque : IRM par tenseur de diffusion (DT-IR1V) et IRM par spectre de diffusion (DSIRM). Grâce à la DT-IRM hautement sophistiquée, nous avons pu effectuer une étude de follow-up à long terme chez des souris ayant subi une ischémie cérébrale transitoire, qui a mis en évidence que les changements microstructurels dans l'infarctus ainsi que la modification des voies anatomiques sont corrélés à la récupération fonctionnelle. De plus, nous avons observé une réorganisation axonale dans des aires où l'on détecte une augmentation d'expression d'une protéine de plasticité exprimée dans le cône de croissance des axones (GAP-43). En appliquant la même technique, nous avons également effectué deux études, rétrospective et prospective, qui ont montré comment des paramètres obtenus avec DT-IRM peuvent monitorer la rapidité de récupération et mettre en évidence un changement structurel dans les voies impliquées dans les manifestations cliniques. Dans la dernière partie de ce travail, nous avons décrit la manière dont la DS-IRM peut être appliquée dans le domaine expérimental et clinique pour étudier la plasticité cérébrale après ischémie. Abstract Ischemic stroke is the third leading cause of death in developed countries and the disease responsible for the most serious long-term neurological disability. Understanding molecular and anatomical basis of stroke recovery is, therefore, extremely important and represents a major field of interest for basic and clinical research. Over the past 2 decades, much attention has focused on counteracting noxious effect of the ischemic insult with exogenous substances (oxygen radical scavengers, AMPA and NMDA receptor antagonists, MMP inhibitors etc) which were successfully tested in the experimental field -but which turned out to have controversial effects in clinical trials. A different but complementary approach to address ischemia pathophysiology and treatment options is to stimulate and investigate intrinsic mechanisms of neuroprotection using the "preconditioning effect": applying a brief insult protects against subsequent prolonged and detrimental ischemic episodes, by up-regulating powerful endogenous pathways that increase resistance to injury. We believe that this approach might offer an important insight into the molecular mechanisms responsible for endogenous neuroprotection. In addition, results from preconditioning model experiment may provide new strategies for making brain cells "naturally" more resistant to ischemic injury and accelerate their rate of functional recovery. In the first part of this work, we investigated down-stream mechanisms of neuroprotection induced by thrombin, a well known neuroprotectant which has been demonstrated to reduce stroke-induced cell death in vitro and in vivo experimental models. Using microsurgery to induce transient brain ischemia in mice, we showed that thrombin can stimulate both MAPK and JNK intracellular pathways through a molecular biology approach and an in vivo analysis of a specific kinase inhibitor (L JNK1). We also studied thrombin's impact on functional recovery demonstrating that these molecular mechanisms could enhance post-stroke motor outcome. The second part of this study is based on investigating the anatomical basis underlying connectivity remodeling, leading to functional improvement after stroke. To do this, we used both a mouse model of experimental ischemia and human subjects with stroke. It is known from previous data published in literature, that the brain adapts to damage in a way that attempts to preserve motor function. The result of this reorganization is a new functional and structural architecture, which will vary from patient to patient depending on the anatomy of the damage, the biological age of the patient and the chronicity of the lesion. The success of any given therapeutic intervention will depend on how well it interacts with this new architecture. For this reason, we applied diffusion magnetic resonance techniques able to detect micro-structural and connectivity changes following an ischemic lesion: diffusion tensor MRI (DT-MRI) and diffusion spectrum MRI (DS-MRI). Using DT-MRI, we performed along-term follow up study of stroke mice which showed how diffusion changes in the stroke region and fiber tract remodeling is correlating with stroke recovery. In addition, axonal reorganization is shown in areas of increased plasticity related protein expression (GAP 43, growth axonal cone related protein). Applying the same technique, we then performed a retrospective and a prospective study in humans demonstrating how specific DTI parameters could help to monitor the speed of recovery and show longitudinal changes in damaged tracts involved in clinical symptoms. Finally, in the last part of this study we showed how DS-MRI could be applied both to experimental and human stroke and which perspectives it can open to further investigate post stroke plasticity.

Structured sparsity for spatially coherent fibre orientation estimation in diffusion MRI.

We propose a novel formulation to solve the problem of intra-voxel reconstruction of the fibre orientation distribution function (FOD) in each voxel of the white matter of the brain from diffusion MRI data. The majority of the state-of-the-art methods in the field perform the reconstruction on a voxel-by-voxel level, promoting sparsity of the orientation distribution. Recent methods have proposed a global denoising of the diffusion data using spatial information prior to reconstruction, while others promote spatial regularisation through an additional empirical prior on the diffusion image at each q-space point. Our approach reconciles voxelwise sparsity and spatial regularisation and defines a spatially structured FOD sparsity prior, where the structure originates from the spatial coherence of the fibre orientation between neighbour voxels. The method is shown, through both simulated and real data, to enable accurate FOD reconstruction from a much lower number of q-space samples than the state of the art, typically 15 samples, even for quite adverse noise conditions.

Structured sparsity through reweighting and application to diffusion MRI

We consider the problem of multiple correlated sparse signals reconstruction and propose a new implementation of structured sparsity through a reweighting scheme. We present a particular application for diffusion Magnetic Resonance Imaging data and show how this procedure can be used for fibre orientation reconstruction in the white matter of the brain. In that framework, our structured sparsity prior can be used to exploit the fundamental coherence between fibre directions in neighbour voxels. Our method approaches the ℓ0 minimisation through a reweighted ℓ1-minimisation scheme. The weights are here defined in such a way to promote correlated sparsity between neighbour signals.

On evaluating the accuracy and biological plausibility of diffusion MRI tractograms

In diffusion MRI, traditional tractography algorithms do not recover truly quantitative tractograms and the structural connectivity has to be estimated indirectly by counting the number of fiber tracts or averaging scalar maps along them. Recently, global and efficient methods have emerged to estimate more quantitative tractograms by combining tractography with local models for the diffusion signal, like the Convex Optimization Modeling for Microstructure Informed Tractography (COMMIT) framework. In this abstract, we show the importance of using both (i) proper multi-compartment diffusion models and (ii) adequate multi-shell acquisitions, in order to evaluate the accuracy and the biological plausibility of the tractograms.

Simulation-based evaluation of susceptibility distortion correction methods in diffusion MRI for connectivity analysis

Connectivity analysis on diffusion MRI data of the whole- brain suffers from distortions caused by the standard echo- planar imaging acquisition strategies. These images show characteristic geometrical deformations and signal destruction that are an important drawback limiting the success of tractography algorithms. Several retrospective correction techniques are readily available. In this work, we use a digital phantom designed for the evaluation of connectivity pipelines. We subject the phantom to a âeurooetheoretically correctâeuro and plausible deformation that resembles the artifact under investigation. We correct data back, with three standard methodologies (namely fieldmap-based, reversed encoding-based, and registration- based). Finally, we rank the methods based on their geometrical accuracy, the dropout compensation, and their impact on the resulting connectivity matrices.

Diffantom: Whole-Brain Diffusion MRI Phantoms Derived from Real Datasets of the Human Connectome Project.

Food allergies are believed to be on the rise and currently management relies on the avoidance of the food. Hen's egg allergy is after cow's milk allergy the most common food allergy; eggs are used in many food products and thus difficult to avoid. A technological process using a combination of enzymatic hydrolysis and heat treatment was designed to produce modified hen's egg with reduced allergenic potential. Biochemical (SDS-PAGE, Size exclusion chromatography and LC-MS/MS) and immunological (ELISA, immunoblot, RBL-assays, animal model) analysis showed a clear decrease in intact proteins as well as a strong decrease of allergenicity. In a clinical study, 22 of the 24 patients with a confirmed egg allergy who underwent a double blind food challenge with the hydrolysed egg remained completely free of symptoms. Hydrolysed egg products may be beneficial as low allergenic foods for egg allergic patients to extent their diet. This article is protected by copyright. All rights reserved.

Therapy response with diffusion MRI: an update

The efficiency of an oncological treatment regimen is often assessed by morphological criteria such as tumour size evaluated by cross-sectional imaging, or by laboratory measurements of plasma biomarkers. Because these types of measures typically allow for assessment of treatment response several weeks or even months after the start of therapy, earlier response assessment that provides insight into tumour function is needed. This is particularly urgent for the evaluation of newer targeted therapies and for fractionated therapies that are delivered over a period of weeks to allow for a change of treatment in non-responding patients. Diffusion-weighted MRI (DW-MRI) is a non-invasive imaging tool that does not involve radiation or contrast media, and is sensitive to tissue microstructure and function on a cellular level. DW-MRI parameters have shown sensitivity to treatment response in a growing number of tumour types and organ sites, with additional potential as predictive parameters for treatment outcome. A brief overview of DW-MRI principles is provided here, followed by a review of recent literature in which DW-MRI has been used to monitor and predict tumour response to various therapeutic regimens.