Showing papers by "Olivier Commowick published in 2014"

A Mathematical Framework for the Registration and Analysis of Multi-Fascicle Models for Population Studies of the Brain Microstructure

Abstract: Diffusion tensor imaging (DTI) is unable to represent the diffusion signal arising from multiple crossing fascicles and freely diffusing water molecules Generative models of the diffusion signal, such as multi-fascicle models, overcome this limitation by providing a parametric representation for the signal contribution of each population of water molecules These models are of great interest in population studies to characterize and compare the brain microstructural properties Central to population studies is the construction of an atlas and the registration of all subjects to it However, the appropriate definition of registration and atlasing methods for multi-fascicle models have proven challenging This paper proposes a mathematical framework to register and analyze multi-fascicle models Specifically, we define novel operators to achieve interpolation, smoothing and averaging of multi-fascicle models We also define a novel similarity metric to spatially align multi-fascicle models Our framework enables simultaneous comparisons of different microstructural properties that are confounded in conventional DTI The framework is validated on multi-fascicle models from 24 healthy subjects and 38 patients with tuberous sclerosis complex, 10 of whom have autism We demonstrate the use of the multi-fascicle models registration and analysis framework in a population study of autism spectrum disorder

Predictive value of imaging markers at multiple sclerosis disease onset based on gadolinium- and USPIO-enhanced MRI and machine learning.

Abstract: OBJECTIVES: A novel characterization of Clinically Isolated Syndrome (CIS) patients according to lesion patterns is proposed. More specifically, patients are classified according to the nature of inflammatory lesions patterns. It is expected that this characterization can infer new prospective figures from the earliest imaging signs of Multiple Sclerosis (MS), since it can provide a classification of different types of lesions across patients. METHODS: The method is based on a two-tiered classification. Initially, the spatio-temporal lesion patterns are classified. The discovered lesion patterns are then used to characterize groups of patients. The patient groups are validated using statistical measures and by correlations at 24-month follow-up with hypointense lesion loads. RESULTS: The methodology identified 3 statistically significantly different clusters of lesion patterns showing p-values smaller than 0.01. Moreover, these patterns defined at baseline correlated with chronic hypointense lesion volumes by follow-up with an [Formula: see text] score of [Formula: see text]. CONCLUSIONS: The proposed methodology is capable of identifying three major different lesion patterns that are heterogeneously present in patients, allowing a patient classification using only two MRI scans. This finding may lead to more accurate prognosis and thus to more suitable treatments at early stage of MS.

Longitudinal Intensity Normalization in Multiple Sclerosis Patients

Abstract: In recent years, there have been many Multiple Sclerosis (MS) studies using longitudinal MR images to study and characterize the MS lesion patterns. The intensity of similar anatomical tissues in MR images is often different because of the variability of the acquisition process and different scanners. This paper proposes a novel methodology for a longitudinal lesion analysis based on intensity standardization to minimize the inter-scan intensity difference. The intensity normalization maps parameters obtained using a robust Gaussian Mixture Model (GMM) estimation not affected by the presence of MS lesions. Experimental results demonstrate that our technique accurately performs the task of intensity standardization. We show consequently how the same technique can improve the results of longitudinal MS lesion detection.

Fast Identification of Optimal Fascicle Configurations from Standard Clinical Diffusion MRI Using Akaike Information Criterion

Abstract: Analytic multi-compartment models have gained a tremen- dous popularity in the recent literature for studying the brain white matter microstructure from diffusion MRI. This class of models require the number of compartments to be known in advance. In the white matter however, several non-collinear bundles of axons, termed fascicles, often coexist in a same voxel. Determining the optimal fascicle configuration is a model selection problem. In this paper, we aim at proposing a novel approach to identify such a configuration from clinical diffusion MRI where only few diffusion images can be ac- quired and time is of the essence. Starting from a set of fitted models with increasing number of fascicles, we use Akaike information criterion to estimate the probability of each can- didate model to be the best Kullback-Leibler model. These probabilities are then used to average the different candidate models and output an MCM with optimal fascicle configura- tion. This strategy is fast and can be adapted to any multi- compartment model. We illustrate its implementation with the ball-and-stick model and show that we obtain better re- sults on single-shell low angular resolution diffusion MRI, compared to the state-of-the-art automatic relevance detection method, in a shorter processing time.

Fast and robust detection of the optimal number of fascicles in diffusion images using model averaging theory

Abstract: Diffusion MRI enables non-invasive in vivo reconstruction of the white matter axon bundles hereafter referred to as fascicles. DTI is known to have a hard time depicting accurately this architecture in regions where multiple fascicles cross. New multi-compartment models [1,2,3] can unravel this issue provided that the number of fascicles is known in advance. This is a model selection problem that translates to finding the optimal number of fascicles. Recently, [4] proposed to use the generalization error to choose the best model based on its ability to predict new data that has not been used for its estimation, thus avoiding the common problem of over-fitting. Despite the excellent results obtained by this method, the generalization error needs to be estimated, which is a long process that takes up to a week on high resolution data such as the recently publicly released Human Connectome Project (HCP) data [5]. In this abstract, we introduce a new model selection approach that gives results at least as good as the generalization error with a dramatically reduced computation time, making it closer to a clinical applicability.

Quantitative Relaxation Templates for the Human Brain at 3T

Abstract: The development of brain Magentic Resonance Imaging (MRI) is driving increasing demand for quantitative measurements. Quantitative MRI (qMRI) templates of relaxation times and proton density can be of particular interest for dedicated clinical applications such as characterizing brain tissue abnormalities, as well as general research purposes. In this paper, we have developed a 3D qMRI statistical template generator consisting of T1, T2, T2* and rho* relaxometry maps from the human brain at 3T. The qMRI templates were built from a population of 20 normal controls, for which individual quantitative maps were estimated in a robust manner, accounting for acquisition artifacts and expected relationships between the relaxometry parameters. For validation, we fed the qMRI templates into a realistic MRI simulator to synthesize various MR-weighted images, and compared these images with the real MR acquisitions. High correlation coefficients (>0.80) show that the developed qMRI templates can be used as input dataset for MRI simulation community, which may be of great interest to clinical neuroscience field.

Simultaneous Estimation of T1, T2 and B1 Maps From Relaxometry MR Sequences

Abstract: Interest in quantitative MRI and relaxometry imaging is rapidly increasing to enable the discovery of new MRI disease imaging biomarkers. While DESPOT1 is a robust method for rapid whole-brain voxel-wise mapping of the longitudinal relaxation time (T1), the approach is inherently sensitive to inaccuracies in the transmitted flip angles, de- fined by the B1 inhomogeneity field, which become more severe at high field strengths (e.g., 3T). We propose a new approach for simultaneously mapping the B1 field, M0 (proton density), T1 and T2 relaxation times based on regular fast T1 and T2 relaxometry sequences. The new method is based on the intrinsic correlation between the T1 and T2 relaxometry sequences to jointly estimate all maps. It requires no additional sequence for the B1 correction. We evaluated our proposed algorithm on simulated and in-vivo data at 3T, demonstrating its improved accuracy with respect to regular separate estimation methods.

Statistical Analysis of White Matter Integrity for the Clinical Study of Typical Specific Language Impairment in Children

Abstract: Children affected by Specific Language Impairment (SLI) fail to develop a normal language capability. To date, the etiology of SLI remains largely unknown. It induces difficulties with oral language which cannot be directly attributed to intellectual deficit or other developmental delay. Whereas previous studies on SLI focused on the psychological and genetic aspects of the pathology, few imaging studies investigated defaults in neuroanatomy or brain function. We propose to investigate the integrity of white matter in SLI thanks to diffusion Magnetic Resonance Imaging . An exploratory analysis was performed without a prior on the impaired regions. A region of interest statistical analysis was performed based, first, on regions defined from Catani’s atlas and, then, on tractography-based regions. Both the mean fractional anisotropy and mean apparent diffusion coefficient were compared across groups. To the best of our knowledge, this is the first study focusing on white matter integrity in specific language impairment. Twenty-two children with SLI and 19 typically developing children were involved in this study. Overall, the tractography-based approach to group comparison was more sensitive than the classical ROI-based approach. Group differences between controls and SLI patients included decreases in FA in both the perisylvian and ventral pathways of language, comforting findings from previous functional studies.

Method of characterizing molecular diffusion within a body from a set of diffusion-weighted magnetic resonance signals and apparatus for carrying out such a method

Abstract: A computer-implemented method of characterizing molecular diffusion within a body from a set of diffusion-weighted magnetic resonance signals by computing a weighted average (AVM) of a plurality of multi-compartment diffusion models (EXM1, EXM2) comprising a same number of compartments, fitted to a set of diffusion-weighted magnetic resonance signals, said weighted average being computed using weights representative of a performance criterion of each of said models; wherein each of said multi-compartment diffusion models comprises a different number of subsets of compartments, the compartments of a same subset being identical to each other.

Analyse d'images spatio-temporelles dans la Sclérose en Plaques

Abstract: L'Imagerie par resonance magnetique (IRM) a emerge comme un puissant outil de diagnostic non invasif et de description de l'histoire naturelle des pathologies cerebrales. Ceci est particulierement le cas dans le contexte de la Sclerose en Plaques (SEP), pour le suivi cette maladie et de son traitement. L'IRM fournit des informations au niveau macroscopique, mais manque de sensibilite et de specificite dans l'identification de l'etendue de la pathologie sous-jacente. Avec l'avenement de medicaments modificateurs de la maladie, le developpement de marqueurs IRM robustes et specifiques pour caracteriser la pathologie au cours du temps devient un besoin crucial. Nous decrivons ici comment l'analyse de donnees IRM longitudinales associee a des descripteurs d'imagerie, peut permettre de mettre en evidence des formes specifiques de la maladie, des son commencement, et ainsi de mieux adapter les traitements en fonction du caractere predictif ainsi mis en evidence.

Tracking the Cortico-Spinal Tract as a Multi-Modal Distribution of Streamlines from Local White Matter Microstructure Models

Abstract: We present a pipeline to reconstruct the corticospinal tract (CST) that connects the spinal cord to the motor cortex. The proposed method combines a new white matter microstructure model coined Diffusion Directions Imaging and a new tractography algorithm based on a particle filter adapted for approximating multi-modal distributions. In this paper, we put the computation time and accuracy of our pipeline to the test in the context of the MICCAI 2014 DTI challenge, which aims to provide fast and accurate reconstructions of the CST for presurgical planning of brain tumor extraction. These two key performance metrics are expected in such a situation where time is of the essence and the quality of the data is dependent on the patient's health condition and ability to cooperate. In no more than 1.5 hours per patient, we successfully provide accurate CSTs of 2 very collaborative patients who underwent a diffusion MRI protocol that included 69 diffusion-sensitizing gradients spread over 4 different shells ranging from b = 200 to b = 3000 s/mm2.