Leading aetiologies of ischaemic stroke in young adults are cervico-cerebral arterial dissections and cardio-embolism, but the causes remain undetermined in a considerable proportion of cases. In a few reports, intracranial arterial stenosis has been suggested to be a potential cause of ischaemic stroke in young adults. The aim of our work was to evaluate the frequency, characteristics and risk factors of intracranial arterial stenosis in a prospective series of young ischaemic stroke patients.
The first goal of this study is to compare gadofosveset trisodium--a gadolinium agent that reversibly binds to albumin--to an extracellular contrast agent (Gd-DOTA) for the detection of multiple sclerosis lesions. The second goal is to determine the best postinjection time for the detection of contrast-enhanced lesions.
Brain atrophy is considered an important marker of disease progression in many chronic neuro-degenerative diseases such as multiple sclerosis (MS). A great deal of attention is being paid toward developing tools that manipulate magnetic resonance (MR) images for obtaining an accurate estimate of atrophy. Nevertheless, artifacts in MR images, inaccuracies of intermediate steps and inadequacies of the mathematical model representing the physical brain volume change, make it rather difficult to obtain a precise and unbiased estimate. This work revolves around the nature and magnitude of bias in atrophy estimations as well as a potential way of correcting them. First, we demonstrate that for different atrophy estimation methods, bias estimates exhibit varying relations to the expected atrophy and these bias estimates are of the order of the expected atrophies for standard algorithms, stressing the need for bias correction procedures. Next, a framework for estimating uncertainty in longitudinal brain atrophy by means of constructing confidence intervals is developed. Errors arising from MRI artifacts and bias in estimations are learned from example atrophy simulations and anatomies. Results are discussed for three popular non-rigid registration approaches with the help of simulated localized brain atrophy in real MR images.
Diffusion weighted magnetic resonance imaging (DW-MRI) makes it possible to probe brain connections in vivo. This paper presents a change detection framework that relies on white matter pathways with application to neuromyelitis optica (NMO). The objective is to detect local or global fiber diffusion property modifications between two longitudinal DW-MRI acquisitions of a patient. To this end, we develop two frameworks based on statistical tests on tensor eigenvalues to detect local or global changes along the white matter pathways: a pointwise test that compares tensor populations extracted in bundles cross sections and a fiberwise test that compares paired tensors along all the fiber bundles. Experiments on both synthetic and real data highlight the benefit of considering fiber based statistical tests compared to standard voxelwise strategies.
The automatic analysis of subtle changes between MRI scans is an important tool for monitoring disease evolution. Several methods have been proposed to detect changes in serial conventional MRI but few works have considered Diffusion Tensor Imaging (DTI), which is a promising modality for monitoring neurodegenerative disease and particularly Multiple Sclerosis (MS). In this paper, we introduce a comprehensive framework for detecting changes between two DTI acquisitions by considering different levels of representation of diffusion imaging, namely the Apparent Diffusion Coefficient (ADC) images, the diffusion tensor fields, and scalar images characterizing diffusion properties such as the fractional anisotropy and the mean diffusivity. The proposed statistical method for change detection is based on the Generalized Likelihood Ratio Test (GLRT) that has been derived for the different diffusion imaging representations, based on the core assumption of a Gaussian diffusion model and of an additive Gaussian noise on the ADCs. Results on synthetic and real images demonstrate the ability of the different tests to bring useful and complementary information in the context of the follow-up of MS patients.
In the case of head trauma, elongation of axons is thought to result in brain damage and to lead to Diffuse Axonal Injuries (DAI). Mechanical parameters have been previously proposed as DAI metric. Typically, brain injury parameters are expressed in terms of pressure, shearing stresses or invariants of the strain tensor. Addressing axonal deformation within the brain during head impact can improve our understanding of DAI mechanisms. A new technique based on directional measurements of water diffusion in soft tissue using Magnetic Resonance Imaging (MRI), called Diffusion Tensor Imaging (DTI), provides information on axonal orientation within the brain. The present study aims at coupling axonal orientation from a 12-patient-based DTI 3D picture, called "DTI atlas", with the Strasbourg University Finite Element Head Model (SUFEHM). This information is then integrated in head trauma simulation by computing axonal elongation for each finite element of the brain model in a post-processing of classical simulation results. Axonal elongation was selected as computation endpoint for its strong potential as a parameter for DAI prediction and location. After detailing the coupling technique between DTI atlas and the head FE model, two head trauma cases presenting different DAI injury levels are reconstructed and analyzed with the developed methodology as an illustration of axonal elongation computation. Results show that anisotropic brain structures can be realistically implemented into an existing finite element model of the brain. The feasibility of integrating axon fiber direction information within a dedicated post-processor is also established in the context of the computation of axonal elongation. The accuracy obtained when estimating level and location of the computed axonal elongation indicates that coupling classical isotropic finite element simulation with axonal structural anisotropy is an efficient strategy. Using this method, tensile elongation of the axons can be directly invoked as a mechanism for Diffuse Axonal Injury.
Caffeine is not considered addictive, and in animals it does not trigger metabolic increases or dopamine release in brain areas involved in reinforcement and reward. Our objective was to measure caffeine effects on cerebral perfusion in humans using single photon emission computed tomography with a specific focus on areas of reinforcement and reward. Two groups of nonsmoking subjects were studied, one with a low (8 subjects) and one with a high (6 subjects) daily coffee consumption. The subjects ingested 3 mg/kg caffeine or placebo in a raspberry-tasting drink, and scans were performed 45 min after ingestion. A control group of 12 healthy volunteers receiving no drink was also studied. Caffeine consumption led to a generalized, statistically nonsignificant perfusion decrease of 6% to 8%, comparable in low and high consumers. Compared with controls, low consumers displayed neuronal activation bilaterally in inferior frontal gyrus-anterior insular cortex and uncus, left internal parietal cortex, right lingual gyrus, and cerebellum. In high consumers, brain activation occurred bilaterally only in hypothalamus. Thus, on a background of widespread low-amplitude perfusion decrease, caffeine activates a few regions mainly involved in the control of vigilance, anxiety, and cardiovascular regulation, but does not affect areas involved in reinforcing and reward.
Neuromyelitis optica (NMO) is an inflammatory disease of central nervous system characterized by optic neuritis and longitudinally extensive acute transverse myelitis. NMO patients have cognitive dysfunctions but other clinical symptoms of brain origin are rare. In the present study, we aimed to investigate cognitive functions and brain volume in NMO. The study population consisted of 28 patients with NMO and 28 healthy control subjects matched for age, sex and educational level. We applied a French translation of the Brief Repeatable Battery (BRB-N) to the NMO patients. Using SIENAx for global brain volume (Grey Matter, GM; White Matter, WM; and whole brain) and VBM for focal brain volume (GM and WM), NMO patients and controls were compared. Voxel-level correlations between diminished brain concentration and cognitive performance for each tests were performed. Focal and global brain volume of NMO patients with and without cognitive impairment were also compared. Fifteen NMO patients (54%) had cognitive impairment with memory, executive function, attention and speed of information processing deficits. Global and focal brain atrophy of WM but not Grey Matter (GM) was found in the NMO patients group. The focal WM atrophy included the optic chiasm, pons, cerebellum, the corpus callosum and parts of the frontal, temporal and parietal lobes, including superior longitudinal fascicle. Visual memory, verbal memory, speed of information processing, short-term memory and executive functions were correlated to focal WM volumes. The comparison of patients with, to patients without cognitive impairment showed a clear decrease of global and focal WM, including brainstem, corticospinal tracts, corpus callosum but also superior and inferior longitudinal fascicles. Cognitive impairment in NMO patients is correlated to the decreased of global and focal WM volume of the brain. Further studies are needed to better understand the precise origin of cognitive impairment in NMO patients, particularly in the WM.
This paper presents a longitudinal change detection framework for detecting relevant modifications in diffusion MRI, with application to neuromyelitis optica (NMO) and multiple sclerosis (MS). The core problem is to identify image regions that are significantly different between two scans. The proposed method is based on multivariate statistical testing which was initially introduced for tensor population comparison. We use this method in the context of longitudinal change detection by considering several strategies to build sets of tensors characterizing the variability of each voxel. These strategies make use of the variability existing in the diffusion weighted images (thanks to a bootstrap procedure), or in the spatial neighborhood of the considered voxel, or a combination of both. Results on synthetic evolutions and on real data are presented. Interestingly, experiments on NMO patients highlight the ability of the proposed approach to detect changes in the normal-appearing white matter (according to conventional MRI) that are related with physical status outcome. Experiments on MS patients highlight the ability of the proposed approach to detect changes in evolving and non-evolving lesions (according to conventional MRI). These findings might open promising prospects for the follow-up of NMO and MS pathologies.
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