Benoit Scherrer

Loss of white matter microstructural integrity is associated with adverse neurological outcome in tuberous sclerosis complex.

RATIONALE AND OBJECTIVES Tuberous sclerosis complex (TSC) is a genetic neurocutaneous syndrome in which cognitive and social-behavioral outcomes for patients vary widely in an unpredictable manner. The cause of adverse neurologic outcome remains unclear. The aim of this study was to investigate the hypothesis that disordered white matter and abnormal neural connectivity are associated with adverse neurologic outcomes. MATERIALS AND METHODS Structural and diffusion magnetic resonance imaging was carried out in 40 subjects with TSC (age range, 0.5-25 years; mean age, 7.2 years; median age, 5 years), 12 of whom had autism spectrum disorders (ASD), and in 29 age-matched controls. Tractography of the corpus callosum was used to define a three-dimensional volume of interest. Regional averages of four diffusion scalar parameters of the callosal projections were calculated for each subject. These were the average fractional anisotropy (AFA) and the average mean, radial, and axial diffusivity. RESULTS Subjects with TSC had significantly lower AFA and higher average mean, radial, and axial diffusivity values compared to controls. Subjects with TSC and ASD had significantly lower AFA values compared to those without ASD and compared to controls. Subjects with TSC without ASD had similar AFA values compared to controls. CONCLUSION Diffusion tensor scalar parameters provided measures of properties of the three-dimensional callosal projections. In TSC, changes in these parameters may reflect microstructural changes in myelination, axonal integrity, or extracellular environment. Alterations in white matter microstructural properties were associated with TSC, and larger changes were associated with TSC and ASD, thus establishing a relationship between altered white matter microstructural integrity and brain function.

Super-resolution reconstruction to increase the spatial resolution of diffusion weighted images from orthogonal anisotropic acquisitions

Diffusion-weighted imaging (DWI) enables non-invasive investigation and characterization of the white matter but suffers from a relatively poor spatial resolution. Increasing the spatial resolution in DWI is challenging with a single-shot EPI acquisition due to the decreased signal-to-noise ratio and T2(∗) relaxation effect amplified with increased echo time. In this work we propose a super-resolution reconstruction (SRR) technique based on the acquisition of multiple anisotropic orthogonal DWI scans. DWI scans acquired in different planes are not typically closely aligned due to the geometric distortion introduced by magnetic susceptibility differences in each phase-encoding direction. We compensate each scan for geometric distortion by acquisition of a dual echo gradient echo field map, providing an estimate of the field inhomogeneity. We address the problem of patient motion by aligning the volumes in both space and q-space. The SRR is formulated as a maximum a posteriori problem. It relies on a volume acquisition model which describes how the acquired scans are observations of an unknown high-resolution image which we aim to recover. Our model enables the introduction of image priors that exploit spatial homogeneity and enables regularized solutions. We detail our SRR optimization procedure and report experiments including numerical simulations, synthetic SRR and real world SRR. In particular, we demonstrate that combining distortion compensation and SRR provides better results than acquisition of a single isotropic scan for the same acquisition duration time. Importantly, SRR enables DWI with resolution beyond the scanner hardware limitations. This work provides the first evidence that SRR, which employs conventional single shot EPI techniques, enables resolution enhancement in DWI, and may dramatically impact the role of DWI in both neuroscience and clinical applications.

Distributed Local MRF Models for Tissue and Structure Brain Segmentation

Accurate tissue and structure segmentation of magnetic resonance (MR) brain scans is critical in several applications. In most approaches this task is handled through two sequential steps. We propose to carry out cooperatively both tissue and subcortical structure segmentation by distributing a set of local and cooperative Markov random field (MRF) models. Tissue segmentation is performed by partitioning the volume into subvolumes where local MRFs are estimated in cooperation with their neighbors to ensure consistency. Local estimation fits precisely to the local intensity distribution and thus handles nonuniformity of intensity without any bias field modelization. Similarly, subcortical structure segmentation is performed via local MRF models that integrate localization constraints provided by a priori fuzzy description of brain anatomy. Subcortical structure segmentation is not reduced to a subsequent processing step but joined with tissue segmentation: the two procedures cooperate to gradually and conjointly improve model accuracy. We propose a framework to implement this distributed modeling integrating cooperation, coordination, and local model checking in an efficient way. Its evaluation was performed using both phantoms and real 3 T brain scans, showing good results and in particular robustness to nonuniformity and noise with a low computational cost. This original combination of local MRF models, including anatomical knowledge, appears as a powerful and promising approach for MR brain scan segmentation.

Multimodal MRI segmentation of ischemic stroke lesions

The problem addressed in this paper is the automatic segmentation of stroke lesions on MR multi-sequences. Lesions enhance differently depending on the MR modality and there is an obvious gain in trying to account for various sources of information in a single procedure. To this aim, we propose a multimodal Markov random field model which includes all MR modalities simultaneously. The results of the multimodal method proposed are compared with those obtained with a mono-dimensional segmentation applied on each MRI sequence separately. We constructed an Atlas of blood supply territories to help clinicians in the determination of stroke subtypes and potential functional deficit.

Impaired Language Pathways in Tuberous Sclerosis Complex Patients with Autism Spectrum Disorders

The purpose of this study was to examine the relationship between language pathways and autism spectrum disorders (ASDs) in patients with tuberous sclerosis complex (TSC). An advanced diffusion-weighted magnetic resonance imaging (MRI) was performed on 42 patients with TSC and 42 age-matched controls. Using a validated automatic method, white matter language pathways were identified and microstructural characteristics were extracted, including fractional anisotropy (FA) and mean diffusivity (MD). Among 42 patients with TSC, 12 had ASD (29%). After controlling for age, TSC patients without ASD had a lower FA than controls in the arcuate fasciculus (AF); TSC patients with ASD had even a smaller FA, lower than the FA for those without ASD. Similarly, TSC patients without ASD had a greater MD than controls in the AF; TSC patients with ASD had even a higher MD, greater than the MD in those without ASD. It remains unclear why some patients with TSC develop ASD, while others have better language and socio-behavioral outcomes. Our results suggest that language pathway microstructure may serve as a marker of the risk of ASD in TSC patients. Impaired microstructure in language pathways of TSC patients may indicate the development of ASD, although prospective studies of language pathway development and ASD diagnosis in TSC remain essential.

Parametric Representation of Multiple White Matter Fascicles from Cube and Sphere Diffusion MRI

The characterization of the complex diffusion signal arising from the brain remains an open problem. Many representations focus on characterizing the global shape of the diffusion profile at each voxel and are limited to the assessment of connectivity. In contrast, Multiple Fascicle Models (MFM) seek to represent the contribution from each white matter fascicle and may be useful in the investigation of both white matter connectivity and diffusion properties of each individual fascicle. However, the most appropriate representation of multiple fascicles remains unclear. In particular, a multiple tensor representation of multiple fascicles has frequently been reported to be numerically challenging and unstable. We provide here the first analytical demonstration that when using a diffusion MRI acquisition with only one non-zero b-value, such as in conventional single-shell HARDI acquisition, a co-linearity in model parameters makes the precise model estimation impossible. Motivated by this theoretical result, we propose the novel CUSP (CUbe and SPhere) optimal acquisition scheme to achieve multiple non-zero b-values. It combines the gradients of a single-shell HARDI with gradients in its enclosing cube, in which varying b-values can be acquired by modulation of the gradient strength, without modifying the minimum echo time. Compared to a multi-shell HARDI acquisition, our scheme has significantly increased signal-to-noise ratio. We propose a novel estimation algorithm that enables efficient, robust and accurate estimation of the parameters of a multi-tensor model. In conjunction with a CUSP acquisition, it enables full estimation of the multi-tensor model. We present an evaluation of CUSP-MFM on both synthetic phantoms and invivo data. We report qualitative and quantitative experimental evaluations which demonstrate the ability of CUSP-MFM to characterize multiple fascicles from short duration acquisitions. CUSP-MFM enables rapid and effective investigation of multiple white matter fascicles, in both normal development and in disease and injury, in research and clinical practice.

Characterizing brain tissue by assessment of the distribution of anisotropic microstructural environments in diffusion-compartment imaging (DIAMOND)

To develop a statistical model for the tridimensional diffusion MRI signal at each voxel that describes the signal arising from each tissue compartment in each voxel.

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

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.

Why multiple b-values are required for multi-tensor models. evaluation with a constrained log-euclidean model

Multi-tensor models have been proposed to assess multiple fiber orientations but are known to be numerically challenging. We show that the estimation cannot be performed with a single-shell HARDI acquisition because the fitting procedure leads to an infinite number of solutions ; multiple-shell HARDI acquisitions are required. Additionally, we propose a new log-euclidean constrained two-tensor model capable of assessing crossing fibers configurations with a relative limited number of DW acquisitions. We provide numerical experiments with this model to verify experimentally the necessity of multiple-shell HARDI acquisitions schemes for multitensor models.

LOCUS: local cooperative unified segmentation of MRI brain scans

We propose to carry out cooperatively both tissue and structure segmentations by distributing a set of local and cooperative models in a unified MRF framework. Tissue segmentation is performed by partitionning the volume into subvolumes where local MRFs are estimated in cooperation with their neighbors to ensure consistency. Local estimation fits precisely to the local intensity distribution and thus handles nonuniformity of intensity without any bias field modelization. Structure segmentation is performed via local MRFs that integrate localization constraints provided by a priori general fuzzy description of brain anatomy. Structure segmentation is not reduced to a postprocessing step but cooperates with tissue segmentation to gradually and conjointly improve models accuracy. The evaluation was performed using phantoms and real 3T brain scans. It shows good results and in particular robustness to nonuniformity and noise with a low computational cost.