White matter | EPFL Graph Search

White matter refers to areas of the central nervous system (CNS) that are mainly made up of myelinated axons, also called tracts. Long thought to be passive tissue, white matter affects learning and brain functions, modulating the distribution of action potentials, acting as a relay and coordinating communication between different brain regions. White matter is named for its relatively light appearance resulting from the lipid content of myelin. However, the tissue of the freshly cut brain appears pinkish-white to the naked eye because myelin is composed largely of lipid tissue veined with capillaries. Its white color in prepared specimens is due to its usual preservation in formaldehyde. Structure White matter White matter is composed of bundles, which connect various grey matter areas (the locations of nerve cell bodies) of the brain to each other, and carry nerve impulses between neurons. Myelin acts as an insulator, which allows electrical signals to jump, r

Neurite Exchange Imaging ((NEXI): A minimal model of diffusion in gray matter with inter-compartment water exchange

Alexandre Jean François Marie De Skowronski, Ileana Ozana Jelescu

Biophysical models of diffusion in white matter have been center-stage over the past two decades and are essentially based on what is now commonly referred to as the "Standard Model" (SM) of non-exchanging anisotropic compartments with Gaussian diffusion. In this work, we focus on diffusion MRI in gray matter, which requires rethinking basic microstructure modeling blocks. In particular, at least three contributions beyond the SM need to be considered for gray matter: water exchange across the cell membrane - between neurites and the extracellular space; non-Gaussian diffusion along neuronal and glial processes - resulting from structural disorder; and signal contribution from soma. For the first contribution, we propose Neurite Exchange Imaging (NEXI) as an extension of the SM of diffusion, which builds on the anisotropic Karger model of two exchanging compartments. Using datasets acquired at multiple diffusion weightings (b) and diffusion times (t) in the rat brain in vivo, we investigate the suitability of NEXI to describe the diffusion signal in the gray matter, compared to the other two possible contributions. Our results for the diffusion time window 20-45 ms show minimal diffusivity time-dependence and more pronounced kurtosis decay with time, which is well fit by the exchange model. Moreover, we observe lower signal for longer diffusion times at high b. In light of these observations, we identify exchange as the mechanism that best explains these signal signatures in both low-b and high-b regime, and thereby propose NEXI as the minimal model for gray matter microstructure mapping. We finally highlight multi-b multi-t acquisition protocols as being best suited to estimate NEXI model parameters reliably. Using this approach, we estimate the inter-compartment water exchange time to be 15 - 60 ms in the rat cortex and hippocampus in vivo, which is of the same order or shorter than the diffusion time in typical diffusion MRI acquisitions. This suggests water exchange as an essential component for interpreting diffusion MRI measurements in gray matter.

ACADEMIC PRESS INC ELSEVIER SCIENCE2022

From volumetric graphical objects to surface registration

Eduardo Solanas Vilar

Relatively recent developments in medical imaging allow the anatomical and functional noninvasive study of internal organs of the body. One of the most complex and interesting organs in our body is the brain. Many efforts are being carried out to try to understand how it works, its internal connections, how different zones interact with each other, why, in some cases, a zone takes in charge a functionality of another damaged one, etc.; the questions are innumerable and the answers, if any, are sparse. Recently, magnetic resonance imaging (MRI) equipments have been developed to capture the functional activity of the brain. Unfortunately, functional images suffer from a lack of spatial resolution. Thus, the need of combining them with anatomical ones, which have a much higher quality. Nowadays these images provide precious information for diagnosis purposes and this is the reason for the need of imaging tools to extract and analyze the information which is present in those images, and which sometimes is not appreciable for the human eye. Acquired MRI images are presented as volumetric sets of data with which one can reconstruct a three dimensional volumetric model of the scanned organ. Nevertheless, sometimes physicians are just interested in the surface of the organ, for example in brain functional activation studies. In addition, many times it is important to be able to carry out group studies, or to locate a lesion with respect to a standard reference. To do such studies it is necessary to perform a transformation of the surfaces in order to put them into correspondence. Such technique is called registration. Unfortunately, volumetric registration does not give satisfactory results for surface registration purposes. The solution is to work directly with surfaces instead of processing the volumetric data. Unfortunately we do not have information about the surfaces, hence the need of computer graphics and computer vision techniques. The present work proposes a number of methods to help in the task of providing information from volumetric objects to surface registration. To achieve that some stages are necessary. The proposed stages are independent one from another so they can be used, or not, depending on the particular need. They can even be used for other purposes than those presented in the present work. The first two stages address the problem of image quality. This is a very important "preprocessing" step because posterior processing will depend on the image quality. Usually MR images are corrupted with different artifacts; among them a spatial-dependent bias field and a Riciandistributed noise. For the former a supervised correction is proposed, and for the later a directional spatial filter using a novel diffusivity function. After both stages the volumetric MRI data is ready to be classified. The purpose of the proposed classification process is to extract the white matter of the brain, given that sulci are better extracted from white matter than from the gray one. So far we have handled volumetric data, the next stage is concerned with the registration of the reconstructed white matter surface. This is achieved by means of a hierarchical representation of brain's sulci using level sets. The hierarchical representation and surface registration are carried out using the theory of partial differential equations (PDE) and level set methods. Validation studies are carried out independently in order to show the performance of the proposed approaches. Also a full scheme is proposed with the aim of presenting a complete approach from volumetric objects to surface registration.

EPFL2004

Design and Validation of Diffusion MRI Models of White Matter

Ileana Ozana Jelescu

Diffusion MRI is arguably the method of choice for characterizing white matter microstructure in vivo. Over the typical duration of diffusion encoding, the displacement of water molecules is conveniently on a length scale similar to that of the underlying cellular structures. Moreover, water molecules in white matter are largely compartmentalized which enables biologically-inspired compartmental diffusion models to characterize and quantify the true biological microstructure. A plethora of white matter models have been proposed. However, overparameterization and mathematical fitting complications encourage the introduction of simplifying assumptions that vary between different approaches. These choices impact the quantitative estimation of model parameters with potential detriments to their biological accuracy and promised specificity. First, we review biophysical white matter models in use and recapitulate their underlying assumptions and realms of applicability. Second, we present up-to-date efforts to validate parameters estimated from biophysical models. Simulations and dedicated phantoms are useful in assessing the performance of models when the ground truth is known. However, the biggest challenge remains the validation of the "biological accuracy" of estimated parameters. Complementary techniques such as microscopy of fixed tissue specimens have facilitated direct comparisons of estimates of white matter fiber orientation and densities. However, validation of compartmental diffusivities remains challenging, and complementary MRI-based techniques such as alternative diffusion encodings, compartment-specific contrast agents and metabolites have been used to validate diffusion models. Finally, white matter injury and disease pose additional challenges to modeling, which are also discussed. This review aims to provide an overview of the current state of models and their validation and to stimulate further research in the field to solve the remaining open questions and converge toward consensus.

Frontiers Research Foundation2017