Advanced image-processing techniques in magnetic resonance imaging for the investigation of brain pathologies and tumour angiogenesis

Magnetic resonance imaging (MRI) is increasingly being used in medical settings because of its ability to produce, non-invasively, high quality images of the inside of the human body. Since its introduction in early 70’s, more and more complex acquisition techniques have been proposed, raising MRI to be exploited in a wide spectrum of applications. Innovative MRI modalities, such as diffu- sion and functional imaging, require complex analysis techniques and advanced algorithms in order to extract useful information from the acquired data. The aim of the present work has been to develop and optimize state-of-the- art techniques to be applied in the analysis of MRI data both in experimental and clinical settings. During my doctoral program I have been actively involved in several research projects, each time facing many different issues. In this dissertation, however, I will report the results obtained in three most appealing projects I partecipated to. These projects were devoted (i) to the implementation of an innovative experimental protocol for functional MRI in laboratory animals, (ii) to the development of new methods for the analysis of Dynamic Contrast Enhanced MRI data in experimental tumour models and (iii) to the analysis of diffusion MRI data in stroke patients. Particular emphasis will be given to the technical aspects regarding the algorithms and processing methods used in the analysis of data. Apart from a brief introduction on magnetic resonance imaging principles, the dissertation is organised in three parts, each one of them covering a separate topic dealt with during my doctoral studies. Each chapter is self-contained, in that it gives an introduction about the issue faced in the study, describes the methods and the techniques employed, and concludes with a discussion about the results obtained. Chapter 3 illustrates the methodology proposed by our group for an innovative method of fMRI (Activation-Induced Manganese Enhanced MRI, AIM-MRI) in rats. The method is based on in-vivo, absolute quantification of Mn concentration in different brain regions performed by fast T1 mapping and coregistration to a rat brain atlas. This strategy allows to quantify the accumulation of Mn in different regions of the rat brain, which is strictly related to the ac- tivation status of each area. This mechanism is similar to what happens in classical functional MRI studies but, in addition, it might be used for functional experiments performed in awake animals. In chapter 4 we cover the topic of tissues classification from MRI images in some experimental tumour models and present some advanced image-processing techniques we introduced for the analysis of Dynamic Contrast-Enhanced MRI (DCEMRI) data. In particular, we proposed to estimate a set of characteristic features from DCEMRI time profiles which well describe their shape, and then to extract peculiar behaviours which discriminate each tissue inside the tumour by combining cluster analysis and machine learning techniques. We have tested several approaches, each time comparing them with the state-of-the-art techniques of analysis for this kind of data. Finally, the proposed approach has been validated in a real case application in order to assess the efficacy of an anti-cancer therapy. Chapter 5 addresses the study on post-stroke plasticity carried out in collab- oration with the Signal Processing Laboratory of the Swiss Federal Institute of Technology in Lausanne (EPFL). Brain structural connectivity was monitored with Diffusion Spectrum Imaging (DSI), i.e. a high angular resolution diffusion MRI technique, during the functional recovery from the stroke, at hyperacute, acute and chronic stages after the lesion onset. The reproducibility of extracted fibre tracts has been extensively studied on healthy subjects, and the parameter set of fiber-tracking algorithm giving the best results was then used in patient analysis. The extracted fibre bundles between each pair of cortical regions were characterized by means of several connectivity measures.

Bio Simulation of Brain Ventricle Dilation in Normal Pressure Hydrocephalus

Kamal Shahim

Hydrocephalus is a brain disease wherein the ventricles dilate and compress the parenchyma towards the skull. It is primarily characterized by the disruption of the cerebrospinal fluid (CSF) flow within the ventricular system. Normal pressure hydrocephalus (NPH) is a form of hydrocephalus for which the enlargement of ventricles occurs although the intracranial pressure (ICP) remains close to normal. The pressure gradient between the source of CSF production in the ventricles and the absorption sites is reported to be very low (∼1 mm Hg), i.e. within the experimental errors. The mechanism of NPH evolution is still obscure and its distinction from the other causes of dementia such as Alzheimer and neurodegenerative diseases is difficult. The present work contributes to a better understanding of the NPH mechanism in terms of CSF disturbances and/or parenchyma defects. To this end, imaging techniques such as Magnetic resonance imaging (MRI), Diffusion tensor imaging (DTI) and Magnetic resonance elastography (MRE) are used together with a finite element (FE) model. As a final step, NPH onset and evolution are clarified via a theoretical model for healthy and NPH brains assuming a spherical geometry. The proposed mechanism is further analyzed in a realistic 3D model of the brain parenchyma. Geometries of ventricular system and skull are obtained from MRI images of a human brain. DTI data are used to establish the fiber tracts direction as well as the local frame of anisotropic elasticity and permeability. The brain parenchyma is considered as a poro-elastic material where the tissue displacement and CSF flow are modeled using the Biot's theory. A link between the CSF diffusion and CSF permeability in brain parenchyma is established and the importance of space dependent CSF content and transverse isotropic (TI) permeability is highlighted in case of low pressure gradient hydrocephalus. Calculations are carried out to simulate the ventricular dilation using FE softwares such as MATLAB® and COMSOL®. The numerical results show that consideration of space dependent CSF content and TI permeability leads to a much more realistic model for NPH in terms of CSF velocity and CSF content. Anisotropic MRE experiment is conducted over selected slices of a healthy human brain. The experimental results are statistically refined and further used to assess the healthy brain stiffness as well as the degree of anisotropy in elasticity. Moreover, the constitutive behavior of the white matter is modeled as a composite material containing fiber tracts surrounded by a matrix; with the assumption of a low fiber-matrix bonding and fiber tract undulation. A non-linear elastic model is proposed in order to take into account the load transfer from white matter matrix to fiber tracts when these are fully stretched. The unknown value of the elastic coefficients in a sick brain is determined by using inverse modeling, i.e. by adjusting these coefficients so that the right ventricle dilation is obtained. It is demonstrated that NPH development can be associated with a degradation of the brain parenchyma elastic stiffness in NPH patients. It is shown that during NPH development, a load transfer from the white matter matrix (cell bodies and interstitial fluid) to fiber tracts takes place, initiating elastic anisotropy in white matter tissues at rather large strains. An analytical approach is developed to seek the underlying NPH mechanism in a simplified model of brain. Without further refinement in the constitutive equation or adding complexity to the material behavior, the Biot's formulation is regarded as the basis. However, an absorption term is added to the Biot's model to consider the possible transparenchymal CSF resorption. The ventricle stability concept is introduced and is further utilized to investigate the equilibrium positions. The influence of different biomechanical parameters on the stable ventricle geometry is assessed and the healthy and NPH equilibrium positions are found to be dependent in particular on the CSF seepage through the ventricle wall and the absorption and permeability coefficients of the brain parenchyma. Although very simple, the proposed analytical model is able to predict the onset and development of NPH conditions as a deviation from healthy conditions. Incorporating the stability concept in a more realistic geometry of brain (3D), the respective equilibrium positions are recaptured using the parameter values provided by the analytical spherical model. The disruption of ventricle surface during the NPH development increases CSF seepage and consequently the medium permeability. A dilation dependent permeability is moreover incorporated in a 3D model of the brain. The results emphasize the importance of strain dependent permeability which favors the ventricle equilibrations in more realistic geometries of brain. Future works might consider the time dependent deformation (creep effects and stress induced remodeling) of ventricles and the incorporation of anisotropic permeability and elasticity in the 3D model. The geometry should be extended to the full ventricular system including the subarachnoid spaces (SAS).

EPFL2011

Global tractography with embedded anatomical priors for quantitative connectivity analysis

Alessandro Daducci, Alia Lemkaddem, Didrik Hans Malcolm Skiöldebrand, Jean-Philippe Thiran

Tractography algorithms provide us with the ability to non-invasively reconstruct fiber pathways in the white matter (WM) by exploiting the directional information described with diffusion magnetic resonance. These methods could be divided into two major classes, local and global. Local methods reconstruct each fiber tract iteratively by considering only directional information at the voxel level and its neighborhood. Global methods, on the other hand, reconstruct all the fiber tracts of the whole brain simultaneously by solving a global energy minimization problem. The latter have shown improvements compared to previous techniques but these algorithms still suffer from an important shortcoming that is crucial in the context of brain connectivity analyses. As no anatomical priors are usually considered during the reconstruction process, the recovered fiber tracts are not guaranteed to connect cortical regions and, as a matter of fact, most of them stop prematurely in the WM; this violates important properties of neural connections, which are known to originate in the gray matter (GM) and develop in the WM. Hence, this shortcoming poses serious limitations for the use of these techniques for the assessment of the structural connectivity between brain regions and, de facto, it can potentially bias any subsequent analysis. Moreover, the estimated tracts are not quantitative, every fiber contributes with the same weight toward the predicted diffusion signal. In this work, we propose a novel approach for global tractography that is specifically designed for connectivity analysis applications which: (i) explicitly enforces anatomical priors of the tracts in the optimization and (ii) considers the effective contribution of each of them, i.e., volume, to the acquired diffusion magnetic resonance imaging (MRI) image. We evaluated our approach on both a realistic diffusion MRI phantom and in vivo data, and also compared its performance to existing tractography algorithms.

2014

Bio Simulation of Brain Ventricle Dilation in Normal Pressure Hydrocephalus

Kamal Shahim

EPFL2011

Global tractography with embedded anatomical priors for quantitative connectivity analysis

Alessandro Daducci, Alia Lemkaddem, Didrik Hans Malcolm Skiöldebrand, Jean-Philippe Thiran

2014