11 research outputs found

    Watershed-based Segmentation of the Midsagittal Section of the Corpus Callosum in Diffusion MRI

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    Abstract -The corpus callosum (CC) is one of the most important white matter structures of the brain, interconnecting the two cerebral hemispheres. The CC is related to several diseases including dyslexia, autism, multiple sclerosis and lupus, which make its study even more important. We propose here a new approach for fully automatic segmentation of the midsagittal section of CC in magnetic resonance diffusion tensor images, including the automatic determination of the midsagittal slice of the brain . It uses the watershed transform and is performed on the fractional anisotropy map weighted by the projection of the principal eigenvector in the left-right direction. Experiments with real diffusion MRI data showed that the proposed method is able to quickly segment the CC and to the determinate the midsagittal slice without any user intervention. Since it is simple, fast a nd does not require parameter settings, the proposed method is well suited for clinical applications

    A stochastic tractography system and applications

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    Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (p. 75-77).Neuroscientists hypothesize that the pathologies of some neurological diseases are associated with neuroanatomical abnormalities. Diffusion Tensor Imaging (DTI) and stochastic tractography allow us to investigate white matter architecture non-invasively through measurements of water self diffusion throughout the brain. Many comparative studies of white matter architecture utilize spatially localized comparisons of diffusion characteristics. White matter tractography enables studies of fiber bundle characteristics. Stochastic tractography facilitates these investigations by providing a measure of confidence regarding the inferred fiber bundles. This thesis presents an implementation of an easy to use, open-source stochastic tractography system that will enable novel studies of fiber tract abnormalities. We demonstrate an application of the system on real DTI images and discuss possible studies of frontal lobe fiber differences in Schizophrenia.by Tri M. Ngo.M.Eng

    Cerebral white matter analysis using diffusion imaging

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    Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2006.Includes bibliographical references (p. 183-198).In this thesis we address the whole-brain tractography segmentation problem. Diffusion magnetic resonance imaging can be used to create a representation of white matter tracts in the brain via a process called tractography. Whole brain tractography outputs thousands of trajectories that each approximate a white matter fiber pathway. Our method performs automatic organization, or segmention, of these trajectories into anatomical regions and gives automatic region correspondence across subjects. Our method enables both the automatic group comparison of white matter anatomy and of its regional diffusion properties, and the creation of consistent white matter visualizations across subjects. We learn a model of common white matter structures by analyzing many registered tractography datasets simultaneously. Each trajectory is represented as a point in a high-dimensional spectral embedding space, and common structures are found by clustering in this space. By annotating the clusters with anatomical labels, we create a model that we call a high-dimensional white matter atlas.(cont.) Our atlas creation method discovers structures corresponding to expected white matter anatomy, such as the corpus callosum, uncinate fasciculus, cingulum bundles, arcuate fasciculus, etc. We show how to extend the spectral clustering solution, stored in the atlas, using the Nystrom method to perform automatic segmentation of tractography from novel subjects. This automatic tractography segmentation gives an automatic region correspondence across subjects when all subjects are labeled using the atlas. We show the resulting automatic region correspondences, demonstrate that our clustering method is reproducible, and show that the automatically segmented regions can be used for robust measurement of fractional anisotropy.by Lauren Jean O'Donnell.Ph.D

    Quantitative analysis of cerebral white matter anatomy from diffusion MRI

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    Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 165-177).In this thesis we develop algorithms for quantitative analysis of white matter fiber tracts from diffusion MRI. The presented methods enable us to look at the variation of a diffusion measure along a fiber tract in a single subject or a population, which allows important clinical studies toward understanding the relation between the changes in the diffusion measures and brain diseases, development, and aging. The proposed quantitative analysis is performed on a group of fiber trajectories extracted from diffusion MRI by a process called tractography. To enable the quantitative analysis we first need to cluster similar trajectories into groups that correspond to anatomical bundles and to establish the point correspondence between these variable-length trajectories. We propose a computationally-efficient approach to find the point correspondence and the distance between each trajectory to the prototype center of each bundle. Based on the computed distances we also develop a novel model-based clustering of trajectories into anatomically-known fiber bundles. In order to cluster the trajectories, we formulate an expectation maximization algorithm to infer the parameters of the gamma-mixture model that we built on the distances between trajectories and cluster centers. We also extend the proposed clustering algorithm to incorporate spatial anatomical information at different levels through hierarchical Bayesian modeling. We demonstrate the effectiveness of the proposed methods in several clinical applications. In particular, we present our findings in identifying localized group differences in fiber tracts between normal and schizophrenic populations.by Mahnaz Maddah.Ph.D

    Population-wise consistent segmentation of diffusion weighted magnetic resonance images

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    Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (p. 161-167).In this thesis, we investigate unsupervised and semi-supervised methods to construct anatomical atlases and segment medical images. We propose an integrated registration and clustering algorithm to compute an anatomical atlas of fiber-bundles as well as deep gray matter structures from a population of diffusion tensor MR images (DT-MRI). We refer to this algorithm as "Consistency Clustering" since the outputs of the algorithm include population-wise consistent segmentations and correspondence between the subjects. The consistency is ensured through using a single anatomical model for the whole population, which is similar to the atlases used by experts for manual labeling. We experiment with both parametric and non-parametric models for the gray matter and white matter segmentation problems, each model resulting in a different kind of atlas. Consistent population-wise segmentations require development of several integrated algorithms for clustering, registration, atlas-building and outlier rejection. In this thesis we develop, implement and evaluate these tools individually and together as a population-wise segmentation tool. Together, Consistency Clustering enables automatic atlas construction in DT-MRI for a population, either normal or affected by a neural disorder. Consistency Clustering also provides the user the choice to include prior knowledge through a few labeled subjects (semi-supervised) or compute an anatomical atlas in a completely data driven manner (unsupervised). Furthermore, resulting anatomical models are compact representations of populations and can be used for population-wise morphometry. We implement and evaluate these methods using in vivo DT-MRI datasets. We investigate the benefits of population-wise segmentation as opposed to individually segmenting subjects, as well as effects of noise and initialization on the segmentations.by Ulas Ziyan.Ph.D

    Doctor of Philosophy

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    dissertationDiffusion magnetic resonance imaging (dMRI) has become a popular technique to detect brain white matter structure. However, imaging noise, imaging artifacts, and modeling techniques, etc., create many uncertainties, which may generate misleading information for further analysis or applications, such as surgical planning. Therefore, how to analyze, effectively visualize, and reduce these uncertainties become very important research questions. In this dissertation, we present both rank-k decomposition and direct decomposition approaches based on spherical deconvolution to decompose the fiber directions more accurately for high angular resolution diffusion imaging (HARDI) data, which will reduce the uncertainties of the fiber directions. By applying volume rendering techniques to an ensemble of 3D orientation distribution function (ODF) glyphs, which we call SIP functions of diffusion shapes, one can elucidate the complex heteroscedastic structural variation in these local diffusion shapes. Furthermore, we quantify the extent of this variation by measuring the fraction of the volume of these shapes, which is consistent across all noise levels, the certain volume ratio. To better understand the uncertainties in white matter fiber tracks, we propose three metrics to quantify the differences between the results of diffusion tensor magnetic resonance imaging (DT-MRI) fiber tracking algorithms: the area between corresponding fibers of each bundle, the Earth Mover's Distance (EMD) between two fiber bundle volumes, and the current distance between two fiber bundle volumes. Based on these metrics, we discuss an interactive fiber track comparison visualization toolkit we have developed to visualize these uncertainties more efficiently. Physical phantoms, with high repeatability and reproducibility, are also designed with the hope of validating the dMRI techniques. In summary, this dissertation provides a better understanding about uncertainties in diffusion magnetic resonance imaging: where and how much are the uncertainties? How do we reduce these uncertainties? How can we possibly validate our algorithms

    Comparative Analysis of Connection and Disconnection in the Human Brain Using Diffusion MRI: New Methods and Applications

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    Institute for Adaptive and Neural ComputationDiffusion magnetic resonance imaging (dmri) is a technique that can be used to examine the diffusion characteristics of water in the living brain. A recently developed application of this technique is tractography, in which information from brain images obtained using dmri is used to reconstruct the pathways which connect regions of the brain together. Proxy measures for the integrity, or coherence, of these pathways have also been defined using dmri-derived information. The disconnection hypothesis suggests that specific neurological impairments can arise from damage to these pathways as a consequence of the resulting interruption of information flow between relevant areas of cortex. The development of dmri and tractography have generated a considerable amount of renewed interest in the disconnectionist thesis, since they promise a means for testing the hypothesis in vivo in any number of pathological scenarios. However, in order to investigate the effects of pathology on particular pathways, it is necessary to be able to reliably locate them in three-dimensional dmri images. The aim of the work described in this thesis is to improve upon the robustness of existing methods for segmenting specific white matter tracts from image data, using tractography, and to demonstrate the utility of the novel methods for the comparative analysis of white matter integrity in groups of subjects. The thesis begins with an overview of probability theory, which will be a recurring theme throughout what follows, and its application to machine learning. After reviewing the principles of magnetic resonance in general, and dmri and tractography in particular, we then describe existing methods for segmenting particular tracts from group data, and introduce a novel approach. Our innovation is to use a reference tract to define the topological characteristics of the tract of interest, and then search a group of candidate tracts in the target brain volume for the best match to this reference. In order to assess how well two tracts match we define a heuristic but quantitative tract similarity measure. In later chapters we demonstrate that this method is capable of successfully segmenting tracts of interest in both young and old, healthy and unhealthy brains; and then describe a formalised version of the approach which uses machine learning methods to match tracts from different subjects. In this case the similarity between tracts is represented as a matching probability under an explicit model of topological variability between equivalent tracts in different brains. Finally, we examine the possibility of comparing the integrity of groups of white matter structures at a level more fine-grained than a whole tract

    Spinal cord diffusion imaging: Challenging characterization and prognostic value

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    The aim of this thesis is to explore the potential of quantitative imaging mark¬ers derived from diffusion-weighted MRI (DW MRI) in the spinal cord to char¬acterise healthy white matter pathways and provide sensitivity to axonal dam¬age, regeneration and collateral sprouting in spinal cord disease. With new innovative treatment strategies emerging for spinal cord patholo¬gies such as spinal cord injury and Multiple Sclerosis, there is a need for new in-vivo biomarkers that can be specific to structural and functional changes and their underlying mechanisms on a microscopic scale. DW MRI has the potential to quantifying those microstructural characteristics beyond the scale of conventional MRI. In the first part of this dissertation I investigate Diffusion Tensor Imaging (DTI), which is the most established DW MRI analysis technique in clinical practice. In two studies we assess DTI in the context of spinal cord imaging. In the first experiment I show that DTI is sensitive to the presence of collateral fi¬bres, e.g., at inter-vertebral level where peripheral nerves enter the spinal tract. In the second experiment I propose a new method for reducing partial volume effects on whole cord DTI measurements, which is specifically tailored for the imaging and analysis challenges in the cord. The second part of this thesis comprises two studies of q-space imaging (QSI) in healthy controls. In theory, QSI offers a more comprehensive descrip¬tion of the diffusion process, but is challenging to set up on a clinical MRI scanner. I present here two QSI protocols, set up for two different scanners with different gradient hardware, receive coils and software limitations. For the first time we perform a systematic study of QSI that assesses the reproducibility and specificity to different white pathways in-vivo in the cervical cord within a group of healthy volunteers. Both studies show superior reproducibility of QSI over conventional analysis, although the results of using QSI parameters to distinguish individual white matter tracts in the cord were inconclusive. The third part of this thesis describes a new imaging method protocol based on the ActiveAx optimisation framework. It uses a complex multi- compartment model, which relates DW MRI data to microstructural parame¬ters like axon diameter and density. I design a new orientation aware method based ActiveAx, which incorporates the known fibre structure of the spinal cord. In a first step I validate the approach in in a post-mortem cervical spinal cord sample of a velveteen monkey. I then demonstrate clinical feasibility and good reproducibility of the new protocol for in-vivo human studies, using the corpus callosum as a preliminary model system for structures with uni¬directional fibre architecture. Finally I present first estimation results of axon diameter and density of the cervical spinal cord in-vivo in a healthy control that agree with the findings in the ex-vivo monkey spinal cord sample

    Micro-computed tomography for high resolution soft tissue imaging; applications in the normal and failing heart

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    The normal structure and function of the heart, the common pathological changes that cause abnormal function and the interventions proposed to improve or restore its function are fundamentally based on cardiac anatomy. Therefore in all these areas a detailed and accurate understanding of 3D structure is essential. However there is still disparity over some aspects of the form and function of the healthy heart. Furthermore, in heart failure (HF) the transition from compensated to decompensated HF is poorly understood, and details of ventricular, and particularly atrial, remodelling and their effects on cardiac function are yet to be fully elucidated. In addition little is known on how the 3D morphology of the cardiac conduction system is affected in disease, and further knowledge is required on the structural substrates for arrhythmogenesis associated with HF. Here we have developed contrast enhanced micro-CT for soft tissue imaging, allowing non-invasive high resolution (~5 µm attainable) differentiation of multiple soft tissue types including; muscle, connective tissue and fat. Micro-CT was optimised for imaging of whole intact mammalian hearts and from these data we reveal novel morphological and anatomical detail in healthy hearts and in hearts after experimental HF (volume and pressure overload). Remodelling of the myocardium in HF was dramatic with significant hypertrophy and dilatation observed in both atria and ventricles. The atria showed a 67% increase in myocardial volume, with the left atrium showing a 93% increase. The pectinate muscle: wall thickness ratio was significantly increased in both atria (p

    Mathematical modelling of cardiac function: constitutive law, fibre dispersion, growth and remodelling

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    The heart is an immensely complex living organ. Myocardium has continually been undergoing adaptive or maladaptive response to surrounding environments, in which the significant importance of growth and remodelling (G&R) has been valued. This PhD project intends to study mechanics modelling of myocardium towards predictive stress/strain-driven growth. Constitutive laws and fibre structures in myocardium work together to determine the mechanical clues which trigger the growth mechanically. Therefore, this project includes two parts: (1) constitutive characterization of myocardium, and (2) myocardial G&R. Constitutive laws and myofibre architectures hold the key to accurately model the biomechanical behaviours of the heart. In the first part of this thesis, we firstly perform an analysis using combinations of uniaxial tension, biaxial tension and simple shear from three different sets of myocardial experimental tissue studies to investigate the descriptive and predictive capabilities of a general invariant-based model that is developed by Holzapfel and Ogden, denoted the HO model. We aim to reduce the constitutive law using the Akaike information criterion to maintain its mechanical integrity whilst achieve minimal computational cost. Our study shows that single-mode tests are insufficient to determine the myocardium responses. It is also essential to consider the transmural fibre rotation within the myocardial samples. We conclude that a competent myocardial material model can be obtained from the general HO model using Akaike information criterion analysis and a suitable combination of tissue tests. Secondly, we develop a neonatal porcine bi-ventricle model with three different myofibre architectures for the left side of the heart. The most realistic one is derived from ex vivo diffusion tensor magnetic resonance image, and the other two simplifications are based on the rule-based methods. We show that the most realistic myofibre architecture model can achieve better cardiac pump functions compared to those of the rule-based models under the same pre/after loads. Our results also reveal that when the cross-fibre contraction is included, the active stress seems to play a dual role: the sheet-normal component enhances the ventricular contraction while the sheet component does the opposite. This study highlights the importance of including myofibre dispersion in cardiac modelling if rule-based methods are used, especially in personalized model. To further describe the detailed fibre distribution, discrete fibre dispersion method is employed to compute passive response because of its advantages in excluding compressed fibres. An additive active stress method that includes cross-fibre active stress is proposed according to the generalised structure tensor method. We find that end-systolic volumes of simulated heart models are much more sensitive to dispersion parameter than end-diastolic volumes. G&R is the focus in the second part of this thesis. An updated reference approach is employed to track the evolution of the reference configuration during G&R, in which the nodal positions and the fibre structure are updated at the beginning of each new growth cycle. Moreover, the homogenised constrained mixture theory is used to describe the G&R process of each constituent within myocardium, which are the ground matrix, collagen network and myofibres. Our models can reproduce the eccentric growth driven by fibre stretch at the diastole, concentric growth driven by fibre stress at the systole, and heterogeneous growth after acute myocardium infarction. Ventricular wall G&R mainly occurs in endocardium, in which the myocyte is the primary responder for the G&R process. G&R laws of collagen fibre have significant impacts on G&R of heart. For example, purely remodeled collagen network without new deposition causes increasingly softer heart wall, leading to excessive heart dilation. Finally, the effects of fibre dispersion on G&R is investigated by including fibre dispersion model in the G&R of infarction model. Highly dispersed fibre structure in the infarcted zone significantly reduces the pump function. This thesis has been focusing on mathematical modelling of biomechanical behaviours of myocardium, firstly on the nonlinear cardiac mechanics including constitutive laws and fibre structures, and then on the G&R process of heart under different pathological conditions. These studies support to choose suitable constitutive laws and fibre architectures in G&R model and illustrate the underlying mechanism of mechanical triggers in G&R. It presents the potential for understanding the mechanics of heart failure and reveal hidden roles of different constituents in myocardium
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