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Understanding the relationship between structure and function is a major challenge in neuroscience. Diffusion MRI (dMRI) in the only non-invasive modality allowing to have access to the neural structure. Magnetoencephalography (MEG) is another non-invasive modality that allows a direct access to the temporal succession of cognitive processes. Functional cortex parcellation being one of the most important...
Fiber tracking from diffusion tensor images is an essential step in numerous clinical applications. There is a growing demand for an accurate and efficient framework to perform quantitative analysis of white matter fiber bundles. In this paper, we propose a robust framework for fiber clustering. This framework is composed of two parts: accessible fiber representation, and a statistically robust divergence...
Diffusion imaging can map anatomical connectivity in the living brain, offering new insights into fundamental questions such as how the left and right brain hemispheres differ. Anatomical brain asymmetries are related to speech and language abilities, but less is known about left/right hemisphere differences in brain wiring. To assess this, we scanned 457 young adults (age 23.4±2.0 SD years) and 112...
The presence of noise in High Angular Resolution Diffusion Imaging (HARDI) data of the brain can limit the accuracy with which fiber pathways of the brain can be extracted. In this work, we present a variational model to denoise HARDI data corrupted by Rician noise. Numerical experiments are performed on three types of data: 2D synthetic data, 3D diffusion-weighted Magnetic Resonance Imaging (DW-MRI)...
The unscented Kalman filter (UKF) was recently introduced in literature for simultaneous multi-tensor estimation and tractography. This UKF however was not intrinsic to the space of diffusion tensors. Lack of this key property leads to inaccuracies in the multi-tensor estimation as well as in tractography. In this paper, we propose an novel intrinsic unscented Kalman filter (IUKF) in the space of...
We propose a biquaternion formalism to model diffusion tensor magnetic resonance imaging (DT-MRI) data. Unlike previous methods that use dimensionality reduction, we are able to process the full tensor in a holistic manner while respecting the underlying manifold of the data. Using this approach, we introduce the Fourier transform and convolution for DT-MRI for the first time, which can be applied...
Human brain connectivity is disrupted in a wide range of disorders — from Alzheimer's disease to autism — but little is known about which specific genes affect it. Here we conducted a genome-wide association for connectivity matrices that capture information on the density of fiber connections between 70 brain regions. We scanned a large twin cohort (N=366) with 4-Tesla high angular resolution diffusion...
Robust estimation of diffusion models in presence of local artefacts that corrupt only a subset of gradient directions is essential in diffusion weighted imaging to accurately assess the brain connectivity and white-matter characteristics. In this work we investigate the estimation of diffusion tensors in the Random Sample Consensus (RANSAC) paradigm. First, we show that it enables robust estimation...
Neural imaging studies of autism spectrum disorders (ASD) have consistently demonstrated deficits in connectivity. In this paper, we propose a new network regularized support vector machines (SVM) method to identify the faulty subnetworks associated with ASD using diffusion tensor imaging (DTI). After constructing the bram connectivity network of each subject using DTI, the SVM-recursive feature elimination...
Intrauterine growth restriction (IUGR) due to placental insufficiency is associated with a wide range of short- and long-term neurodevelopmental disorders. Prediction of neurodevelopmental outcomes in IUGR is among the clinical challenges of modern fetal medicine and pediatrics. In this paper we analyze brain networks obtained from diffusion MRI of a prospective cohort of one year old infants (32...
Recent development in the inference of brain connectivity from neuroimaging data such as functional magnetic resonance images (fMRI) provides better understanding of brain activities and functions. The group analysis of fMRI data usually focuses on functional connectivity, while exploratory graphical modeling of effective connectivity is generally designed for the single-subject case. In this paper,...
Brain abnormalities such as white matter lesions (WMLs) are not only linked to cerebrovascular disease, but also with normal aging, diabetes and other conditions increasing the risk for cerebrovascular pathologies. Obtaining quantitative measures which assesses the degree or probability of WML in patients is important for evaluating disease burden, and for evaluating its progression and response to...
Dynamic contrast enhanced magnetic resonance (DCE-MR) imaging is an exciting tool to study the pharmacokinetics of a suspected tumor tissue. Nonetheless, the inevitable partial volume effect in DCE-MR images may seriously hinder the quantitative analysis of the kinetic parameters. In this work, based on the conventional three-tissue compartment model, we propose an unsupervised nonnegative blind source...
Manifold learning techniques have been widely used to produce low-dimensional representations of patient brain magnetic resonance (MR) images. Diagnosis classifiers trained on these coordinates attempt to separate healthy, mild cognitive impairment and Alzheimer's disease patients. The performance of such classifiers can be improved by incorporating clinical data available in most large-scale clinical...
Automatic detection of brain tumor is a difficult task due to variations in type, size, location and shape of tumors. In this paper, a multi-modality framework for automatic tumor detection is presented, fusing different Magnetic Resonance Imaging modalities including T1-weighted, T2-weighted, and T1 with gadolinium contrast agent. The intensity, shape deformation, symmetry, and texture features were...
This paper proposes a method to track abnormalities in successive frames in a capsule endoscopic image sequence. Exact tracking of an abnormality in the gastrointestinal tract is useful in preparing the content for educational systems. However, if the abnormality is de-formable over continuous frames and its features are not highly distinct, it is difficult to track abnormalities precisely. The proposed...
In this work, we present an automatic branch and stenoses detection method that is capable of detecting all types of plaques in Computed Tomography Angiography (CTA) modality. Our method is based on the vessel extraction algorithm we proposed in [1], and detects branches and stenoses in a very fast way. We demonstrate the performance of our branch detection method on 3 complex tubular structured synthetic...
Positron emission tomography (PET), with many kinds of radioactive tracers, have been used widely for molecular imaging. In order to retrieve useful information and render a diagnosis from measured PET images, the compartment models that originated from the area of pharmacokinetics have been employed extensively for data analysis. The unknown parameters in the models are usually solved by use of curve-fitting...
In previous work we developed a support vector machine (SVM) approach for detection of microcalcifications (MCs) in mammogram images, which was demonstrated to outperform several existing methods for MC detection in the literature. In this work, we explore whether we can further improve the performance of the SVM detector by exploiting the fact that MCs are inherently invariant to their spatial orientation...
White matter lesions (WML) are hyperintense signals in T2-weighted MRI of the brain. Volume and regional distribution of WML have been extensively studied in dementia, but not much attention has been given to texture analysis in these regions. We wanted to explore if it is possible to distinguish patients with dementia from healthy elderly in a classification framework testing different texture features...
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