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A population growth model that represents the growth trajectories of individual subjects is critical to study and understand neurodevelopment. This paper presents a framework for jointly estimating and modeling individual and population growth trajectories, and determining significant regional differences in growth pattern characteristics applied to longitudinal neuroimaging data. We use non-linear...
Vascular diseases are among the most important health problems. Vessel segmentation is a very critical task for stenosis measurement and simulation, diagnosis and treatment planning. However, vessel segmentation is much more challenging than blob-like object segmentation due to the thin elongated anatomy of the blood vessels, which can easily appear disconnected in the acquired images due to noise...
We present a new framework for spatiotemporal analysis of parameterized functions attributed by properties of 4D longitudinal image data. Our driving application is the measurement of temporal change in white matter diffusivity of fiber tracts. A smooth temporal modeling of change from a discrete-time set of functions is obtained with an extension of the logistic growth model to time-dependent spline...
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,...
We propose a new feature that can be used to automatically detect cerebral aneurysms in angiographic images. It combines both low-level and high-level features to a feature indicating aneurysms. The feature is used in a system for aneurysm detection in two types of magnetic resonance angiography (MRA) images and computed tomography angiography (CTA) images. The method was tested on 66 angiographic...
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)...
Even if a number of methods have been proposed for resolving crossing fibres from diffusion-weighted (DW) MRI, other complex fibre geometries have drawn minimal attention. In this study, we focus on fibre orientation dispersion induced by within-voxel fanning. We use a multi-compartment, model-based approach to estimate fibre dispersion. A Bingham distribution is employed to represent a continuum...
In this study, we propose a methodology to estimate 3D+time maps of left ventricular fibre strain from human structural and dynamic MRI data. A finite element model integrates fibre principal direction throughout the left ventricle from an ex vivo human diffusion tensor MRI acquisition and motion from tagged MRI. This combination enables the estimation of fibre strain and its variation throughout...
Clinical translation of computational models of the heart has been hampered by the absence of complete and rigorous technical and clinical validation, as well as benchmarking of the developed tools. To address this issue, a dataset containing the cardiac anatomy and fibre orientations from magnetic resonance images (MRI), as well as epicardial transmembrane potentials from optical mapping acquired...
Cardiac therapies aim to correct pathological blood flow. Patient-specific therapy planning is challenging due to the large variability in disease cause, location and severity. A predictive framework is therefore needed to assess the optimal treatment for a patient in terms of maximizing effectiveness (blood flow velocity, vorticity, cardiac output, etc.) and minimizing the risk of complications....
The work presented in this paper focuses on fitting of a neural mass model to EEG data. Neurophysiology inspired mathematical models were developed for simulating brain's electrical activity imaged through Electroencephalography (EEG) more than three decades ago. At the present well informative models which even describe the functional integration of cortical regions also exists. However, a very limited...
This paper proposes a registration method for supine and prone CTC scans. The method matches graphs built using the teniae coli, three muscles that run the length of the colon. The teniae are visible on CTC and were detected using fully-automatically software. Then key points of the teniae were obtained by non-uniformed sampling of the teniae. Graphs were built using these key points. The colon registration...
We have developed techniques to automatically generate personalised biomechanical models of patients' hearts based on 3D cardiac images. We demonstrate this approach using multi-slice computed tomography images. Unsupervised segmentation was performed using non-rigid image registration with a segmented image. A finite element model was automatically fitted to the segmented data of the left ventricle...
Computational fluid dynamics (CFD) simulations of blood flow within intracranial aneurysms may provide important hemodynamic information for treatment planning. However, reliable validation is required prior to applications in clinical environments. For that purpose, we introduce a workflow for generating virtual digital subtraction angiographies (DSA) based on CFD results and real patient-individual...
Image segmentation of very large and complex microscopy images are challenging due to variability in the images and the need for algorithms to be robust, fast and able to incorporate various types of information and constraints in the segmentation model. In this paper we propose a graphical model based image segmentation framework that combines the information in images regions with the information...
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...
Experimental rodent models of induced ischemic injury have been extensively used in biomedical research to study molecular, cellular and histological alterations following myocar-dial infarction. These models are increasingly employed to assess the potential of newly developed therapies for functional restoration of the damaged heart. Such studies are based on myocardial infarction induction followed...
Sparse sampling of (k, t)-space has proved useful for cardiac MRI. This paper builds on previous work on using partial separability (PS) and spatial-spectral sparsity for high-quality image reconstruction from highly undersampled (k, t)-space data. This new method uses a more flexible control over the PS-induced low-rank constraint via group-sparse regularization. A novel algorithm is also described...
Standard analysis techniques for Functional Magnetic Resonance Imaging (fMRI) assume a linear, time invariant model of underlying signal behaviour. These assumptions are valid for some but not all data. Hence each model characteristic should be formally tested for its validity when analysing particular data. Diagnosing model violations is a necessary step in statistical modeling but is not yet common...
Standard Bayesian analysis of event-related functional Magnetic Resonance Imaging (fMRI) data usually assumes that all delivered stimuli possibly generate a BOLD response everywhere in the brain although activation is likely to be induced by only some of them in specific brain areas. Criteria are not always available to select the relevant conditions or stimulus types (e.g. visual, auditory, etc.)...
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