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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...
Factor Analysis (FA) is a well established method for factors separation in analysis of dynamic medical imaging. However, its assumptions are valid only in limited regions of interest (ROI) in the images which must be selected manually or using heuristics. The resulting quality of separation is sensitive to the choice of these ROI. We propose a new probabilistic model for functional analysis with...
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,...
Three different DCE-MRI quantification methods: model-free-based, compartment-model-based and principal component analysis, are compared by evaluating parameter maps for histological defined volumes of vital and non-vital tumor tissue. To obtain an accurate spatial correspondence between histology and DCE-MRI, a two-step registration process was used involving dense histological sampling, a reference...
Atrial fibrillation (AF), a very common cardiac arrhythmia, has recently emerged as a new interdisciplinary epicenter of clinical, engineering, and basic science research. This emergence is fueled by new results from experiments, animal models of the disease, and multimodal measurements in patients, catalyzed by rapid developments in interventional approaches to treatment. Our understanding of the...
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....
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...
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...
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