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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...
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...
Single-particle tracking is computationally a challenging problem, and usually solved with local methods. Local methods suffer from defects in the image data or in the detection of particles, such as temporal disappearing of particles. A particle tracking method has to provide a solution also to real disappearing and appearing of particles as a result of merging and splitting. Here, we present an...
The thoracic aorta is an anatomical structure that is subject to constant motion. Stent-grafting changes the deformation patterns on the vessel surface, and the nature of these changes is suspected to correlate with treatment outcome. However, they are currently only poorly understood. We propose a method for quantifying the changes in vessel motion caused by stent-grafting in the aortic arch. For...
Quantitative analysis of early brain development through imaging is critical for identifying pathological development, which may in turn affect treatment procedures. We propose a framework for analyzing spatiotemporal patterns of brain maturation by quantifying intensity changes in longitudinal MR images. We use a measure of divergence between a pair of intensity distributions to study the changes...
The understanding of the embryogenesis in living systems requires reliable quantitative analysis of the cell migration throughout all the stages of development. This is a major challenge of the ”in-toto” reconstruction based on different modalities of ”in-vivo” imaging techniques-spatio-temporal resolution and image artifacts and noise. Several methods for cell tracking are available, but expensive...
Survival and function of neurons depend critically on bidirectional transport of essential cargoes within their axons. To study related molecular mechanisms, we developed an automated single particle tracking method that combines nanometer resolution detection of cargo positions with kymograph-based multiple hypothesis tracking of cargo motion for precise and complete recovery of cargo trajectories...
Hyperpolarized 13C chemical shift imaging (CSI) is a spectroscopy technique for magnetic resonance imaging. Due to the fast decay of the hyperpolarized tracer, acquisition speed represents a key issue. Spiral trajectories are usually exploited to fast fill the K-space. Several strategies have been proposed for CSI image reconstruction form spiral trajectories, but the performances in hyperpolarized...
The dynamical structure of electrical recordings from the heart or torso surface is a valuable source of information about cardiac physiological behavior. In this paper, we use an existing data-driven technique for manifold identification to reveal electrophysiologically significant changes in the underlying dynamical structure of these signals. Our results suggest that this analysis tool characterizes...
Ordered movement of virus particles along cell filopodia before cell entry known as viral surfing is an important pathway for cell infection. To quantitatively analyze the process of viral surfing, tracking of viruses over time in fluorescent time-lapse microscopy images is required. We have developed an automatic approach for tracking single surfing virus particles. The approach combines probabilistic...
The dynamic behavior of sub-cellular structures is of great interest to bio-medical research. We propose to use probabilistic multi-target tracking to analyze the dynamics of stress granules (SGs) to alleviate the effort of manual analysis. Inherent to multi-target tracking approaches is the combinatorial problem to associate observations to underlying targets. Rao-Blackwellized Monte Carlo Data Association...
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