26,131 research outputs found
Advanced signal processing methods in dynamic contrast enhanced magnetic resonance imaging
Tato dizertační práce představuje metodu zobrazování perfúze magnetickou rezonancí, jež je výkonným nástrojem v diagnostice, především v onkologii. Po ukončení sběru časové sekvence T1-váhovaných obrazů zaznamenávajících distribuci kontrastní látky v těle začíná fáze zpracování dat, která je předmětem této dizertace. Je zde představen teoretický základ fyziologických modelů a modelů akvizice pomocí magnetické rezonance a celý řetězec potřebný k vytvoření obrazů odhadu parametrů perfúze a mikrocirkulace v tkáni. Tato dizertační práce je souborem uveřejněných prací autora přispívajícím k rozvoji metodologie perfúzního zobrazování a zmíněného potřebného teoretického rozboru.This dissertation describes quantitative dynamic contrast enhanced magnetic resonance imaging (DCE-MRI), which is a powerful tool in diagnostics, mainly in oncology. After a time series of T1-weighted images recording contrast-agent distribution in the body has been acquired, data processing phase follows. It is presented step by step in this dissertation. The theoretical background in physiological and MRI-acquisition modeling is described together with the estimation process leading to parametric maps describing perfusion and microcirculation properties of the investigated tissue on a voxel-by-voxel basis. The dissertation is divided into this theoretical analysis and a set of publications representing particular contributions of the author to DCE-MRI.
Statistical properties of a filtered Poisson process with additive random noise: Distributions, correlations and moment estimation
Filtered Poisson processes are often used as reference models for
intermittent fluc- tuations in physical systems. Such a process is here
extended by adding a noise term, either as a purely additive term to the
process or as a dynamical term in a stochastic differential equation. The
lowest order moments, probability density function, auto-correlation function
and power spectral density are derived and used to identify and compare the
effects of the two different noise terms. Monte-Carlo studies of synthetic time
series are used to investigate the accuracy of model pa- rameter estimation and
to identify methods for distinguishing the noise types. It is shown that the
probability density function and the three lowest order moments provide
accurate estimations of the parameters, but are unable to separate the noise
types. The auto-correlation function and the power spectral density also
provide methods for estimating the model parameters, as well as being capable
of identifying the noise type. The number of times the signal crosses a
prescribed threshold level in the positive direction also promises to be able
to differentiate the noise type.Comment: 34 pages, 25 figure
Radio astronomical polarimetry and phase-coherent matrix convolution
A new phase-coherent technique for the calibration of polarimetric data is
presented. Similar to the one-dimensional form of convolution, data are
multiplied by the response function in the frequency domain. Therefore, the
system response may be corrected with arbitrarily high spectral resolution,
effectively treating the problem of bandwidth depolarization. As well, the
original temporal resolution of the data is retained. The method is therefore
particularly useful in the study of radio pulsars, where high time resolution
and polarization purity are essential requirements of high-precision timing. As
a demonstration of the technique, it is applied to full-polarization baseband
recordings of the nearby millisecond pulsar, PSR J0437-4715.Comment: 8 pages, 4 figures, accepted for publication in Ap
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