Martin Sámal

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In functional MRI (fMRI), the changes in cerebral haemodynamics related to stimulated neural brain activity are measured using standard clinical MR equipment. Small intensity variations in fMRI data have to be detected and distinguished from non-neural effects by careful image analysis. Based on multivariate statistics we describe an algorithm involving(More)
Results of principal component analysis depend on data scaling. Recently, based on theoretical considerations, several data transformation procedures have been suggested in order to improve the performance of principal component analysis of image data with respect to the optimum separation of signal and noise. The aim of this study was to test some of those(More)
The aim of the study was a quantitative comparison of relative renal uptake and both the whole-kidney and the parenchymal transit time derived from factor analysis of image sequences and provided by standard clinical procedues. In order to extract the stable, well-interpretable factors, factor analysis was performed locally in the problem-specific time and(More)
Our aim was to estimate the volumes of homogeneous structures whose contrast / intensity was changing with time, using only few (here 4) projections of the structures. Each projection was recorded over a period of time and consisted of a sequence of images. The projections of the structures were first separated by factor analysis of the total projections.(More)
Principal component analysis is a well developed and understood method of multivariate data processing. Its optimal performance requires knowledge of noise covariance that is not available in most applications. We suggest a method for estimation of noise covariance based on assumed smoothness of the estimated dynamics.
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