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A novel and computationally efficient method for exploratory analysis of functional MRI data is presented. The basic idea is to reveal underlying components in the fMRI data that have maximum autocorrelation. The tool for accomplishing this task is Canonical Correlation Analysis. The relation to Principal Component Analysis and Independent Component(More)
A novel method for detecting neural activity in functional magnetic resonance imaging (fMRI) data is introduced. It is based on canonical correlation analysis (CCA), which is a multivariate extension of the univariate correlation analysis widely used in fMRI. To detect homogeneous regions of activity, the method combines a subspace modeling of the(More)
This article addresses the impact that colored noise, temporal filtering, and temporal detrending have on the fMRI analysis situation. Specifically, it is shown why the detection of event-related designs benefit more from pre-whitening than blocked designs in a colored noise structure. Both theoretical and empirical results are provided. Furthermore, a(More)
PURPOSE To assess the ability of a conventional density mask method to detect mild emphysema by high-resolution computed tomography (HRCT); to analyze factors influencing quantification of mild emphysema; and to validate a new algorithm for detection of mild emphysema. MATERIAL AND METHODS Fifty-five healthy male smokers and 34 never-smokers, 61-62 years(More)
The interest for surgery simulator systems with anatomical models generated from authentic patient data is growing as these systems evolve. With access to volumetric patient data, e.g., from a computer tomography scan, haptic and visual feedback can be created directly from this dataset. This opens the door for patient specific simulations. Hip fracture(More)