Name
Abstract | ||
|---|---|---|
Functional Magnetic Resonance Imaging (fMRI) data consist of relatively weak signals with a complicated noise structure. To reduce the effects of noise arising from both instrumental and physiological sources, a series of standard preprocessing steps is performed. Nevertheless, fMRI signals will show an undesired offset due to the measurement setup. Prior to fMRI data analysis, this offset component needs to be removed in an additional preprocessing step. Classically, one assumes the data to be Gaussian distributed which eases this preprocessing step. However, this assumption is only valid for high signal-to-noise ratios (SNRs). For low SNRs, it is known that fMRI data follow a Rice distribution. Hence, to perform a proper data preprocessing, we need to take into account the correct characteristics of the Rice distributed data. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/MeMeA.2013.6549740 | MeMeA |
Keywords | Field | DocType |
signal processing,correct rice conditions,fmri signals,functional magnetic resonance imaging (fmri),noise effects,gaussian distribution,image denoising,rice distribution,offset component,noise structure,biomedical mri,rice distributed data,fmri data analysis,magnitude data,fmri data preprocessing,high signal-noise ratios,medical image processing,functional magnetic resonance imaging data,histograms,data analysis,magnetic resonance imaging,distributed databases,noise | Computer vision,Functional magnetic resonance imaging,Computer science,Data pre-processing,Rice distribution,Gaussian,Preprocessor,Artificial intelligence,Image denoising,Offset (computer science) | Conference |
ISBN | Citations | PageRank |
978-1-4673-5195-9 | 0 | 0.34 |
References | Authors | |
2 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lieve Lauwers | 1 | 43 | 5.82 |
| Kurt Barbé | 2 | 81 | 20.28 |
| Wendy Van Moer | 3 | 99 | 29.63 |