Preprocessing of fMR Datasets

Authors:
F. Kruggel;X. Descombes;D. Y. von Cramon
Affiliations:
-;-;-
Venue:
WBIA '98 Proceedings of the IEEE Workshop on Biomedical Image Analysis
Year:
1998

Citing 0
Cited 3

A Markov Pixon Information Approach for Low-Level Image Description

IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov Random Field Modelling of fMRI Data Using a Mean Field EM-algorithm

EMMCVPR '99 Proceedings of the Second International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Physiologically Oriented Models of the Hemodynamic Response in Functional MRI

IPMI '99 Proceedings of the 16th International Conference on Information Processing in Medical Imaging

Quantified Score

Hi-index	0.00

Visualization

Abstract

When studying complex cognitive tasks using functional magnetic resonance (fMR) imaging one often encounters weak signal responses. These weak responses are corrupted by noise and artifacts of various sources. Preprocessing of the raw data before the application of test statistics helps to extract the signal and thus can vastly improve signal detection. We discuss artifact sources and algorithms to handle them. Experiments with simulated and real data underline the usefulness of this preprocessing sequence.