@InProceedings{Supelec577,
author = {Julien Oster and Olivier Pietquin and Michel Kraemer and Jacques Felblinger},
title = {{Bayesian Framework for Artifact Reduction on ECG in MRI}},
year = {2010},
booktitle = {{Proceedings of the 35th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2010)}},
pages = {489-492},
month = {March},
address = {Dallas (TX, USA)},
url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp\'earnumber=5495684},
doi = {10.1109/ICASSP.2010.5495684},
abstract = {Electrocardiogram (ECG) is required during Magnetic Resonance
Imaging (MRI) for two reasons, patient monitoring and MRI
sequence synchronization for cardiovascular imaging. The MRI
environment severely distorts ECG signals. The Magnetic Field
Gradients (MFG) especially induce artifacts, which make ECG
analysis during MRI acquisition challenging. Specific signal
processing is thus required. An MFG artifact modeling has been
proposed for their suppression. However the resulting techniques
do not take the ECG signals into account during the model
parameter estimation.
Recently, ECG denoising based on an artificial ECG model and
nonlinear Bayesian filtering has been presented. In this paper,
a
new MFG artifact suppression method based on nonlinear Bayesian
filtering and the unification of the ECG and MFG models is
proposed.
This new approach enables accurate patient monitoring and
outperforms state-of-the-art methods in terms of both QRS
detection quality and signal to noise ratio.}
}