An adaptive filter for suppression of cardiac and respiratory noise in MRI time series data

@article{citeulike:943093, abstract = {The quality of MRI time series data, which allows the study of dynamic processes, is often affected by confounding sources of signal fluctuation, including the cardiac and respiratory cycle. An adaptive filter is described, reducing these signal fluctuations as long as they are repetitive and their timing is known. The filter, applied in image domain, does not require temporal oversampling of the artifact-related fluctuations. Performance is demonstrated for suppression of cardiac and respiratory artifacts in 10-minute brain scans on 6 normal volunteers. Experimental parameters resemble a typical fMRI experiment (17 slices; 1700[no-break space]ms TR). A second dataset was acquired at a rate well above the Nyquist frequency for both cardiac and respiratory cycle (single slice; 100[no-break space]ms TR), allowing identification of artifacts specific to the cardiac and respiratory cycles, aiding assessment of filtering performance. Results show significant reduction in temporal standard deviation (SDt) in all subjects. For all 6 datasets with 1700[no-break space]ms TR combined, the filtering method resulted in an average reduction in SDt of 9.2\% in 2046 voxels substantially affected by respiratory artifacts, and 12.5\% for the 864 voxels containing substantial cardiac artifacts. The maximal SDt reduction achieved was 52.7\% for respiratory and 55.3\% for cardiac filtering. Performance was found to be at least equivalent to the previously published RETROICOR method. Furthermore, the interaction between the filter and fMRI activity detection was investigated using Monte Carlo simulations, demonstrating that filtering algorithms introduce a systematic error in the detected BOLD-related signal change if applied sequentially. It is demonstrated that this can be overcome by combining physiological artifact filtering and detection of BOLD-related signal changes simultaneously. Visual fMRI data from 6 volunteers were analyzed with and without the filter proposed here. Inclusion of the cardio-respiratory regressors in the design matrix yielded a 4.6\% t-score increase and 4.0\% increase in the number of significantly activated voxels.}, author = {Deckers, Roel H. and van Gelderen, Peter and Ries, Mario and Barret, Olivier and Duyn, Jeff H. and Ikonomidou, Vasiliki N. and Fukunaga, Masaki and Glover, Gary H. and de Zwart, Jacco A. }, citeulike-article-id = {943093}, doi = {10.1016/j.neuroimage.2006.08.006}, journal = {NeuroImage}, keywords = {fmri, noise}, month = {December}, number = {4}, pages = {1072--1081}, posted-at = {2007-04-13 13:16:48}, priority = {3}, title = {An adaptive filter for suppression of cardiac and respiratory noise in MRI time series data}, url = {http://dx.doi.org/10.1016/j.neuroimage.2006.08.006}, volume = {33}, year = {2006} }

TY - JOUR ID - citeulike:943093 L3 - citeulike-article-id:943093 TI - An adaptive filter for suppression of cardiac and respiratory noise in MRI time series data JF - NeuroImage VL - 33 IS - 4 SP - 1072 EP - 1081 N2 - The quality of MRI time series data, which allows the study of dynamic processes, is often affected by confounding sources of signal fluctuation, including the cardiac and respiratory cycle. An adaptive filter is described, reducing these signal fluctuations as long as they are repetitive and their timing is known. The filter, applied in image domain, does not require temporal oversampling of the artifact-related fluctuations. Performance is demonstrated for suppression of cardiac and respiratory artifacts in 10-minute brain scans on 6 normal volunteers. Experimental parameters resemble a typical fMRI experiment (17 slices; 1700[no-break space]ms TR). A second dataset was acquired at a rate well above the Nyquist frequency for both cardiac and respiratory cycle (single slice; 100[no-break space]ms TR), allowing identification of artifacts specific to the cardiac and respiratory cycles, aiding assessment of filtering performance. Results show significant reduction in temporal standard deviation (SDt) in all subjects. For all 6 datasets with 1700[no-break space]ms TR combined, the filtering method resulted in an average reduction in SDt of 9.2% in 2046 voxels substantially affected by respiratory artifacts, and 12.5% for the 864 voxels containing substantial cardiac artifacts. The maximal SDt reduction achieved was 52.7% for respiratory and 55.3% for cardiac filtering. Performance was found to be at least equivalent to the previously published RETROICOR method. Furthermore, the interaction between the filter and fMRI activity detection was investigated using Monte Carlo simulations, demonstrating that filtering algorithms introduce a systematic error in the detected BOLD-related signal change if applied sequentially. It is demonstrated that this can be overcome by combining physiological artifact filtering and detection of BOLD-related signal changes simultaneously. Visual fMRI data from 6 volunteers were analyzed with and without the filter proposed here. Inclusion of the cardio-respiratory regressors in the design matrix yielded a 4.6% t-score increase and 4.0% increase in the number of significantly activated voxels. KW - fmri KW - noise AU - Deckers, Roel AU - van Gelderen, Peter AU - Ries, Mario AU - Barret, Olivier AU - Duyn, Jeff AU - Ikonomidou, Vasiliki AU - Fukunaga, Masaki AU - Glover, Gary AU - de Zwart, Jacco PY - 2006/12// UR - http://dx.doi.org/10.1016/j.neuroimage.2006.08.006 ER -