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Neuroimage. 2006 Dec;33(4):1072-81. Epub 2006 Sep 29.

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

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Advanced MRI section, LFMI, NINDS, National Institutes of Health, Bldg. 10, Rm. B1D-728, MSC 1065, 9000 Rockville Pike, Bethesda, MD 20892-1065, USA.


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 ms TR). A second dataset was acquired at a rate well above the Nyquist frequency for both cardiac and respiratory cycle (single slice; 100 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 (SD(t)) in all subjects. For all 6 datasets with 1700 ms TR combined, the filtering method resulted in an average reduction in SD(t) of 9.2% in 2046 voxels substantially affected by respiratory artifacts, and 12.5% for the 864 voxels containing substantial cardiac artifacts. The maximal SD(t) 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.

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