David Bancelin1, Beata Bachrata1,2, Pedro Lima Cardoso1, Siegfried Trattnig1,2, and Simon Daniel Robinson1,3,4
1High-Field MR Centre, Medical University of Vienna, Vienna, Austria, 2Christian Doppler Laboratory for Clinical Molecular MR Imaging, Vienna, Austria, 3Department of Neurology, Medical University of Graz, Graz, Austria, 4Centre for Advanced Imaging, University of Queensland, Queensland, Australia
1High-Field MR Centre, Medical University of Vienna, Vienna, Austria, 2Christian Doppler Laboratory for Clinical Molecular MR Imaging, Vienna, Austria, 3Department of Neurology, Medical University of Graz, Graz, Austria, 4Centre for Advanced Imaging, University of Queensland, Queensland, Australia
We have developed an unsupervised tool to extract cardiac and respiratory waveforms from magnitude and phase fMRI data. The level of improvement is close to that using tertiary physiological measurements and outperforms a rival method.
Figure 3: Spectrograms of uncorrected versus corrected magnitude data. The location of the cardiac and respiratory frequency bands are indicated in the uncorrected magnitude image (left) with red and black horizontal arrows, respectively. While some residual cardiac and respiration power remained after the RETROICOR(PMU) and PESTICA correction (red and black vertical arrows in the central two figures, respectively), PREPAIR showed effective removal of these frequencies.
Figure 4: Average variance improvement over subjects (in percentage) for protocols with different TRs after correction with both respiratory and cardiac regression. PREPAIR (red) achieved similar image variance reduction to RETROICOR(PMU) (green) and outperformed in PESTICA (blue) for all protocols.
