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Abstract #4791

Data driven whole-brain cardiac signal regression from highly accelerated fMRI acquisitions

Marco Marino1,2, Nigel Colenbier1,2,3, Nicola Filippini2, Giovanni Pellegrino2, Daniele Marinazzo2,3, and Giulio Ferrazzi2
1Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, Belgium, 2IRCCS San Camillo Hospital, Venice, Italy, 3Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent, Belgium


Cardiac pulsation is a physiological confound of fMRI analysis pipelines and so accurate mapping of cardiac contributions to the BOLD signal is highly warranted. To overcome cardiac aliasing associated with the limited temporal resolution in fMRI, we developed a data-driven methodology to spatially and temporally resolve cardiac contributions from the BOLD signal itself (i.e without the need of processing external physiological recordings such as PPU/ECG signals). This is achieved by combining simultaneous multi-slice imaging and a dedicated hyper-sampling decomposition scheme.

The proposed methodology is fully data driven and it does not make specific assumptions on the shape of cardiac pulsation.

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