Exploratory Identification of Cardiac Noise in fMRI Images

A fast exploratory framework for extracting cardiac noise signals contained in rest-case fMRI images is presented. Highly autocorrelated, independent components of the input time series are extracted by applying Canonical Correlation Analysis in the time domain. A close correspondence between some of these components and cardiac noise contributions is established. Our analysis is carried out without using any external monitoring of the subject or any modification applied to the standard image acquisition protocol. Using the results as a priori information about the presence of corrupting cardiac noise, several approaches are suggested that could improve the performance of activation detection algorithms on non-rest-case datasets.