Figure 4.

The pipeline for automatic respiratory motion detection. (a) Z-projection profiles of the entire imaging volume are computed by performing a 1D partition-direction FFT on the series of central k-space points (k_x=k_y=0). For each coil-element, principal component analysis (PCA) is performed to determine the most common signal variation mode in the late contrast phase. For coil elements that have a good representation of respiratory pattern, their first principal components (PC) are expected to represent respiratory motion. Thus, the clustering algorithm shown in Figure 3 can be used to identify those “good” coil elements and to extract a respiratory motion signal for the late contrast-enhancement phase. (b) The selected “good” coils are concatenated together and PCA is performed to determine the most common signal variation mode for all contrast phases. The PC that has the highest correlation with the late contrast phase respiratory motion signal extracted in the previous step is selected as the final respiratory motion signal.