Transfer function analysis of respiratory and cardiac pulsations in human brain observed on dynamic magnetic resonance images

Abstract

Magnetic resonance (MR) imaging provides a noninvasive, in vivo imaging technique for studying respiratory and cardiac pulsations in human brains, because these pulsations can be recorded as flow-related enhancement on dynamic MR images. By applying independent component analysis to dynamic MR images, respiratory and cardiac pulsations were observed. Using the signal-time curves of these pulsations as reference functions, the magnitude and phase of the transfer function were calculated on a pixel-by-pixel basis. The calculated magnitude and phase represented the amplitude change and temporal delay at each pixel as compared with the reference functions. In the transfer function analysis, near constant phases were found at the respiratory and cardiac frequency bands, indicating the existence of phase delay relative to the reference functions. In analyzing the dynamic MR images using the transfer function analysis, we found the following: (1) a good delineation of temporal delay of these pulsations can be achieved; (2) respiratory pulsation exists in the ventricular and cortical cerebrospinal fluid; (3) cardiac pulsation exists in the ventricular cerebrospinal fluid and intracranial vessels; and (4) a 180-degree phase delay or inverted amplitude is observed on phase images.

Figure 1

Analysis of a dataset acquired from a slice through the third ventricle. The 180th to the 479th dynamic images had stable respiratory and cardiac pulsations (pointed by arrows) as shown in the periodogram (a). The first image of the 512 dynamic images is used to display anatomy (b). Temporal-standard-deviation image illustrates the flow-related enhancement caused by oscillatory flows (c).

Figure 2

FastICA segmentation results for the dynamic images are displayed in Figure 1, which include the following: four output images with their frames displayed in different colors (a–d), corresponding signal-time curves (e), and corresponding amplitude spectrum (f). The phase, ϕ(f), calculated by using transfer function analysis and the complex-valued spectrum of the green signal-time curve as a reference function are illustrated in (g). The respiratory and cardiac frequency bands are marked by yellow shaded areas. Note that constant phases are observable at the respiratory (black, red, and blue curves) and cardiac pulsations (black and red curves).

Figure 3

The amplitude (first column) and phase (second column) images are calculated by using the transfer function analysis for the respiratory (first row) and cardiac (second row) frequency bands. A pixel with a negative value (displayed in red color) has a signal-time curve leading the reference function. A pixel with a positive value (displayed in green color) indicates its signal-time curve is lagging the reference signal. The white color implies no or very small delays, and yellow color implies either a leading or a delay of T/2.

Figure 4

FastICA segmentation results for a slice location above the third ventricle, including four output images (a–d), and corresponding signal-time curves (e).

Figure 5

Transfer function analysis of the dynamic images shown in Figure 4. The amplitude (first column) and phase (second column) images were calculated by using the transfer function analysis for the respiratory (first row) and cardiac (second row) frequency bands. The color coding is the same as that in Figure 3.