Automatic motion compensation of free breathing acquired myocardial perfusion data by using independent component analysis

Abstract

Images acquired during free breathing using first-pass gadolinium-enhanced myocardial perfusion magnetic resonance imaging (MRI) exhibit a quasiperiodic motion pattern that needs to be compensated for if a further automatic analysis of the perfusion is to be executed. In this work, we present a method to compensate this movement by combining independent component analysis (ICA) and image registration: First, we use ICA and a time-frequency analysis to identify the motion and separate it from the intensity change induced by the contrast agent. Then, synthetic reference images are created by recombining all the independent components but the one related to the motion. Therefore, the resulting image series does not exhibit motion and its images have intensities similar to those of their original counterparts. Motion compensation is then achieved by using a multi-pass image registration procedure. We tested our method on 39 image series acquired from 13 patients, covering the basal, mid and apical areas of the left heart ventricle and consisting of 58 perfusion images each. We validated our method by comparing manually tracked intensity profiles of the myocardial sections to automatically generated ones before and after registration of 13 patient data sets (39 distinct slices). We compared linear, non-linear, and combined ICA based registration approaches and previously published motion compensation schemes. Considering run-time and accuracy, a two-step ICA based motion compensation scheme that first optimizes a translation and then for non-linear transformation performed best and achieves registration of the whole series in 32±12s on a recent workstation. The proposed scheme improves the Pearsons correlation coefficient between manually and automatically obtained time-intensity curves from .84±.19 before registration to .96±.06 after registration.

Figure 1

Images from a first-pass gadolinium-enhanced myocardial perfusion MRI study. From left to right: pre-contrast, RV-peak, LV peak, and myocardial perfusion.

Figure 2

Manually tracked intensity change of a myocardial segment versus an automatically obtained time-intensity curve of a free breathingly acquired data set. Note, that not even the average of the automatically obtained time-intensity curve would result in a proper assessment of the blood flow. Also note, that the manually tracked curve is not smooth in itself - an effect that is the result of the image acquisition process.

Figure 3

Time curve representation of the mixing matrix for ICAs with three numbers of retained components and the corresponding ICs.

Figure 4

Scheme of the registration algorithm. The bold lines indicate the data flow and the thin lines represent the logical flow. The grayed texts in the gray area and dashed lines represent the additional steps for bounding box creation that are not a requirement for a non-linear motion compensation scheme. These steps may be executed to accelerate the registration and are required if linear registration is to be run.

Figure 5

Left: Mixing matrix obtained using a five component ICA. Note, that the quasiperiodic movement component is actually split into two components. Using a four component ICA results in better separation (right).

Figure 6

Left: Mixing matrix obtained using a five component ICA. Note, that the shape of the quasiperiodic movement component indicates that it is not completely separated from the perfusion component. With a six component ICA this effect is reduced significantly (right).

Figure 7

Time curve representation of the mixing matrix for ICAs with four components after normalization and sign correction (left), and corresponding RV (right, upper), and LV (right, lower) components.

Figure 8

Visualization of the absolute values (vertical axis) of the wavelet coefficients (b–f) over time (horizontal axis given in frames/heartbeats) of the mixing curves (a) of the perfusion series features obtained by an optimal five component ICA. Note, how the movement component (f) exhibits larger coefficients in the lower wavelet levels (depth axis), while the signal energy for the LV (c), RV (d) and perfusion component (e) is located in the higher wavelet levels.

Figure 9

Wavelet spectrum corresponding to the movement IC for a perfusion series that starts with breath holding. Note, that the coefficients related to motion are close to zero at the beginning, and that coefficient related to the highest wavelet level is also large.

Figure 10

Example synthetic references, note the blurriness of the reference in the first pass (left) and the improved representation of features in the third pass (right).

Figure 11

Example segmentation of a slice of the myocardium (left). The RV insertion point is indicated by a circle and based on its location and the center of the LV the myocardium is segmented clock-wise into 12 segments that enclose equal angles. An example of the time-intensity curves before and after registration as well as the corresponding manually obtained curve is given on the right.