Robust Non-Rigid Motion Compensation of Free-Breathing Myocardial Perfusion MRI Data

Abstract

Kinetic parameter values, such as myocardial perfusion, can be quantified from dynamic contrast-enhanced magnetic resonance imaging data using tracer-kinetic modeling. However, respiratory motion affects the accuracy of this process. Motion compensation of the image series is difficult due to the rapid local signal enhancement caused by the passing of the gadolinium-based contrast agent. This contrast enhancement invalidates the assumptions of the (global) cost functions traditionally used in intensity-based registrations. The algorithms are unable to distinguish whether the differences in signal intensity between frames are caused by the spatial motion artifacts or the local contrast enhancement. In order to address this problem, a fully automated motion compensation scheme is proposed, which consists of two stages. The first of which uses robust principal component analysis (PCA) to separate the local signal enhancement from the baseline signal, before a refinement stage which uses the traditional PCA to construct a synthetic reference series that is free from motion but preserves the signal enhancement. Validation is performed on 18 subjects acquired in free-breathing and 5 clinical subjects acquired with a breath-hold. The validation assesses the visual quality, the temporal smoothness of tissue curves, and the clinically relevant quantitative perfusion values. The expert observers score the visual quality increased by a mean of 1.58/5 after motion compensation and improvement over the previously published methods. The proposed motion compensation scheme also leads to the improved quantitative performance of motion compensated free-breathing image series [30% reduction in the coefficient of variation across quantitative perfusion maps and 53% reduction in temporal variations (p < 0.001)].

Fig. 1

Two pairs of successive frames from a myocardial perfusion MRI image series. The first pair ((a) and (b)) are during the arrival of contrast agent in the right ventricle and the second pair ((c) and (d)) are during the arrival of contrast agent in the left ventricle. This serves to show that the contrast profile is not necessary similar between two successive frames.

Fig. 2

A flow chart of the proposed motion compensation scheme.

Fig. 3

The RPCA based separation of the example images from the original image series (M) into its low-rank (L) and sparse components (S). As discussed, the local signal enhancement is represented in S with no dynamically changing contrast present in L.

Fig. 4

An example image from the image series which can be expressed as a linear combination of its 3 principal eigen-images.

Fig. 5

The motion profile of the synthetic reference. This is constructed by taking the centre column (a) and row (b) from each image in the series and stacking them left to right (a) and top to bottom (b). (a) shows the vertical motion (anterior to inferior) and (b) shows the horizontal motion (septal to lateral). This figure indicates a complete absence of motion.

Fig. 6

The motion profile of a free-breathing image series that was created for the same image series as shown in Fig. 5. The motion is represented as the oscillating pattern and is quite severe in this case. As expected, there is strong vertical motion. There is less horizontal motion but it is still present.

Fig. 7

Voxel-wise time-intensity curves which were extracted from the myocardial segmentation, before and after motion compensation. On the left the motion causes the segmentation of the myocardium to be contaminated by the left ventricle during the upslope of myocardial signal. After motion compensation (right) this effect is corrected and the curves look as expected.

Fig. 8

The values for the mean standard deviation of the 2^nd derivative of myocardial time-intensity curves. This indicates the temporal smoothness of the image series. The smoother the transition between successive images in the series the less motion that is present.

Fig. 9

The values for the standard deviation of perfusion values in each map. Lower standard deviations indicate more homogenous perfusion maps and hence less motion.

Fig. 10

The temporal maximum intensity projection of the three slices from a free-breathing stress acquisition. The increase in sharpness in the RPCA corrected series indicates a lack of motion. The blurring artefacts as a result of motion are shown with yellow arrows.

Fig. 11

The equivalent motion profile for the same image series as shown in Fig. 6 after motion compensation. The smooth transition between frames indicates the near-total eradication of motion.

Fig. 12

The equivalent motion profile as shown in Fig. 6 for a breath-hold acquisition. In this image series there is a period of free-breathing followed by a breath-hold during the passage of the main bolus and then another period of free-breathing. The breath-hold is short relative to the passage of the contrast agent, this will impact the tissue curves from the myocardium and subsequently the quantitative perfusion values.