Automatic motion estimation using flow parameters for dynamic contrast-enhanced ultrasound

Dynamic contrast-enhanced ultrasound (DCE-US) sequences are subject to motion which can disturb functional flow quantification. This can make estimated parameters more variable or unreliable. Methods that compensate for motion are therefore desirable. The most commonly used motion correction techniques in DCE-US register the images in the sequence with respect to a user-selected reference image. However, this image may not include all features that are representative of the whole sequence. Moreover, image-based registration neglects pertinent, functional-flow information contained in the DCE-US sequence. An operator-free method is proposed that combines the motion estimation and flow-parameter quantification (M/Q method) in a single mathematical framework. This method is based on a realistic multiplicative model of the DCE-US noise. By computing likelihood in this model, motion and flow parameters are both estimated iteratively. First, the maximization is accomplished by estimating functional and motion parameters. Then, a final registration based on a non-parametric temporal smoothing of the sequence is performed. This method is compared to a conventional (mutual information) registration method where all the images of the sequence are registered with respect to a reference image chosen by an expert. The two methods are evaluated on simulated sequences and DCE-US sequences acquired in patients (N = 15). The M/Q method demonstrates significantly (p < 0.05) lower Dice coefficients and Hausdorff distance than the conventional method on the simulated data sets. On the in vivo sequences analysed, the M/Q methods outperformed the conventional method in terms of mean Dice and Hausdorff distance on 80% of the sequences, and in terms of standard deviation of Dice and Hausdorff distance on 87% of the sequences.