Analysis and quantification of arterial wall motion from B-mode ultrasound images - comparison of block-matching and optical flow

An analysis of contrast agent flow patterns from sequential ultrasound images using a motion estimation algorithm based on optical flow patterns

This study estimates flow patterns of contrast agents from successive ultrasound image sequences by using an anisotropic diffusion-based optical flow algorithm. Before flow fields were recovered, the test sequences were reconstructed using relative composition of structural and textural parts from the original image. To improve estimation performance, an anisotropic diffusion filtering model was embedded into a spline-based slightly nonconvex total variation-L1 minimization algorithm. In addition, an incremental coarse-to-fine warping framework was employed with a linear minimization scheme to account for a large displacement. After each warping iteration, the implementation used intermediate bilateral filtering to prevent oversmoothing across motion boundaries. The performance of the proposed algorithm was tested using three different sequences obtained from two simulated datasets and phantom ultrasound sequences. The results indicate the robust performance of the proposed method under different noise environments. The results of the phantom study also demonstrate reliable performance according to different injection conditions of contrast agents. These experimental results suggest the potential clinical applicability of the proposed algorithm to ultrasonographic diagnosis based on contrast agents.

Bayesian regularization applied to ultrasound strain imaging

Noise artifacts due to signal decorrelation and reverberation are a considerable problem in ultrasound strain imaging. For block-matching methods, information from neighboring matching blocks has been utilized to regularize the estimated displacements. We apply a recursive Bayesian regularization algorithm developed by Hayton et al. [Artif. Intell., vol. 114, pp. 125-156, 1999] to phase-sensitive ultrasound RF signals to improve displacement estimation. The parameter of regularization is reformulated, and its meaning examined in the context of strain imaging. Tissue-mimicking experimental phantoms and RF data incorporating finite-element models for the tissue deformation and frequency-domain ultrasound simulations are used to compute the optimal parameter with respect to nominal strain and algorithmic iterations. The optimal strain regularization parameter was found to be twice the nominal strain and did not vary significantly with algorithmic iterations. The technique demonstrates superior performance over median filtering in noise reduction at strains 5% and higher for all quantitative experiments performed. For example, the strain SNR was 11 dB higher than that obtained using a median filter at 7% strain. It has to be noted that for applied deformations lower than 1%, since signal decorrelation errors are minimal, using this approach may degrade the displacement image.

Affine optical flow combined with multiscale image analysis for motion estimation of the arterial wall from B-mode ultrasound

This paper investigated the performance of affine optical flow (AFOF) in motion tracking of the arterial wall from B-mode ultrasound images and the effect of its combination with multiscale image analysis on the accuracy of the process. Multiscale AFOF (MAFOF) exploits the information obtained with AFOF from the approximation sub-images at different spatial resolution levels of the images, obtained using a 2D discrete wavelet transform. Both AFOF and MAFOF were evaluated through their application to synthetic image sequences of the common carotid artery. Multiscale image analysis increased the accuracy in motion tracking, with MAFOF yielding average displacement error reductions of 9% with respect to AFOF. The methods were also effectively applied to real ultrasound image sequences of the carotid artery. The results showed that MAFOF could be considered as a reliable estimator for arterial wall motion from B-mode ultrasound images.