Adaptive multilevel thresholding for SVD-based clutter filtering in ultrafast transthoracic coronary flow imaging

Abstract

Background and Objective:

The integration of ultrafast Doppler imaging with singular value decomposition clutter filtering has demonstrated notable enhancements in flow measurement and Doppler sensitivity, surpassing conventional Doppler techniques. However, in the context of transthoracic coronary flow imaging, additional challenges arise due to factors such as the utilization of unfocused diverging waves, constraints in spatial and temporal resolution for achieving deep penetration, and rapid tissue motion. These challenges pose difficulties for ultrafast Doppler imaging and singular value decomposition in determining optimal tissue-blood (TB) and blood-noise (BN) thresholds, thereby limiting their ability to deliver high-contrast Doppler images.

Methods:

This study introduces a novel local blood subspace detection method that utilizes multilevel thresholding by the valley-emphasized Otsu’s method to estimate the TB and BN thresholds on a pixel-based level, operating under the assumption that the magnitude of the spatial singular vector curve of each pixel resembles the shape of a trimodal Gaussian. Upon obtaining the local TB and BN thresholds, a weighted mask (WM) is generated to assess the blood content in each pixel. To enhance the computational efficiency of this pixel-based algorithm, a dedicated tree-structure $k$-means clustering approach, further enhanced by noise rejection (NR) at each singular vector order, is proposed to group pixels with similar spatial singular vector curves, subsequently applying local thresholding (LT) on a cluster-based (CB) level.

Results:

The effectiveness of the proposed method was evaluated using an ex-vivo setup featuring a Langendorff swine heart. Comparative analysis with power Doppler images filtered using the conventional global thresholding method, which uniformly applies TB and BN thresholds to all pixels, revealed noteworthy enhancements. Specifically, our proposed CBLT+NR+WM approach demonstrated an average 10.8-dB and 11.2-dB increase in Contrast-to-Noise ratio and Contrast in suppressing the tissue signal, paralleled by an average 5-dB (Contrast-to-Noise ratio) and 9-dB (Contrast) increase in suppressing the noise signal.

Conclusions:

These results clearly indicate the capability of our method to attenuate residual tissue and noise signals compared to the global thresholding method, suggesting its promising utility in challenging transthoracic settings for coronary flow measurement.