Background: Speckle noise is an inherent characteristic of dynamic contrast-enhanced ultrasound (DCEUS) movies and ultrasound images in general. Speckle noise considerably reduces the quality of these images and limits their clinical use. Currently, temporal compounding and maximum intensity persistence (MIP) are among the most widely accepted processing methods enabling the visualization of vasculature using DCEUS.
Goal: A different approach has been used in this study, in order to improve the noise removal, while enabling the investigation of CEUS dynamics.
Methods: A multiplicative model for the formation of DCEUS speckled images is adopted and the log-transformed cines are processed. A preprocessing step was performed, locally removing low value outliers. Due to the fast-changing spatial distribution of microbubbles inside the vasculature, the noise in consecutive DCEUS frames is independent, facilitating its removal by temporal denoising. Noise reduction is efficiently achieved by wavelet denoising, in which the signal's wavelet coefficients are thresholded and small-value noise-related coefficients are discarded. The main advantage of using wavelet denoising in the present context is its ability to estimate ultrasound contrast agents' (UCA) concentration over time adaptively, without assuming a model or predefining the signal's degree of smoothness. The performance of wavelet denoising was compared against MIP, temporal compounding, and Log-normal model fitting.
Results: Phantom experiments showed improved SNR, using wavelet denoising over a wide range of UCA concentrations (MicroMarker, 0.001-1%). In the in vivo tests, improved noise removal was achieved, reflected by a significantly lower coefficient of variation in homogeneous vascular regions (p < 0.01).