Adaptive Slow-Time Singular Value Thresholding (Svt) Based On Stein'S Unbiased Risk Estimate (Sure) For Ultrasound Image Random Noise Reduction

Ultrasound (US) image quality often suffers from random and speckle noise. Especially in the case of difficult patients, increased presence of random noise leads to compromised signal to noise ratio (SNR). The proposed technique removes random or speckle noise by promoting the low rank component of temporal US image sequences comprising of RF-data or envelope-detected data, respectively. This approach leverages the fact that due to tissue connectedness, tissue motion tends to exhibit limited temporal profiles. An approach to approximate the low rank component of the image sequence involves truncating its singular value decomposition. In the proposed algorithm, this is performed in a block-wise manner. Additionally, an adaptive singular value threshold is proposed based on Stein's unbiased risk estimator. This paper presents applications of this methodology on sequences of RF-data and envelope-detected data.