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Minimum variance (MV) beamforming has been studied for improving the performance of a diagnostic ultrasound imaging system. However, it is not easy for the MV beamforming to be implemented in a real-time ultrasound imaging system because of the enormous amount of computation time associated with the covariance matrix inversion. In this paper, to address this problem, we propose a new fast MV beamforming method that almost optimally approximates the MV beamforming while reducing the computational complexity greatly through dimensionality reduction using principal component analysis (PCA). The principal components are estimated offline from pre-calculated conventional MV weights. Thus, the proposed method does not directly calculate the MV weights but approximates them by a linear combination of a few selected dominant principal components. The combinational weights are calculated in almost the same way as in MV beamforming, but in the transformed domain of beamformer input signal by the PCA, where the dimension of the transformed covariance matrix is identical to the number of some selected principal component vectors. Both computer simulation and experiment were carried out to verify the effectiveness of the proposed method with echo signals from simulation as well as phantom and in vivo experiments. It is confirmed that our method can reduce the dimension of the covariance matrix down to as low as 2 × 2 while maintaining the good image quality of MV beamforming.

A method is introduced to monitor cardiac ablative therapy by examining slope changes in the thermal strain curve caused by speed of sound variations with temperature. The sound speed of water-bearing tissue such as cardiac muscle increases with temperature. However, at temperatures above about 50°C, there is no further increase in the sound speed and the temperature coefficient may become slightly negative. For ablation therapy, an irreversible injury to tissue and a complete heart block occurs in the range of 48 to 50°C for a short period in accordance with the well-known Arrhenius equation. Using these two properties, we propose a potential tool to detect the moment when tissue damage occurs by using the reduced slope in the thermal strain curve as a function of heating time. We have illustrated the feasibility of this method initially using porcine myocardium in vitro. The method was further demonstrated in vivo, using a specially equipped ablation tip and an 11-MHz microlinear intracardiac echocardiography (ICE) array mounted on the tip of a catheter. The thermal strain curves showed a plateau, strongly suggesting that the temperature reached at least 50°C.

In ultrasound strain and elasticity imaging, an accurate and cost-effective sub-pixel displacement estimator is required because strain/elasticity imaging quality relies on the displacement SNR, which can often be higher if more computational resources are provided. In this paper, we introduce an autocorrelation-based method to cost-effectively improve sub-pixel displacement estimation quality. To quantitatively evaluate the performance of the autocorrelation method, simulated and tissue-mimicking phantom experiments were performed. The computational cost of the autocorrelation method is also discussed. The results of our study suggest the autocorrelation method can be used for a real-time elasticity imaging system.