A Minimum Variance Ultrasound Imaging Method Based on Signal-to-Noise Ratio Post-filtering and Feature Space Fusion

Abstract

The invention relates to a minimum variance ultrasonic imaging method of post-signal-to-noise filtering and feature space fusion. The method first performs delay, forward and backward smoothing and diagonal loading processing on the sampling signal received by the array element to obtain an estimated sample covariance matrix; Then the estimated covariance matrix is ​​characterized by decomposition to construct a signal subspace; in the desired signal subspace, the adaptive beamforming weights are calculated according to the minimum variance criterion; then the post-filtering coefficients are designed based on the signal coherence, and based on the input signal The signal-to-noise ratio introduces noise weighting coefficients to calculate the signal-to-noise ratio filter coefficients; fuses the adaptive beamforming weights and the signal-to-noise ratio filter coefficients to obtain a new weighted vector; finally, the obtained fused signal-to-noise ratio post-filtering and feature space The minimum variance weight performs a weighted summation of the multi-channel data that has been smoothed forward and backward to obtain an adaptive beam signal. This method can improve the performance of ultrasound images in terms of resolution, contrast, and robustness to noise, thereby improving the overall quality of ultrasound imaging.

Description

technical field

[0001] The invention belongs to the technical field of ultrasonic imaging, and relates to a minimum variance ultrasonic imaging method of signal-to-noise ratio post-filtering and feature space fusion. Background technique

[0002] The most widely used and simplest beamforming technology in ultrasonic imaging is DelayAnd Sum (DAS), which calculates the delay amount of the received echo signal according to the geometric position relationship of the array element channel. Then align and superimpose the delayed data. The traditional DAS algorithm has low complexity and fast imaging speed, but the width of the main lobe is increased and the resolution is low due to the weighting of the fixed window function.

[0003] In recent years, in order to improve the contrast and resolution of beamforming algorithms, adaptive algorithms have been more and more researched. The Minimum Variance (MV) beamforming algorithm proposed by Capon in 1969 is the most widely used ada...

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