Eigenvalue decomposition-fused low-complexity minimum variance ultrasound imaging method

Abstract

The invention relates to an eigenvalue decomposition-fused low-complexity minimum variance ultrasound imaging method, and belongs to the field of ultrasound imaging. Firstly, echo data is converted toa beam domain with fewer dimensions by means of discrete cosine transform; and then a sample covariance matrix is subjected to eigenvalue decomposition to extract signal subspaces, a maximum eigenvalue and an eigenvector corresponding to the maximum eigenvalue are selected, remaining eigenvalues are the same value on the condition that it is guaranteed that a trace of the sample covariance matrixis invariable, and inversion of the matrix is simplified into multiplication of a vector. According to the algorithm, the operation time can be obviously shorter than that of an eigenvalue decomposition-based minimum variance algorithm, the good robustness is achieved on noise, and the imaging effect is obviously better than that of a traditional delay and sum algorithm, minimum variance algorithm and beam domain minimum variance algorithm.

Description

technical field [0001] The invention belongs to the field of ultrasonic imaging, and relates to an ultrasonic imaging method with low complexity and minimum variance combined with eigenvalue decomposition. 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 adaptive algor...

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Problems solved by technology

[0004] In addition, the adaptive algorithm is not as robust and efficient as the DAS algorithm. The main reason for these problems is that the adaptive algorithm involves matrix inversion and matrix multiplication operations, resulting in higher algorithm complexity.

Assuming an n×n-dimensional matrix, the complexity of inversion is O(n 3 ), while the traditional DAS algorithm is only O(n); although the minimum variance algorithm based on eigenvalue decomposition has a good imaging effect, it has the problems of high complexity and poor robustness

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