Research On Low Complexity Adaptive Beamforming Ultrasound Imaging Algorithm

Ultrasound imaging has been widely used in medical and industrial detection field due to its simple operation,high safety and real-time imaging.Digital beamforming algorithm has been a hot research topic as a key technology of ultrasound imaging.Although the adaptive digital beamforming technology improves the imaging resolution and contrast of the traditional and conventional delay-and-sum beamforming technology,there are a large number of complex matrix operations and large amount of data processing in the adaptive digital beamforming technology,so the complexity of these algorithms is very high and the robustness is poor.For these two problems,the main research works in this paper are as follows:Firstly,in view of the large computational complexity and the poor robustness of the generalized sidelobe canceller algorithm,this paper proposed a kind of generalized sidelobe canceller algorithm based on beam domain.Firstly,the discrete cosine transform is used to construct the transformation matrix to transform the ultrasonic echo signal from the array element domain to the beam domain,reducing the dimension of the sample covariance matrix.Compared with the original generalized sidelobe canceller algorithm,the proposed algorithm improves the robustness of image results and reduces the complexity of original algorithm.Secondly,the eigenvalue decomposition based on generalized sidelobe canceller algorithm(IGSC)is also proposed in this paper to improve the robustness and the imaging contrast of GSC.For the IGSC algorithm,firstly,the signal subspace is obtained by performing eigenvalue decomposition on the covariance matrix of received data.Secondly,the weighting vector of generalized sidelobe canceller(GSC)is divided into adaptive and non-adaptive two parts.Then the non-adaptive part is projected into the signal subspace to obtain a new steer vector.Subsequently,based on the orthogonal complementary space of the new steer vector,the blocking matrix is constructed.Finally,the weighting vector is updated by projecting the final weighting vector into the signal subspace.The simulation imaging results show that the proposed method outperforms the original algorithm in terms of imaging contrast and the robustness of algorithm.Thirdly,for the large computational complexity and the poor robustness of the ESBMV algorithm.A low-complexity minimum variance algorithm combined with eigenvalue decomposition(IBMV)is proposed in this paper,for the IBMV algorithm,firstly,the echo signals are transformed into beam domain by the Discrete Cosine transformation.Then,the eigenvalue decomposition of the sample covariance matrix is used to extract the signal subspace.The largest eigenvalue and its corresponding eigenvector can be extracted,and the other eigenvalues are taken the same value when the trace of sample covariance matrix remains unchanged.By these ways,the inverse operation of the covariance matrix can be simplified to the multiplication of vectors.The complexity of the algorithm is reduced from both the reduction of matrix dimension and the simplicity of matrix inversion.Fourthly,for the large computational complexity of the minimum variance algorithm,the minimum variance algorithm based on Fast Fourier transform combined with coherence factor(STFTMV-CF)is proposed in this paper.The STFTMV-CF algorithm firstly artificially segments the ultrasonic echo data by Fast Fourier transform to obtain frequency domain data.Then,every part of data performs the minimum variance algorithm to get weighting vector separately.Due to the conjugate symmetry of the Fast Fourier transform,only half of the data is weighted and summed and the other part is obtained by conjugate symmetric operation.At last,the beamforming results of all echo data are combined with the frequency domain coherence factor.The simulation imaging results show that the proposed method outperforms the original algorithms in terms of imaging resolution and the running time of algorithm compared with ESBMV and better than the MV in terms of imaging resolution,contrast and the running time of algorithm.