基于DWT-IRLS算法的超声断层成像方法研究

Research on ultrasound tomography based on DWT-IRLS algorithm

超声成像因非侵入式、成本低且实时性好而被广泛应用。超声系统需要大量的采集通道数据和较高的采样率来提高图像重建质量,导致成像耗时,系统复杂。压缩感知(compressed sensing, CS)算法能够在欠采样的条件下用较少的测量值重构出原始信号。因此,针对系统面临的采样率高,数据量大的问题,本文将CS理论中的DWT-IRLS算法应用在超声成像中,通过离散小波变换基(discrete wavelet transformation, DWT)对超声数据进行稀疏转换,对高低频系数进行采样测量,并使用迭代重加权最小二乘法(iterative reweighted least squares, IRLS)进行测量系数重构,最后对变换域系数进行DWT逆转换得到重建图像。通过实验分析,以50%原始数据重建图像效果逐渐趋于稳定,在均方误差和峰值信噪比方面进行对比分析,DWT-IRLS算法相比较于DWT-OMP、DWT-CoSamp和DCT-IRLS等重构算法,成像质量更高,细节特征更为明显。

Ultrasonic imaging is widely used because of its non-invasive,low cost and good real-time performance.A large number of acquisition channel data and high sampling rate are needed by ultrasound system to improve the quality of image reconstruction,resulting in time-consuming imaging and complex system.Compressed sensing(CS)algorithm only needs a small number of measurements to reconstruct the original signal under the condition of under sampling.In order to solve the problem of high sampling rate and large data volume faced by the system,DWT-IRLS algorithm in compressed sensing theory is applied to ultrasonic imaging in this paper.The ultrasonic data are sparse transformed through discrete wavelet transformation(DWT),the high and low frequency coefficients are sampled and measured,and the iterative reweighted least squares(IRLS)method is introduced to reconstruct the measurement coefficients.Finally,the reconstructed image is obtained by DWT inverse transformation.Through experimental analysis,the image reconstruction effect with 50%of the original data gradually tends to be stable.Compared with the reconstruction algorithms such as DWT-OMP,DWT-CoSamp and DCT-IRLS,DWT-IRLS algorithm has higher imaging quality and more obvious detail features.