摘要
In synthetic transmit aperture medical ultrasound imaging field,a compressed sensing ultrasound imaging method based on the sparsity in frequency domain is presented in order to reduce huge amount of data and large numbers of receiving channels.First,the sparsity in frequency domain is verified.Then the echo signal is compressively sampled in time-spatial domain based on compressed sensing and the echo signal is reconstructed by solving an optimization problem.Finally the image is made by using the synthetic transmit aperture approach.The experiments based on point target and fetus target are used to verify the proposed method.The MSE,resolution and image quality of reconstructed image and those of original image are compared and analyzed.The results show that only 30%amount of data and 50%of receiving channels were used to implement ultrasound imaging without reducing the quality of image in experiment.The amount of data and the complexity of system are reduced greatly by the proposed method based on compressed sensing.
In synthetic transmit aperture medical ultrasound imaging field,a compressed sensing ultrasound imaging method based on the sparsity in frequency domain is presented in order to reduce huge amount of data and large numbers of receiving channels.First,the sparsity in frequency domain is verified.Then the echo signal is compressively sampled in time-spatial domain based on compressed sensing and the echo signal is reconstructed by solving an optimization problem.Finally the image is made by using the synthetic transmit aperture approach.The experiments based on point target and fetus target are used to verify the proposed method.The MSE,resolution and image quality of reconstructed image and those of original image are compared and analyzed.The results show that only 30%amount of data and 50%of receiving channels were used to implement ultrasound imaging without reducing the quality of image in experiment.The amount of data and the complexity of system are reduced greatly by the proposed method based on compressed sensing.
基金
supported by Main Direction Program of Knowledge Innovation of Chinese Academy of Sciences(KGCX2-YW-915)
the National Natural Science Foundation of China(11204346)
