Abstract
Plane wave imaging (PWI) can cover the entire image region by using a single plane wave transmission. The time-saving imaging mode, however, provides poor imaging resolution and contrast. It is highly demanded for the PWI to compensate the weakness in the imaging quality while maintain the ultrafast imaging speed. In this paper, we proposed a multi-scaled convolutional neural network (CNN) model to improve the quality of the PWI. To further increase the convergence rate and robustness of the CNN, a feedback system was added into the iteration process of the stochastic parallel gradient descent (SPGD) optimization. Three different types of data including the simulation, phantom and real human data have been used in the experiment with each class containing 150 pairs of data. The proposed method produced 52% improvement in the peak signal to noise ratio (PSNR) and 4 times improvement in the structural similarity index measurement (SSIM) compared with the original images. Moreover, the proposed method not only guarantees the global convergence, but also improves the converging rate with 15% reduction of the total elapsed time.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (No. 81627804).
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Zhou, Z., Wang, Y., Yu, J., Guo, W., Fang, Z. (2018). Super-Resolution Reconstruction of Plane-Wave Ultrasound Imaging Based on the Improved CNN Method. In: Tavares, J., Natal Jorge, R. (eds) VipIMAGE 2017. ECCOMAS 2017. Lecture Notes in Computational Vision and Biomechanics, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-319-68195-5_12
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DOI: https://doi.org/10.1007/978-3-319-68195-5_12
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