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.
Keywords
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Holfort, I.K., Gran, F., Jensen, J.A.: Plane wave medical ultrasound imaging using adaptive beamforming. In: Sensor Array and Multichannel Signal Processing Workshop, pp. 288–292 (2008)
Sasso, M., Cohen-Bacrie, C.: Medical ultrasound imaging using the fully adaptive beamformer. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 489–492 (2005)
Zhao, J., Wang, Y., Zeng, X.: Plane wave compounding based on a joint transmitting-receiving adaptive beamformer. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 62(8), 1440–1452 (2015)
Kruizinga, P., Mastik, F., De, J.N.: Plane-wave ultrasound beamforming using a nonuniform fast Fourier transform. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 59(12), 2684–2691 (2012)
Zhou, F., Yang, W., Liao, Q.: Interpolation-based image super-resolution using multi-surface fitting. IEEE Trans. Image Process. 21(7), 3312–3318 (2012)
Ji, H., Ller, C.: Robust wavelet-based super-resolution reconstruction: theory and algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 31(4), 649–660 (2008)
Zhao, N., Wei, Q., Basarab, A.: Single image super-resolution of medical ultrasound im-ages using a fast algorithm. In: IEEE International Symposium on Biomedical Imaging, pp. 473–476 (2016)
Gu, S., Zuo, W., Xie, Q.: Convolutional sparse coding for image super-resolution. In: IEEE International Conference on Computer Vision, pp. 1823–1831 (2015)
Dong, C., Loy, C.C., He, K.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016)
Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1646–1654 (2016)
Bahrami, K., Shi, F., Rekik, I.: Convolutional neural network for reconstruction of 7T-like Images from 3T MRI using appearance and anatomical features. In: Deep Learning and Data Labeling for Medical Applications, pp. 39–47 (2016)
Liebgott, H., Rodriguez-Molares, A., Cervenansky, F.: Plane-wave imaging challenge in medical ultrasound. In: Ultrasonics Symposium, pp. 1–4 (2016)
Huynh-Thu, Q., Ghanbari, M.: Scope of validity of PSNR in image/video quality assessment. Electron. Lett. 44(13), 800–801 (2008)
Hore, A., Ziou, D.: Image quality metrics: PSNR vs. SSIM. In: International Conference on Pattern Recognition, pp. 2366–2369 (2010)
Li, J., Zhang, X., Ding, M.: Image quality assessment based on regional mutual in-formation. In: International Conference on Intelligent Computation and Bio-Medical Instrumentation, pp. 113–115 (2011)
Amiri, I.S., Ahmad, H., Al-Khafaji, H.M.: Full width at half maximum (FWHM) analysis of solitonic pulse applicable in optical network communication. Am. J. Networks Commun. 4(2–1), 1–5 (2015)
Zhao, J., Wang, Y., Yu, J., Guo, W., Li, T., Zheng, Y.: Subarray coherence based postfilter for eigenspace based minimum variance beamformer in ultrasound plane-wave imaging. Ultrasonics 65, 23–33 (2016)
Hoochang, S., Roth, H.R., Gao, M.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016)
Kappeler, A., Yoo, S., Dai, Q.: Video super-resolution with convolutional neural net-works. IEEE Trans. Comput. Imaging 2(2), 109–122 (2016)
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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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