Abstract
Plane wave ultrasound imaging has helped to achieve high frame rate ultrasound, however the data required to achieve frames rates over 1000 fps remains challenging to handle, as the transfer of large amounts of data represents a bottleneck for image reconstruction. This paper presents a novel method of using a fully convolutional encoder-decoder deep neural network to interpolate pre-beamformed raw RF data from ultrasound transducer elements. The network is trained on in vivo human carotid data, then tested on both carotid data and a standard ultrasound phantom. The neural network outputs are compared to linear interpolation and the proposed method captures more meaningful patterns in the signal; the output channels are then combined with the non-interpolated channels and beamformed to form an image, showing not only significant improvement in mean-squared error compared to the alternatives, but also 10–15 dB reduction in grating lobe artifacts. The proposed method has implications for current ultrasound research directions, with applications to real-time high frame rate ultrasound and 3D ultrasound imaging.
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References
Garcia, D., Tarnec, L., Muth, S., Montagnon, E., Poree, J., Cloutier, G.: Stolt’s f-k migration for plane wave ultrasound imaging. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 60, 1853–1867 (2013)
Montaldo, G., Tanter, M., Bercoff, J., Benech, N., Fink, M.: Coherent plane-wave compounding for very high frame rate ultrasonography and transient elastography. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 56, 489–506 (2009)
Boni, E., Yu, A., Freear, S., Jensen, J., Tortoli, P.: Ultrasound open platforms for next-generation imaging technique development. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 65, 1078–1092 (2018)
Yiu, B., Tsang, I., Yu, A.: GPU-based beamformer: fast realization of plane wave compounding and synthetic aperture imaging. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 58, 1698–1705 (2011)
Liebgott, H., Prost, R., Friboulet, D.: Pre-beamformed RF signal reconstruction in medical ultrasound using compressive sensing. Ultrasonics 53, 525–533 (2013)
Yoon, Y., Khan, S., Huh, J., Ye, J.: Efficient B-mode ultrasound image reconstruction from sub-sampled RF data using deep learning. IEEE Trans. Med. Imaging 38, 325–336 (2019)
Carotenuto, R., Sabbi, G., Pappalardo, M.: Spatial resolution enhancement of ultrasound images using neural networks. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 49, 1039–1049 (2002)
Zhou, B., Lapedriza, A., Xiao, J., Torralba, A., Oliva, A.: Learning deep features for scene recognition using places database. In: Advances in Neural Information Processing Systems, pp. 487–495 (2014)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39, 2481–2495 (2017)
Xie, J., Xu, L., Chen, E.: Image denoising and inpainting with deep neural networks. In: Advances in Neural Information Processing Systems, pp. 341–349 (2012)
Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.: Striving for Simplicity: The All Convolutional Net. arXiv preprint arXiv:1412.6806 (2014)
Abadi, M., et al.: Tensorflow: a system for large-scale machine learning. In: 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 2016), pp. 265–283 (2016)
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Xiao, D., Yiu, B.Y.S., Chee, A.J.Y., Yu, A.C.H. (2019). Channel Count Reduction for Plane Wave Ultrasound Through Convolutional Neural Network Interpolation. In: Karray, F., Campilho, A., Yu, A. (eds) Image Analysis and Recognition. ICIAR 2019. Lecture Notes in Computer Science(), vol 11663. Springer, Cham. https://doi.org/10.1007/978-3-030-27272-2_39
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DOI: https://doi.org/10.1007/978-3-030-27272-2_39
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