Abstract
Deep learning takes the lead in developing new ultrasound beamforming techniques, the rapid development of new models in computer vision, image segmentation, and classification has turned ultrasound beamforming into an image processing problem. The CNN and GAN structures achieved noticeable performance compared with the classical methods regarding B-mode image formation time and accuracy; nonetheless, using the time series with a sequence-to-sequence approach still needs to be thoroughly investigated. The challenge of acquiring suitable datasets limits any significant advancement in the time series with the Seq2seq approach, in contrast to the image processing approach delivering good results with less training data, in the other side the successive evolution of Seq2seq models applied in (NLP) show some promising future of this approach. This paper outlines the ultrasound Radio-Frequency time series (USRFTS) and the various architectures that tackle this problem, from RNNs and their derivatives like LSTM and GRU to the attention mechanism, and mainly self-attention the mechanism behind the transformers architecture. We also highlighted some limitations in using each model, some of the common obstacles, and the thriving opportunities to use this method in ultrasound B-mode image beamforming.
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References
Smallwood, N., Dachsel, M.: Point-of-care ultrasound (POCUS): unnecessary gadgetry or evidence-based medicine? Clin. Med. J. Royal College Phys. London 18(3), 219–224 (2018)
Synnevag, J.F., Austeng, A., Holm, S.: Adaptive beamforming applied to medical ultrasound imaging. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 54(8), 1606–1613 (2007)
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(3), 489–506 (2009)
Nair, A.A., Tran, T.D., Reiter, A., Lediju Bell, M.A.: A deep learning based alternative to beamforming ultrasound images. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada, pp. 3359–3363 (2018)
Strohm, H., Rothlübbers, S., Eickel, K., et al.: Deep learning-based reconstruction of ultrasound images from raw channel data. Int. J. CARS 15, 1487–1490 (2020)
Goudarzi, S., Asif, A., Rivaz, H.: Ultrasound Beamforming using MobileNetV2. In: 2020 IEEE International Ultrasonics Symposium (IUS), Las Vegas, NV, USA, pp. 1–4 (2020)
Bell, M.A.L., Huang, J., Hyun, D., Eldar, Y.C., van Sloun, R., Mischi, M.: Challenge on ultrasound beamforming with deep learning (CUBDL). In: 2020 IEEE International Ultrasonics Symposium (IUS), Las Vegas, NV, USA, pp. 1–5 (2020)
Wang, Y., Kempski, K., Kang, J.U., Bell, M.A.L.: A conditional adversarial network for single plane wave beamforming. In: 2020 IEEE International Ultrasonics Symposium (IUS), Las Vegas, NV, USA, pp. 1–4 (2020)
Lim, B., Zohren, S.: Time-series forecasting with deep learning: a survey. Philos. Trans. Royal Soc. A: Math. Phys. Eng. Sci. 379 (2194) (2021)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems (2014)
Sherstinsky, A.: Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D: Nonlinear Phenomena 404, 132306 (2020)
Goodfellow, I, Bengio, Y, Courville, A.: Deep learning. MIT Press (2016). http://www.deeplearningbook.org
Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 45(11), 2673–2681 (1997)
Van Houdt, G., Mosquera, C., Nápoles, G.: A review on the long short-term memory model. Artif. Intell. Rev. 53, 5929–5955 (2020)
Chung, J., Gulcehre, C., Cho, K., et al.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)
Sutskever, I., Vinyals, O., et Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, vol. 27 (2014)
Niu, Z., Zhong, G., et Yu, H.: A review on the attention mechanism of deep learning. Neurocomputing 452, 48–62 (2021)
Vaswani, A., Shazeer, N., Parmar, N., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Wen, Q., Zhou, T., Zhang, C., et al.: Transformers in time series: a survey. arXiv preprint arXiv:2202.07125 (2022)
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This work was supported by the National Center of Scientific and Technical Research (CNRST).
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Hadri, H., Fail, A., Sadik, M. (2024). Ultrasound Beamforming: Investigating Time Series with Sequence to Sequence Approach in Deep Learning. In: Ezziyyani, M., Kacprzyk, J., Balas, V.E. (eds) International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD’2023). AI2SD 2023. Lecture Notes in Networks and Systems, vol 904. Springer, Cham. https://doi.org/10.1007/978-3-031-52388-5_9
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