Abstract
Deep learning has been applied to ultrasound imaging recently, and it needs to be further studied to improve ultrasound beamforming methods. According to the latest research, deep neural network was able to suppress off-axis scattering signals in ultrasound channel data, which enhanced the performance of beamforming and improved the contrast of the output ultrasound images. Minimum variance beamforming was capable to present high lateral resolution, but lacked of high image contrast of ultrasound images. In order to effectively improve the contrast of minimum variance beamforming, this work investigated the combination of deep neural network and minimum variance beamforming. In the experiments, the simulated point target and cyst scenarios were adopted to evaluate the performance of the proposed methods. The results demonstrated that combining deep neural network and minimum variance beamforming can effectively reduce the side lobe level and thus can improve the contrast of the ultrasound images while maintaining the lateral resolution performance.
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Asl, B.M., Mahloojifar, A.: Eigenspace-based minimum variance beamforming applied to medical ultrasound imaging. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 57(11), 2381–2390 (2010)
Asl, B.M., Mahloojifar, A.: Contrast enhancement and robustness improvement of adaptive ultrasound imaging using forward-backward minimum variance beamforming. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 58(4), 858–867 (2011)
Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y.: Deep Learning. MIT Press, Cambridge (2016)
Havlice, J.F., Taenzer, J.C.: Medical ultrasonic imaging: an overview of principles and instrumentation. Proc. IEEE 67(4), 620–641 (1979)
He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of IEEE International Conference on Computer Vision, pp. 1026–1034 (2015)
Jensen, J.A.: Field: a program for simulating ultrasound systems. In: Proceedings of Nordic-Balttc Conference on Biomedical Imaging, pp. 351–353 (1996)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Proceedings of International Conference for Learning Representations, pp. 1–15 (2015)
Liu, T., Zhao, H., Zheng, Y.: Eigenspace-based minimum variance beamforming combined with sign coherence factor for ultrasound beamforming. Acta Acustica 40(6), 855–862 (2015). (in Chinese)
Luchies, A.C., Byram, B.C.: Deep neural networks for ultrasound beamforming. IEEE Trans. Med. Imaging 37(9), 2010–2021 (2018)
Simson, W., Paschali, M., Navab, N., Zahnd, G.: Deep learning beamforming for sub-sampled ultrasound data. In: Proceedings of IEEE International Ultrasonics Symposium, pp. 1–4 (2018)
Synnevåg, J.F., Austeng, A., Holm, S.: Adaptive beamforming applied to medical ultrasound imaging. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 54(8), 1606–1613 (2007)
Zaharis, Z.D., Gotsisa, K.A., Sahalos, J.N.: Adaptive beamforming with low side lobe level using neural networks trained by mutated boolean PSO. Prog. Electromagnet. Res. 127, 139–154 (2012)
Zaharis, Z.D., et al.: Implementation of antenna array beamforming by using a novel neural network structure. In: Proceedings of International Conference on Telecommunications and Multimedia, pp. 25–27 (2016)
Acknowledgements
This work is supported by “National Natural Science Foundation of China” (No. 61802130), “Guangdong Natural Science Foundation” (No. 2018A030310355), and “Guangzhou Science and Technology Program” (No. 201707010223).
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Zhuang, R., Chen, J. (2019). Deep Learning Based Minimum Variance Beamforming for Ultrasound Imaging. In: Wang, Q., et al. Smart Ultrasound Imaging and Perinatal, Preterm and Paediatric Image Analysis. PIPPI SUSI 2019 2019. Lecture Notes in Computer Science(), vol 11798. Springer, Cham. https://doi.org/10.1007/978-3-030-32875-7_10
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DOI: https://doi.org/10.1007/978-3-030-32875-7_10
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