With the introduction of very dense sensor arrays in ultrasound (US) imaging, data transfer rate and data storage became a bottleneck in ultrasound system design. To reduce the amount of sampled channel data, we propose to use a low-rank and joint-sparse model to represent US signals and exploit the correlations between adjacent receiving channels. Results show that the proposed method is adapted to the ultrasound signals and can recover high quality image approximations from as low as 10% of the samples.
Zhang, M, Markovsky, I, Schretter, C & D'Hooge, J 2018, A Low-Rank and Joint-Sparse Model for Ultrasound Signal Reconstruction. in in Proceedings of iTWIST'18: international Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques, CIRM., 32, ArXiv, Marseille, France, pp. 21-23, International Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques, Marseille, France, 21/11/18. <https://arxiv.org/pdf/1812.04843v1.pdf>
Zhang, M., Markovsky, I., Schretter, C., & D'Hooge, J. (2018). A Low-Rank and Joint-Sparse Model for Ultrasound Signal Reconstruction. In in Proceedings of iTWIST'18: international Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques, CIRM (pp. 21-23). Article 32 ArXiv. https://arxiv.org/pdf/1812.04843v1.pdf
@inproceedings{566dd38f79dc4f5ab6de1ee42565241d,
title = "A Low-Rank and Joint-Sparse Model for Ultrasound Signal Reconstruction",
abstract = "With the introduction of very dense sensor arrays in ultrasound (US) imaging, data transfer rate and data storage became a bottleneck in ultrasound system design. To reduce the amount of sampled channel data, we propose to use a low-rank and joint-sparse model to represent US signals and exploit the correlations between adjacent receiving channels. Results show that the proposed method is adapted to the ultrasound signals and can recover high quality image approximations from as low as 10% of the samples.",
keywords = "Electrical Engineering and Systems Science, Signal Processing",
author = "Miaomiao Zhang and Ivan Markovsky and Colas Schretter and Jan D'Hooge",
year = "2018",
month = nov,
day = "23",
language = "English",
pages = "21--23",
booktitle = "in Proceedings of iTWIST'18",
publisher = "ArXiv",
note = "International Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques, iTWIST'18 ; Conference date: 21-11-2018 Through 23-11-2018",
url = "https://sites.google.com/view/itwist18",
}