Compressed ultrasound signal reconstruction using a low-rank and joint-sparse representation model

With the introduction of very dense sensor arrays in ultrasound imaging, data transfer rate and data storage can become a bottle neck in ultrasound system design. To reduce the amount of sampled channel data, we proposed a new approach based on the low-rank and joint-sparse model that allows to explore the correlations between different ultrasound channels and transmissions. With this method, the minimum number of measurements at each channel can be lower than the sparsity in compressive sensing theory. The accuracy of reconstruction is less dependent on the sparse basis. An optimization algorithm, based on simultaneous direction method of multipliers, is proposed to efficiently solve the resulting optimization problem. Results on different data sets with different experimental settings show that the proposed method is better adapted to the ultrasound signals and can recover the image with fewer samples (e.g. 10\% of the samples), while maintaining adequate image quality.

References

  1. M. Zhang, I. Markovsky, C. Schretter, and J. D'hooge. Compressed ultrasound signal reconstruction using a low-rank and joint-sparse representation model. Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2019.
  2. M. Zhang, I. Markovsky, C. Schretter, and J. D'hooge. Ultrasound signal reconstruction from sparse samples using a low-rank and joint-sparse model. In In Proceedings of iTWIST'18, Paper-ID: 21, Marseilles, France, 2018.
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