@inproceedings{21c47bd6c20e49249909bc8ffadda909,
title = "Compressive-sensing-guided design of sparse receiver arrays for ultrasound imaging system",
abstract = "Efficient implementation of ultrasound (US) imaging systems is crucial for US applications. Previous studies have shown the utilization of compressive sensing (CS) to simplify US systems by reducing the channels of receiver arrays. We introduce a CS-based optimization approach tailored for the design of receiver arrays without any prior information about target objects. Simulation results demonstrate the benefit of the proposed optimization approach compared to previous uniform or random selection scheme based receiver channel selection in terms of location estimations and shape estimations of objects from reconstructed images.",
keywords = "Ultrasound imaging, compressive sensing, location estimation, restricted isometry property",
author = "Chunlei Xu and Guzman, \{Yuneisy Esthela Garcia\} and Zhongjie Zhang and Liang Zeng and Tingzhong Xu",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 International Workshop on the Theory of Computational Sensing and its Applications to Radar, Multimodal Sensing and Imaging, CoSeRa 2024 ; Conference date: 18-09-2024 Through 20-09-2024",
year = "2024",
doi = "10.1109/CoSeRa60846.2024.10720364",
language = "英语",
series = "2024 International Workshop on the Theory of Computational Sensing and its Applications to Radar, Multimodal Sensing and Imaging, CoSeRa 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "91--95",
booktitle = "2024 International Workshop on the Theory of Computational Sensing and its Applications to Radar, Multimodal Sensing and Imaging, CoSeRa 2024",
}