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Proceedings Paper

Wavelet shrinkage-based adaptive compounding for improvement of SNR in high volume-rate ultrasound imaging
Author(s): Takashi Toyomura; Teiichiro Ikeda; Misaki Hiroshima; Peifei Zhu
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Paper Abstract

High-speed acquisition of ultrasound volume data is needed for fetal cardiac diagnosis. The heartbeat of healthy fetus is 120-160 times per second. Therefore, the acquiring method based on plane wave compounding has been developed to achieve both volume rate and image quality. However, in the conventional plane wave compounding method, the sufficient image quality couldn’t be obtained when the acquiring speed is about 150 volumes per second. Compressed Sensing has been applied to improve the image quality and reduce the number of compounding in the previous work [1], but huge memory (458GB) is required. In this paper, we propose a method to acquire high quality volume image at high-speed with reasonable hardware resources. The basic concept is to improve the image quality of each plane wave image before compounding by simple signal processing. Generally, the acoustic noise of plane wave image generated by the diffraction depends on the steering angle. In our method, the acoustic noise is adaptively reduced depending on the steering angle, and the wavelet shrinkage [2] is used as a basic noise reduction algorithm. In the experimental results, the acoustic noise is reduced by 22dB with only 20MB memory usage for radio frequency simulation data. As a result, we achieved high-speed data acquisition of 167 volumes per second.

Paper Details

Date Published: 6 March 2018
PDF: 6 pages
Proc. SPIE 10580, Medical Imaging 2018: Ultrasonic Imaging and Tomography, 105800C (6 March 2018); doi: 10.1117/12.2287323
Show Author Affiliations
Takashi Toyomura, Hitachi, Ltd. (Japan)
Teiichiro Ikeda, Hitachi, Ltd. (Japan)
Misaki Hiroshima, Hitachi, Ltd. (Japan)
Peifei Zhu, Hitachi, Ltd. (Japan)

Published in SPIE Proceedings Vol. 10580:
Medical Imaging 2018: Ultrasonic Imaging and Tomography
Neb Duric; Brett C. Byram, Editor(s)

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