Medical ultrasound image speckle reduction and resolution enhancement using texture compensated multi-resolution convolution neural network.

Autor:	Moinuddin M; Center of Excellence in Intelligent Engineering Systems, King Abdulaziz University, Jeddah, Saudi Arabia.; Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah, Saudi Arabia., Khan S; Department of Bio and Brain Engineering, KAIST, Daejeon, South Korea., Alsaggaf AU; Center of Excellence in Intelligent Engineering Systems, King Abdulaziz University, Jeddah, Saudi Arabia.; Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah, Saudi Arabia., Abdulaal MJ; Center of Excellence in Intelligent Engineering Systems, King Abdulaziz University, Jeddah, Saudi Arabia.; Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah, Saudi Arabia., Al-Saggaf UM; Center of Excellence in Intelligent Engineering Systems, King Abdulaziz University, Jeddah, Saudi Arabia.; Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah, Saudi Arabia., Ye JC; Kim Jaechul Graduate School of AI, KAIST, Daejeon, South Korea.
Jazyk:	angličtina
Zdroj:	Frontiers in physiology [Front Physiol] 2022 Nov 14; Vol. 13, pp. 961571. Date of Electronic Publication: 2022 Nov 14 (Print Publication: 2022).
DOI:	10.3389/fphys.2022.961571
Abstrakt:	Ultrasound (US) imaging is a mature technology that has widespread applications especially in the healthcare sector. Despite its widespread use and popularity, it has an inherent disadvantage that ultrasound images are prone to speckle and other kinds of noise. The image quality in the low-cost ultrasound imaging systems is degraded due to the presence of such noise and low resolution of such ultrasound systems. Herein, we propose a method for image enhancement where, the overall quality of the US images is improved by simultaneous enhancement of US image resolution and noise suppression. To avoid over-smoothing and preserving structural/texture information, we devise texture compensation in our proposed method to retain the useful anatomical features. Moreover, we also utilize US image formation physics knowledge to generate augmentation datasets which can improve the training of our proposed method. Our experimental results showcase the performance of the proposed network as well as the effectiveness of the utilization of US physics knowledge to generate augmentation datasets. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2022 Moinuddin, Khan, Alsaggaf, Abdulaal, Al-Saggaf and Ye.)
Databáze:	MEDLINE
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