Enabling low-cost point-of-care ultrasound imaging system using single element transducer and delta configuration actuator

2019 ◽  
Author(s):  
Keshuai Xu ◽  
Younsu Kim ◽  
Emad M. Boctor ◽  
Haichong K. Zhang
Keyword(s):  
Ultrasound Imaging ◽  
Imaging System ◽  
Point Of Care ◽  
Low Cost ◽  
Single Element ◽  
Point Of Care Ultrasound

Single-element Ultrasound Imaging System based on Mirror Scanning

2020 ◽  
Author(s):  
Seongwook Choi ◽  
Jin Young Kim ◽  
Hae Gyun Lim ◽  
Jin Woo Baik ◽  
Hyung Ham Kim ◽  
...  
Keyword(s):  
Ultrasound Imaging ◽  
Imaging System ◽  
Single Element

scholarly journals Reverse Scan Conversion and Efficient Deep Learning Network Architecture for Ultrasound Imaging on a Mobile Device

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2629
Author(s):  
Kunkyu Lee ◽  
Min Kim ◽  
Changhyun Lim ◽  
Tai-Kyong Song
Keyword(s):  
Deep Learning ◽  
Ultrasound Imaging ◽  
Mobile Device ◽  
Network Architecture ◽  
Point Of Care ◽  
Ultrasound Image ◽  
Training Dataset ◽  
Point Of Care Ultrasound ◽  
Reverse Scan ◽  
Scan Conversion

Point-of-care ultrasound (POCUS), realized by recent developments in portable ultrasound imaging systems for prompt diagnosis and treatment, has become a major tool in accidents or emergencies. Concomitantly, the number of untrained/unskilled staff not familiar with the operation of the ultrasound system for diagnosis is increasing. By providing an imaging guide to assist clinical decisions and support diagnosis, the risk brought by inexperienced users can be managed. Recently, deep learning has been employed to guide users in ultrasound scanning and diagnosis. However, in a cloud-based ultrasonic artificial intelligence system, the use of POCUS is limited due to information security, network integrity, and significant energy consumption. To address this, we propose (1) a structure that simultaneously provides ultrasound imaging and a mobile device-based ultrasound image guide using deep learning, and (2) a reverse scan conversion (RSC) method for building an ultrasound training dataset to increase the accuracy of the deep learning model. Experimental results show that the proposed structure can achieve ultrasound imaging and deep learning simultaneously at a maximum rate of 42.9 frames per second, and that the RSC method improves the image classification accuracy by more than 3%.

Acute Orthodeoxia: Evaluation Using Point-of-Care Ultrasound Imaging

2017 ◽  
Vol 14 (4) ◽  
pp. 594-596 ◽  
Author(s):  
Rishik Vashisht ◽  
Siddharth Dugar ◽  
Narendrakumar Alappan ◽  
Ajit Moghekar
Keyword(s):  
Ultrasound Imaging ◽  
Point Of Care ◽  
Point Of Care Ultrasound

A prototype low-cost handheld ultrasound imaging system

2004 ◽  
Author(s):  
Karthik Ranganathan ◽  
Mary K. Santy ◽  
Michael I. Fuller ◽  
Shiwei Zhou ◽  
Travis N. Blalock ◽  
...  
Keyword(s):  
Ultrasound Imaging ◽  
Imaging System ◽  
Low Cost ◽  
Handheld Ultrasound
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