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Sewify A, Antico M, Alzubaidi L, Alwzwazy HA, Roots J, Pivonka P, Fontanarosa D. Systematic Review of Commercially Available Clinical CMUT-Based Systems for Use in Medical Ultrasound Imaging: Products, Applications, and Performance. SENSORS (BASEL, SWITZERLAND) 2025; 25:2245. [PMID: 40218757 PMCID: PMC11991037 DOI: 10.3390/s25072245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2024] [Revised: 03/18/2025] [Accepted: 03/22/2025] [Indexed: 04/14/2025]
Abstract
An emerging alternative to conventional piezoelectric technologies, which continue to dominate the ultrasound medical imaging (US) market, is Capacitive Micromachined Ultrasonic Transducers (CMUTs). Ultrasound transducers based on this technology offer a wider frequency bandwidth, improved cost-effectiveness, miniaturized size and effective integration with electronics. These features have led to an increase in the commercialization of CMUTs in the last 10 years. We conducted a review to answer three main research questions: (1) What are the commercially available CMUT-based clinical sonographic devices in the medical imaging space? (2) What are the medical imaging applications of these devices? (3) What is the performance of the devices in these applications? We additionally reported on all the future work expressed by modern studies released in the past 2 years to predict the trend of development in future CMUT device developments and express gaps in current research. The search retrieved 19 commercially available sonographic CMUT products belonging to seven companies. Four of the products were clinically approved. Sonographic CMUT devices have established their niche in the medical US imaging market mainly through the Butterfly iQ and iQ+ for quick preliminary screening, emergency care in resource-limited settings, clinical training, teleguidance, and paramedical applications. There were no commercialized 3D CMUT probes.
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Affiliation(s)
- Ahmed Sewify
- School of Clinical Sciences, Queensland University of Technology, Gardens Point Campus, 2 George St., Brisbane, QLD 4000, Australia; (J.R.); (D.F.)
- Centre for Biomedical Technologies (CBT), Queensland University of Technology, Brisbane, QLD 4000, Australia; (M.A.); (P.P.)
- ARC ITTC Centre for Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Maria Antico
- Centre for Biomedical Technologies (CBT), Queensland University of Technology, Brisbane, QLD 4000, Australia; (M.A.); (P.P.)
- Australian e-Health Research Centre, The Commonwealth Scientific and Industrial Research Organisation (CSIRO), 296 Herston Rd., Herston, QLD 4029, Australia
| | - Laith Alzubaidi
- School of Mechanical Medical and Process Engineering, Faculty of Engineering, Queensland University of Technology, Gardens Point Campus, 2 George St., Brisbane, QLD 4000, Australia; (L.A.); (H.A.A.)
| | - Haider A. Alwzwazy
- School of Mechanical Medical and Process Engineering, Faculty of Engineering, Queensland University of Technology, Gardens Point Campus, 2 George St., Brisbane, QLD 4000, Australia; (L.A.); (H.A.A.)
| | - Jacqueline Roots
- School of Clinical Sciences, Queensland University of Technology, Gardens Point Campus, 2 George St., Brisbane, QLD 4000, Australia; (J.R.); (D.F.)
- Centre for Biomedical Technologies (CBT), Queensland University of Technology, Brisbane, QLD 4000, Australia; (M.A.); (P.P.)
| | - Peter Pivonka
- Centre for Biomedical Technologies (CBT), Queensland University of Technology, Brisbane, QLD 4000, Australia; (M.A.); (P.P.)
- ARC ITTC Centre for Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia
- School of Mechanical Medical and Process Engineering, Faculty of Engineering, Queensland University of Technology, Gardens Point Campus, 2 George St., Brisbane, QLD 4000, Australia; (L.A.); (H.A.A.)
| | - Davide Fontanarosa
- School of Clinical Sciences, Queensland University of Technology, Gardens Point Campus, 2 George St., Brisbane, QLD 4000, Australia; (J.R.); (D.F.)
- Centre for Biomedical Technologies (CBT), Queensland University of Technology, Brisbane, QLD 4000, Australia; (M.A.); (P.P.)
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Huang LX, Wu XB, Liu YA, Guo X, Liu CC, Cai WQ, Wang SW, Luo B. High-resolution magnetic resonance vessel wall imaging in ischemic stroke and carotid artery atherosclerotic stenosis: A review. Heliyon 2024; 10:e27948. [PMID: 38571643 PMCID: PMC10987942 DOI: 10.1016/j.heliyon.2024.e27948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 03/02/2024] [Accepted: 03/08/2024] [Indexed: 04/05/2024] Open
Abstract
Ischemic stroke is a significant burden on human health worldwide. Carotid Atherosclerosis stenosis plays an important role in the comprehensive assessment and prevention of ischemic stroke patients. High-resolution vessel wall magnetic resonance imaging has emerged as a successful technique for assessing carotid atherosclerosis stenosis. This advanced imaging modality has shown promise in effectively displaying a wide range of characteristics associated with the condition, leading to a comprehensive evaluation. High-resolution vessel wall magnetic resonance imaging not only enables a comprehensive evaluation of the instability of carotid atherosclerosis stenosis plaques but also provides valuable information for understanding the pathogenesis and predicting the prognosis of ischemic stroke patients. The purpose of this article is to review the application of high-resolution magnetic resonance imaging in ischemic stroke and carotid atherosclerotic stenosis.
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Affiliation(s)
- Li-Xin Huang
- Department of Neurosurgery, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
- Department of Neurosurgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
- Department of Neurosurgery, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China
| | - Xiao-Bing Wu
- Department of Neurosurgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yi-Ao Liu
- Department of Neurosurgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
- Department of Neurosurgery, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China
| | - Xin Guo
- Department of Neurosurgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
- Department of Neurosurgery, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China
| | - Chi-Chen Liu
- Department of Neurosurgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
- Department of Neurosurgery, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China
| | - Wang-Qing Cai
- Department of Neurosurgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Sheng-Wen Wang
- Department of Neurosurgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Bin Luo
- Department of Neurosurgery, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China
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Chang C, Qi F, Xu C, Shen Y, Li Q. A dual-modal dynamic contour-based method for cervical vascular ultrasound image instance segmentation. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:1038-1057. [PMID: 38303453 DOI: 10.3934/mbe.2024043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
OBJECTIVES We intend to develop a dual-modal dynamic contour-based instance segmentation method that is based on carotid artery and jugular vein ultrasound and its optical flow image, then we evaluate its performance in comparison with the classic single-modal deep learning networks. METHOD We collected 2432 carotid artery and jugular vein ultrasound images and divided them into training, validation and test dataset by the ratio of 8:1:1. We then used these ultrasound images to generate optical flow images with clearly defined contours. We also proposed a dual-stream information fusion module to fuse complementary features between different levels extracted from ultrasound and optical flow images. In addition, we proposed a learnable contour initialization method that eliminated the need for manual design of the initial contour, facilitating the rapid regression of nodes on the contour to the ground truth points. RESULTS We verified our method by using a self-built dataset of carotid artery and jugular vein ultrasound images. The quantitative metrics demonstrated a bounding box detection mean average precision of 0.814 and a mask segmentation mean average precision of 0.842. Qualitative analysis of our results showed that our method achieved smoother segmentation boundaries for blood vessels. CONCLUSIONS The dual-modal network we proposed effectively utilizes the complementary features of ultrasound and optical flow images. Compared to traditional single-modal instance segmentation methods, our approach more accurately segments the carotid artery and jugular vein in ultrasound images, demonstrating its potential for reliable and precise medical image analysis.
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Affiliation(s)
- Chenkai Chang
- College of Information Science and Engineering, Hohai University, Changzhou 213200, Jiangsu, China
| | - Fei Qi
- Changzhou No.2 People's Hospital, No.29 Xinglong Lane, Changzhou 213004, Jiangsu, China
| | - Chang Xu
- College of Information Science and Engineering, Hohai University, Changzhou 213200, Jiangsu, China
| | - Yiwei Shen
- College of Information Science and Engineering, Hohai University, Changzhou 213200, Jiangsu, China
| | - Qingwu Li
- College of Information Science and Engineering, Hohai University, Changzhou 213200, Jiangsu, China
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