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Mallard S, Kennedy N, Najafzadeh Abriz A, Quinton A. Exploring the use of knobology for image optimisation in final year sonography students. ULTRASOUND (LEEDS, ENGLAND) 2022; 30:299-306. [PMID: 36969539 PMCID: PMC10034655 DOI: 10.1177/1742271x211053029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 09/17/2021] [Indexed: 11/17/2022]
Abstract
Introduction Image optimisation is essential for acquisition of quality images in ultrasound and critical to the diagnostic ability of the examination. These skills are taught to sonography students early in their education, but research has found that retention of non-rehearsed knowledge decreases significantly after a year. The aim of this study was to determine which optimisation tools (knobology) final year sonography students use, how often and why they chose to adjust parameters and assess barriers to optimisation of knobology tools. Methods A prospective study using data from an anonymous online survey of 34 final year sonography students. Results Survey results showed that 19/34 (55%) of students "frequently" optimise all Doppler settings and 23/34 (67%) of students "frequently" optimise basic parameter settings (depth, focus, time gain compensation). Time constraints (24/34 (70%)) and loss of gained knowledge of sonography principles and instrumentation (17/34 (50%)) were the major barriers to the use of knobology. The majority 28/34 (82%) believed they would benefit from further training. Conclusion This study demonstrates that although most students are optimising settings to improve image quality, sonography principles and instrumentation knowledge loss and time constraints prevent students from maximising machine capabilities. This study supports the need for further training prior to final year clinical placement.
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Affiliation(s)
| | - Narelle Kennedy
- Discipline of Obstetrics, Gynaecology and Neonatology, Sydney Medical
School Nepean, University of Sydney, Nepean Hospital, Penrith, NSW, Australia
| | - Afrooz Najafzadeh Abriz
- Medical Sonography School of Health, Medical and Applied Science, Central
Queensland University, Perth, WA, Australia
| | - Ann Quinton
- Medical Sonography School of Health, Medical and Applied Science, Central
Queensland University, Perth, WA, Australia
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Wang Q, Chan KF, Schweizer K, Du X, Jin D, Yu SCH, Nelson BJ, Zhang L. Ultrasound Doppler-guided real-time navigation of a magnetic microswarm for active endovascular delivery. SCIENCE ADVANCES 2021; 7:7/9/eabe5914. [PMID: 33637532 PMCID: PMC7909881 DOI: 10.1126/sciadv.abe5914] [Citation(s) in RCA: 121] [Impact Index Per Article: 40.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 01/12/2021] [Indexed: 05/18/2023]
Abstract
Swarming micro/nanorobots offer great promise in performing targeted delivery inside diverse hard-to-reach environments. However, swarm navigation in dynamic environments challenges delivery capability and real-time swarm localization. Here, we report a strategy to navigate a nanoparticle microswarm in real time under ultrasound Doppler imaging guidance for active endovascular delivery. A magnetic microswarm was formed and navigated near the boundary of vessels, where the reduced drag of blood flow and strong interactions between nanoparticles enable upstream and downstream navigation in flowing blood (mean velocity up to 40.8 mm/s). The microswarm-induced three-dimensional blood flow enables Doppler imaging from multiple viewing configurations and real-time tracking in different environments (i.e., stagnant, flowing blood, and pulsatile flow). We also demonstrate the ultrasound Doppler-guided swarm formation and navigation in the porcine coronary artery ex vivo. Our strategy presents a promising connection between swarm control and real-time imaging of microrobotic swarms for localized delivery in dynamic environments.
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Affiliation(s)
- Qianqian Wang
- Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong (CUHK), Shatin, NT, Hong Kong, China
| | - Kai Fung Chan
- Chow Yuk Ho Technology Centre for Innovative Medicine, CUHK, Shatin, NT, Hong Kong, China
- Department of Biomedical Engineering, CUHK, Shatin, NT, Hong Kong, China
| | - Kathrin Schweizer
- Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong (CUHK), Shatin, NT, Hong Kong, China
- Institute of Robotics and Intelligent Systems, ETH Zurich, Zurich, Switzerland
| | - Xingzhou Du
- Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong (CUHK), Shatin, NT, Hong Kong, China
- Department of Biomedical Engineering, CUHK, Shatin, NT, Hong Kong, China
| | - Dongdong Jin
- Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong (CUHK), Shatin, NT, Hong Kong, China
- Department of Biomedical Engineering, CUHK, Shatin, NT, Hong Kong, China
| | - Simon Chun Ho Yu
- Department of Imaging and Interventional Radiology, CUHK, Shatin, NT, Hong Kong, China
| | - Bradley J Nelson
- Institute of Robotics and Intelligent Systems, ETH Zurich, Zurich, Switzerland
| | - Li Zhang
- Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong (CUHK), Shatin, NT, Hong Kong, China.
- Chow Yuk Ho Technology Centre for Innovative Medicine, CUHK, Shatin, NT, Hong Kong, China
- CUHK T Stone Robotics Institute, CUHK, Shatin, NT, Hong Kong, China
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