Towards Standardized Acquisition With a Dual-Probe Ultrasound Robot for Fetal Imaging
Standardized acquisitions and diagnoses using robots and AI would potentially increase the general usability and reliability of medical ultrasound. Working towards this prospect, this letter presents the recent developments of a standardized acquisition workflow using a novel dual-probe ultrasound r...
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| Vydáno v: | IEEE robotics and automation letters Ročník 6; číslo 2; s. 1059 - 1065 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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IEEE
01.04.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 2377-3766, 2377-3766 |
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| Abstract | Standardized acquisitions and diagnoses using robots and AI would potentially increase the general usability and reliability of medical ultrasound. Working towards this prospect, this letter presents the recent developments of a standardized acquisition workflow using a novel dual-probe ultrasound robot, for a project known as intelligent Fetal Imaging and Diagnosis (iFIND). The workflow includes an abdominal surface mapping step to obtain a non-parametric spline surface, a rule-based end-point calculation method to position each individual joint, and a motor synchronization method to achieve a smooth motion towards a target point. The design and implementation of the robot are first presented in this letter and the proposed workflow is then explained in detail with simulation and volunteer experiments performed and analyzed. The closed-form analytical solution to the specific motion planning problem has demonstrated a reliable performance controlling the robot to move towards the expected scanning areas and the calculated proximity of the robot to the surface shows that the robot maintains a safe distance while moving around the abdomen. The volunteer study has successfully demonstrated the reliable working and controllability of the robot in terms of acquiring desired ultrasound views. Our future work will focus on improving the motion planning, and on integrating the proposed standardized acquisition workflow with newly-developed ultrasound image processing methods to obtain diagnostic results in an accurate and consistent way. |
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| AbstractList | Standardized acquisitions and diagnoses using robots and AI would potentially increase the general usability and reliability of medical ultrasound. Working towards this prospect, this paper presents the recent developments of a standardized acquisition workflow using a novel dual-probe ultrasound robot, for a project known as intelligent Fetal Imaging and Diagnosis (iFIND). The workflow includes an abdominal surface mapping step to obtain a non-parametric spline surface, a rule-based end-point calculation method to position each individual joint, and a motor synchronization method to achieve a smooth motion towards a target point. The design and implementation of the robot are first presented in this paper and the proposed workflow is then explained in detail with simulation and volunteer experiments performed and analyzed. The closed-form analytical solution to the specific motion planning problem has demonstrated a reliable performance controlling the robot to move towards the expected scanning areas and the calculated proximity of the robot to the surface shows that the robot maintains a safe distance while moving around the abdomen. The volunteer study has successfully demonstrated the reliable working and controllability of the robot in terms of acquiring desired ultrasound views. Our future work will focus on improving the motion planning, and on integrating the proposed standardized acquisition workflow with newly- developed ultrasound image processing methods to obtain diagnostic results in an accurate and consistent way. Standardized acquisitions and diagnoses using robots and AI would potentially increase the general usability and reliability of medical ultrasound. Working towards this prospect, this paper presents the recent developments of a standardized acquisition workflow using a novel dual-probe ultrasound robot, for a project known as intelligent Fetal Imaging and Diagnosis (iFIND). The workflow includes an abdominal surface mapping step to obtain a non-parametric spline surface, a rule-based end-point calculation method to position each individual joint, and a motor synchronization method to achieve a smooth motion towards a target point. The design and implementation of the robot are first presented in this paper and the proposed workflow is then explained in detail with simulation and volunteer experiments performed and analyzed. The closed-form analytical solution to the specific motion planning problem has demonstrated a reliable performance controlling the robot to move towards the expected scanning areas and the calculated proximity of the robot to the surface shows that the robot maintains a safe distance while moving around the abdomen. The volunteer study has successfully demonstrated the reliable working and controllability of the robot in terms of acquiring desired ultrasound views. Our future work will focus on improving the motion planning, and on integrating the proposed standardized acquisition workflow with newly- developed ultrasound image processing methods to obtain diagnostic results in an accurate and consistent way.Standardized acquisitions and diagnoses using robots and AI would potentially increase the general usability and reliability of medical ultrasound. Working towards this prospect, this paper presents the recent developments of a standardized acquisition workflow using a novel dual-probe ultrasound robot, for a project known as intelligent Fetal Imaging and Diagnosis (iFIND). The workflow includes an abdominal surface mapping step to obtain a non-parametric spline surface, a rule-based end-point calculation method to position each individual joint, and a motor synchronization method to achieve a smooth motion towards a target point. The design and implementation of the robot are first presented in this paper and the proposed workflow is then explained in detail with simulation and volunteer experiments performed and analyzed. The closed-form analytical solution to the specific motion planning problem has demonstrated a reliable performance controlling the robot to move towards the expected scanning areas and the calculated proximity of the robot to the surface shows that the robot maintains a safe distance while moving around the abdomen. The volunteer study has successfully demonstrated the reliable working and controllability of the robot in terms of acquiring desired ultrasound views. Our future work will focus on improving the motion planning, and on integrating the proposed standardized acquisition workflow with newly- developed ultrasound image processing methods to obtain diagnostic results in an accurate and consistent way. Standardized acquisitions and diagnoses using robots and AI would potentially increase the general usability and reliability of medical ultrasound. Working towards this prospect, this letter presents the recent developments of a standardized acquisition workflow using a novel dual-probe ultrasound robot, for a project known as intelligent Fetal Imaging and Diagnosis (iFIND). The workflow includes an abdominal surface mapping step to obtain a non-parametric spline surface, a rule-based end-point calculation method to position each individual joint, and a motor synchronization method to achieve a smooth motion towards a target point. The design and implementation of the robot are first presented in this letter and the proposed workflow is then explained in detail with simulation and volunteer experiments performed and analyzed. The closed-form analytical solution to the specific motion planning problem has demonstrated a reliable performance controlling the robot to move towards the expected scanning areas and the calculated proximity of the robot to the surface shows that the robot maintains a safe distance while moving around the abdomen. The volunteer study has successfully demonstrated the reliable working and controllability of the robot in terms of acquiring desired ultrasound views. Our future work will focus on improving the motion planning, and on integrating the proposed standardized acquisition workflow with newly-developed ultrasound image processing methods to obtain diagnostic results in an accurate and consistent way. |
| Author | Singh, Anisha Bao, Xianqiang Housden, James Matthew, Jacqueline Eltiraifi, Olla Wang, Shuangyi Skelton, Emily Noh, Yohan Singh, Davinder Rhode, Kawal Zheng, Jia |
| AuthorAffiliation | 3 School of General Engineering, Beihang University, Beijing 100191, China 4 Department of Mechanical and Aerospace Engineering, Brunel University, London UB8 3PH, UK 1 School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK 2 State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China 5 Xtronics, Ltd., Gravesend, Kent DA12 2AD, UK |
| AuthorAffiliation_xml | – name: 4 Department of Mechanical and Aerospace Engineering, Brunel University, London UB8 3PH, UK – name: 3 School of General Engineering, Beihang University, Beijing 100191, China – name: 5 Xtronics, Ltd., Gravesend, Kent DA12 2AD, UK – name: 2 State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China – name: 1 School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK |
| Author_xml | – sequence: 1 givenname: James surname: Housden fullname: Housden, James email: richard.housden@kcl.ac.uk organization: School of Biomedical Engineering and Imaging Sciences, King's College London, London, U.K – sequence: 2 givenname: Shuangyi orcidid: 0000-0003-4316-3259 surname: Wang fullname: Wang, Shuangyi email: shuangyi.wang@ia.ac.cn organization: State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China – sequence: 3 givenname: Xianqiang orcidid: 0000-0001-9723-9883 surname: Bao fullname: Bao, Xianqiang email: xianqiang.bao@kcl.ac.uk organization: School of Biomedical Engineering and Imaging Sciences, King's College London, London, U.K – sequence: 4 givenname: Jia surname: Zheng fullname: Zheng, Jia email: zhengjia210@buaa.edu.cn organization: School of General Engineering, Beihang University, Beijing, China – sequence: 5 givenname: Emily surname: Skelton fullname: Skelton, Emily email: emily.skelton@kcl.ac.uk organization: School of Biomedical Engineering and Imaging Sciences, King's College London, London, U.K – sequence: 6 givenname: Jacqueline surname: Matthew fullname: Matthew, Jacqueline email: jacqueline.matthew@kcl.ac.uk organization: School of Biomedical Engineering and Imaging Sciences, King's College London, London, U.K – sequence: 7 givenname: Yohan orcidid: 0000-0002-1103-6397 surname: Noh fullname: Noh, Yohan email: yohan.noh@brunel.ac.uk organization: Department of Mechanical and Aerospace Engineering, Brunel University, London, U.K – sequence: 8 givenname: Olla surname: Eltiraifi fullname: Eltiraifi, Olla email: olla.eltiraifi@kcl.ac.uk organization: School of Biomedical Engineering and Imaging Sciences, King's College London, London, U.K – sequence: 9 givenname: Anisha surname: Singh fullname: Singh, Anisha email: xtronics-ltd@outlook.com organization: Xtronics, Ltd., Gravesend, U.K – sequence: 10 givenname: Davinder surname: Singh fullname: Singh, Davinder email: xtronics@me.com organization: Xtronics, Ltd., Gravesend, U.K – sequence: 11 givenname: Kawal surname: Rhode fullname: Rhode, Kawal email: kawal.rhode@kcl.ac.uk organization: School of Biomedical Engineering and Imaging Sciences, King's College London, London, U.K |
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| Cites_doi | 10.1007/s11548-018-1759-x 10.1258/smj.2011.011107 10.3390/app9091900 10.1097/00043764-199911000-00010 10.1007/978-3-030-32254-0_70 10.1007/978-3-030-32248-9_43 10.1109/TMI.2017.2712367 10.1109/TMI.2019.2913311 10.1109/TUFFC.2013.2593 10.1111/j.1365-3156.2012.03014.x 10.1109/70.988970 10.1007/978-3-7091-0277-0_42 10.1109/MRA.2016.2580478 10.1002/rcs.1691 10.1542/peds.2014-3783 10.2214/AJR.16.16780 |
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| References | ref13 ref12 ref15 ref14 housden (ref19) 0 ref11 ref10 wang (ref16) 0 ref2 ref1 ref17 ref8 ref7 (ref20) 2020 ref9 ref4 ref3 ref6 ref5 yohan (ref18) 2019 |
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| SubjectTerms | Abdomen Controllability Exact solutions Force Image acquisition Image processing Mathematical analysis Medical imaging Medical robots and systems motion and path planning Motion planning Probes Robots Sensors software-hardware integration for robot systems Springs Stability Synchronism Torque Ultrasonic imaging Ultrasonic testing Ultrasound Workflow |
| Title | Towards Standardized Acquisition With a Dual-Probe Ultrasound Robot for Fetal Imaging |
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