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Tang X, Wang H, Luo J, Jiang J, Nian F, Qi L, Sang L, Gan Z. Autonomous ultrasound scanning robotic system based on human posture recognition and image servo control: an application for cardiac imaging. Front Robot AI 2024; 11:1383732. [PMID: 38774468 PMCID: PMC11106497 DOI: 10.3389/frobt.2024.1383732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 04/12/2024] [Indexed: 05/24/2024] Open
Abstract
In traditional cardiac ultrasound diagnostics, the process of planning scanning paths and adjusting the ultrasound window relies solely on the experience and intuition of the physician, a method that not only affects the efficiency and quality of cardiac imaging but also increases the workload for physicians. To overcome these challenges, this study introduces a robotic system designed for autonomous cardiac ultrasound scanning, with the goal of advancing both the degree of automation and the quality of imaging in cardiac ultrasound examinations. The system achieves autonomous functionality through two key stages: initially, in the autonomous path planning stage, it utilizes a camera posture adjustment method based on the human body's central region and its planar normal vectors to achieve automatic adjustment of the camera's positioning angle; precise segmentation of the human body point cloud is accomplished through efficient point cloud processing techniques, and precise localization of the region of interest (ROI) based on keypoints of the human body. Furthermore, by applying isometric path slicing and B-spline curve fitting techniques, it independently plans the scanning path and the initial position of the probe. Subsequently, in the autonomous scanning stage, an innovative servo control strategy based on cardiac image edge correction is introduced to optimize the quality of the cardiac ultrasound window, integrating position compensation through admittance control to enhance the stability of autonomous cardiac ultrasound imaging, thereby obtaining a detailed view of the heart's structure and function. A series of experimental validations on human and cardiac models have assessed the system's effectiveness and precision in the correction of camera pose, planning of scanning paths, and control of cardiac ultrasound imaging quality, demonstrating its significant potential for clinical ultrasound scanning applications.
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Affiliation(s)
- Xiuhong Tang
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Hongbo Wang
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Jingjing Luo
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Jinlei Jiang
- Intelligent Robot Engineering Research Center of Ministry of Education, Shanghai, China
| | - Fan Nian
- Intelligent Robot Engineering Research Center of Ministry of Education, Shanghai, China
| | - Lizhe Qi
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Lingfeng Sang
- Institute of Intelligent Medical Care Technology, Ningbo, China
| | - Zhongxue Gan
- Academy for Engineering and Technology, Fudan University, Shanghai, China
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Du W, Zhang L, Suh E, Lin D, Marcus C, Ozkan L, Ahuja A, Fernandez S, Shuvo II, Sadat D, Liu W, Li F, Chandrakasan AP, Ozmen T, Dagdeviren C. Conformable ultrasound breast patch for deep tissue scanning and imaging. SCIENCE ADVANCES 2023; 9:eadh5325. [PMID: 37506210 PMCID: PMC10382022 DOI: 10.1126/sciadv.adh5325] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 06/26/2023] [Indexed: 07/30/2023]
Abstract
Ultrasound is widely used for tissue imaging such as breast cancer diagnosis; however, fundamental challenges limit its integration with wearable technologies, namely, imaging over large-area curvilinear organs. We introduced a wearable, conformable ultrasound breast patch (cUSBr-Patch) that enables standardized and reproducible image acquisition over the entire breast with less reliance on operator training and applied transducer compression. A nature-inspired honeycomb-shaped patch combined with a phased array is guided by an easy-to-operate tracker that provides for large-area, deep scanning, and multiangle breast imaging capability. The in vitro studies and clinical trials reveal that the array using a piezoelectric crystal [Yb/Bi-Pb(In1/2Nb1/2)O3-Pb(Mg1/3Nb2/3)O3-PbTiO3] (Yb/Bi-PIN-PMN-PT) exhibits a sufficient contrast resolution (~3 dB) and axial/lateral resolutions of 0.25/1.0 mm at 30 mm depth, allowing the observation of small cysts (~0.3 cm) in the breast. This research develops a first-of-its-kind ultrasound technology for breast tissue scanning and imaging that offers a noninvasive method for tracking real-time dynamic changes of soft tissue.
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Affiliation(s)
- Wenya Du
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Lin Zhang
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Emma Suh
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Dabin Lin
- School of Opto-electronical Engineering, Xi’an Technological University, Xi’an 710021, China
| | - Colin Marcus
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Lara Ozkan
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Avani Ahuja
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Sara Fernandez
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | | | - David Sadat
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Weiguo Liu
- School of Opto-electronical Engineering, Xi’an Technological University, Xi’an 710021, China
| | - Fei Li
- Electronic Materials Research Laboratory, School of Electronic Science and Engineering, Xi'an Jiaotong University, Xi'an 710049, China
| | - Anantha P. Chandrakasan
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Tolga Ozmen
- Division of Surgical Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Canan Dagdeviren
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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