This paper gives an overview of the methods developed for tissue motion estimation using transverse oscillation images (TO). TO images are specific radiofrequency ultrasound images featuring oscillations in both spatial directions. The initial studies on TO were published in the late 1990s. This paper reviews the main ideas and applications behind this motion estimation approach. First the origin and motivation of TO is briefly reviewed. Then the beamforming methods that lead to TO images are given, detailing the receive-only approach and the transmit-and-receive approach using synthetic aperture data. The different medical applications where TO has been used are discussed (blood flow, elastography and echocardiography), showing how it can improve motion estimation. Finally, the future perspectives of TO are outlined.
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http://dx.doi.org/10.1016/j.ultras.2009.11.001 | DOI Listing |
J Biomech
January 2025
Department of Orthopedic Surgery and Orthopedic Research Institute, West China Hospital, Sichuan University Chengdu Sichuan Province China. Electronic address:
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Beijing Aerospace Automatic Control Institute, Beijing 100854, China.
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January 2025
Department of Electrical and Information Engineering, Kiel University, 24143 Kiel, Germany.
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January 2025
Department of Biomechanical Engineering, Vilnius Gediminas Technical University, LT-10223 Vilnius, Lithuania.
This study includes musculoskeletal disorder (MSD) risk evaluation based on the IMU sensor data gathered from patient-lifting movement performed by healthcare specialists. This is a continuation of previous research focusing on a novel multicriteria statistical model integrating experimental and large-scale statistical datasets. The proposed model estimates MSD probabilities over 5, 10, and 15 years for the neck (0.
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Faculty of Land and Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China.
Scarce feature points are a critical limitation affecting the accuracy and stability of incremental structure from motion (SfM) in small-scale scenes. In this paper, we propose an incremental SfM method for small-scale scenes, combined with an auxiliary calibration plate. This approach increases the number of feature points in sparse regions, and we randomly generate feature points within those areas.
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