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Towards Standardized Acquisition with a Dual-probe Ultrasound Robot for Fetal Imaging.

Authors :
Housden J
Wang S
Bao X
Zheng J
Skelton E
Matthew J
Noh Y
Eltiraifi O
Singh A
Singh D
Rhode K
Source :
IEEE robotics and automation letters [IEEE Robot Autom Lett] 2021 Apr; Vol. 6 (2), pp. 1059-1065. Date of Electronic Publication: 2021 Feb 01.
Publication Year :
2021

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 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.

Details

Language :
English
ISSN :
2377-3766
Volume :
6
Issue :
2
Database :
MEDLINE
Journal :
IEEE robotics and automation letters
Publication Type :
Academic Journal
Accession number :
33912664
Full Text :
https://doi.org/10.1109/LRA.2021.3056033