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Towards Autonomous Robotic Minimally Invasive Ultrasound Scanning and Vessel Reconstruction on Non-Planar Surfaces

Autonomous robotic Ultrasound (US) scanning has been the subject of research for more than 2 decades. However, little work has been done to apply this concept into a minimally invasive setting, in which accurate force sensing is generally not available and robot kinematics are unreliable due to the...

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Autores principales: Marahrens, Nils, Scaglioni, Bruno, Jones, Dominic, Prasad, Raj, Biyani, Chandra Shekhar, Valdastri, Pietro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9594548/
https://www.ncbi.nlm.nih.gov/pubmed/36304794
http://dx.doi.org/10.3389/frobt.2022.940062
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author Marahrens, Nils
Scaglioni, Bruno
Jones, Dominic
Prasad, Raj
Biyani, Chandra Shekhar
Valdastri, Pietro
author_facet Marahrens, Nils
Scaglioni, Bruno
Jones, Dominic
Prasad, Raj
Biyani, Chandra Shekhar
Valdastri, Pietro
author_sort Marahrens, Nils
collection PubMed
description Autonomous robotic Ultrasound (US) scanning has been the subject of research for more than 2 decades. However, little work has been done to apply this concept into a minimally invasive setting, in which accurate force sensing is generally not available and robot kinematics are unreliable due to the tendon-driven, compliant robot structure. As a result, the adequate orientation of the probe towards the tissue surface remains unknown and the anatomy reconstructed from scan may become highly inaccurate. In this work we present solutions to both of these challenges: an attitude sensor fusion scheme for improved kinematic sensing and a visual, deep learning based algorithm to establish and maintain contact between the organ surface and the US probe. We further introduce a novel scheme to estimate and orient the probe perpendicular to the center line of a vascular structure. Our approach enables, for the first time, to autonomously scan across a non-planar surface and navigate along an anatomical structure with a robotically guided minimally invasive US probe. Our experiments on a vessel phantom with a convex surface confirm a significant improvement of the reconstructed curved vessel geometry, with our approach strongly reducing the mean positional error and variance. In the future, our approach could help identify vascular structures more effectively and help pave the way towards semi-autonomous assistance during partial hepatectomy and the potential to reduce procedure length and complication rates.
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spelling pubmed-95945482022-10-26 Towards Autonomous Robotic Minimally Invasive Ultrasound Scanning and Vessel Reconstruction on Non-Planar Surfaces Marahrens, Nils Scaglioni, Bruno Jones, Dominic Prasad, Raj Biyani, Chandra Shekhar Valdastri, Pietro Front Robot AI Robotics and AI Autonomous robotic Ultrasound (US) scanning has been the subject of research for more than 2 decades. However, little work has been done to apply this concept into a minimally invasive setting, in which accurate force sensing is generally not available and robot kinematics are unreliable due to the tendon-driven, compliant robot structure. As a result, the adequate orientation of the probe towards the tissue surface remains unknown and the anatomy reconstructed from scan may become highly inaccurate. In this work we present solutions to both of these challenges: an attitude sensor fusion scheme for improved kinematic sensing and a visual, deep learning based algorithm to establish and maintain contact between the organ surface and the US probe. We further introduce a novel scheme to estimate and orient the probe perpendicular to the center line of a vascular structure. Our approach enables, for the first time, to autonomously scan across a non-planar surface and navigate along an anatomical structure with a robotically guided minimally invasive US probe. Our experiments on a vessel phantom with a convex surface confirm a significant improvement of the reconstructed curved vessel geometry, with our approach strongly reducing the mean positional error and variance. In the future, our approach could help identify vascular structures more effectively and help pave the way towards semi-autonomous assistance during partial hepatectomy and the potential to reduce procedure length and complication rates. Frontiers Media S.A. 2022-10-11 /pmc/articles/PMC9594548/ /pubmed/36304794 http://dx.doi.org/10.3389/frobt.2022.940062 Text en Copyright © 2022 Marahrens, Scaglioni, Jones, Prasad, Biyani and Valdastri. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Robotics and AI
Marahrens, Nils
Scaglioni, Bruno
Jones, Dominic
Prasad, Raj
Biyani, Chandra Shekhar
Valdastri, Pietro
Towards Autonomous Robotic Minimally Invasive Ultrasound Scanning and Vessel Reconstruction on Non-Planar Surfaces
title Towards Autonomous Robotic Minimally Invasive Ultrasound Scanning and Vessel Reconstruction on Non-Planar Surfaces
title_full Towards Autonomous Robotic Minimally Invasive Ultrasound Scanning and Vessel Reconstruction on Non-Planar Surfaces
title_fullStr Towards Autonomous Robotic Minimally Invasive Ultrasound Scanning and Vessel Reconstruction on Non-Planar Surfaces
title_full_unstemmed Towards Autonomous Robotic Minimally Invasive Ultrasound Scanning and Vessel Reconstruction on Non-Planar Surfaces
title_short Towards Autonomous Robotic Minimally Invasive Ultrasound Scanning and Vessel Reconstruction on Non-Planar Surfaces
title_sort towards autonomous robotic minimally invasive ultrasound scanning and vessel reconstruction on non-planar surfaces
topic Robotics and AI
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9594548/
https://www.ncbi.nlm.nih.gov/pubmed/36304794
http://dx.doi.org/10.3389/frobt.2022.940062
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