74 research outputs found
Autonomous Robotic Screening of Tubular Structures based only on Real-Time Ultrasound Imaging Feedback
Ultrasound (US) imaging is widely employed for diagnosis and staging of
peripheral vascular diseases (PVD), mainly due to its high availability and the
fact it does not emit radiation. However, high inter-operator variability and a
lack of repeatability of US image acquisition hinder the implementation of
extensive screening programs. To address this challenge, we propose an
end-to-end workflow for automatic robotic US screening of tubular structures
using only the real-time US imaging feedback. We first train a U-Net for
real-time segmentation of the vascular structure from cross-sectional US
images. Then, we represent the detected vascular structure as a 3D point cloud
and use it to estimate the longitudinal axis of the target tubular structure
and its mean radius by solving a constrained non-linear optimization problem.
Iterating the previous processes, the US probe is automatically aligned to the
orientation normal to the target tubular tissue and adjusted online to center
the tracked tissue based on the spatial calibration. The real-time segmentation
result is evaluated both on a phantom and in-vivo on brachial arteries of
volunteers. In addition, the whole process is validated both in simulation and
physical phantoms. The mean absolute radius error and orientation error (
SD) in the simulation are and ,
respectively. On a gel phantom, these errors are and
. This shows that the method is able to automatically screen
tubular tissues with an optimal probe orientation (i.e. normal to the vessel)
and at the same to accurately estimate the mean radius, both in real-time.Comment: Accepted for publication in IEEE Transactions on Industrial
Electronics Video: https://www.youtube.com/watch?v=VAaNZL0I5i
Robot-Assisted Image-Guided Interventions
Image guidance is a common methodology of minimally invasive procedures. Depending on the type of intervention, various imaging modalities are available. Common imaging modalities are computed tomography, magnetic resonance tomography, and ultrasound. Robotic systems have been developed to enable and improve the procedures using these imaging techniques. Spatial and technological constraints limit the development of versatile robotic systems. This paper offers a brief overview of the developments of robotic systems for image-guided interventions since 2015 and includes samples of our current research in this field
Robot-Assisted Image-Guided Interventions
Image guidance is a common methodology of minimally invasive procedures. Depending
on the type of intervention, various imaging modalities are available. Common imaging
modalities are computed tomography, magnetic resonance tomography, and ultrasound.
Robotic systems have been developed to enable and improve the procedures using these
imaging techniques. Spatial and technological constraints limit the development of
versatile robotic systems. This paper offers a brief overview of the developments of
robotic systems for image-guided interventions since 2015 and includes samples of our
current research in this field
Robotic Ultrasound Imaging: State-of-the-Art and Future Perspectives
Ultrasound (US) is one of the most widely used modalities for clinical
intervention and diagnosis due to the merits of providing non-invasive,
radiation-free, and real-time images. However, free-hand US examinations are
highly operator-dependent. Robotic US System (RUSS) aims at overcoming this
shortcoming by offering reproducibility, while also aiming at improving
dexterity, and intelligent anatomy and disease-aware imaging. In addition to
enhancing diagnostic outcomes, RUSS also holds the potential to provide medical
interventions for populations suffering from the shortage of experienced
sonographers. In this paper, we categorize RUSS as teleoperated or autonomous.
Regarding teleoperated RUSS, we summarize their technical developments, and
clinical evaluations, respectively. This survey then focuses on the review of
recent work on autonomous robotic US imaging. We demonstrate that machine
learning and artificial intelligence present the key techniques, which enable
intelligent patient and process-specific, motion and deformation-aware robotic
image acquisition. We also show that the research on artificial intelligence
for autonomous RUSS has directed the research community toward understanding
and modeling expert sonographers' semantic reasoning and action. Here, we call
this process, the recovery of the "language of sonography". This side result of
research on autonomous robotic US acquisitions could be considered as valuable
and essential as the progress made in the robotic US examination itself. This
article will provide both engineers and clinicians with a comprehensive
understanding of RUSS by surveying underlying techniques.Comment: Accepted by Medical Image Analysi
Toward Fully Automated Robotic Platform for Remote Auscultation
Since most developed countries are facing an increase in the number of
patients per healthcare worker due to a declining birth rate and an aging
population, relatively simple and safe diagnosis tasks may need to be performed
using robotics and automation technologies, without specialists and hospitals.
This study presents an automated robotic platform for remote auscultation,
which is a highly cost-effective screening tool for detecting abnormal clinical
signs. The developed robotic platform is composed of a 6-degree-of-freedom
cooperative robotic arm, light detection and ranging (LiDAR) camera, and a
spring-based mechanism holding an electric stethoscope. The platform enables
autonomous stethoscope positioning based on external body information acquired
using the LiDAR camera-based multi-way registration; the platform also ensures
safe and flexible contact, maintaining the contact force within a certain range
through the passive mechanism. Our preliminary results confirm that the robotic
platform enables estimation of the landing positions required for cardiac
examinations based on the depth and landmark information of the body surface.
It also handles the stethoscope while maintaining the contact force without
relying on the push-in displacement by the robotic arm.Comment: 8 pages, 11 figure
Autonomous Tissue Scanning under Free-Form Motion for Intraoperative Tissue Characterisation
In Minimally Invasive Surgery (MIS), tissue scanning with imaging probes is
required for subsurface visualisation to characterise the state of the tissue.
However, scanning of large tissue surfaces in the presence of deformation is a
challenging task for the surgeon. Recently, robot-assisted local tissue
scanning has been investigated for motion stabilisation of imaging probes to
facilitate the capturing of good quality images and reduce the surgeon's
cognitive load. Nonetheless, these approaches require the tissue surface to be
static or deform with periodic motion. To eliminate these assumptions, we
propose a visual servoing framework for autonomous tissue scanning, able to
deal with free-form tissue deformation. The 3D structure of the surgical scene
is recovered and a feature-based method is proposed to estimate the motion of
the tissue in real-time. A desired scanning trajectory is manually defined on a
reference frame and continuously updated using projective geometry to follow
the tissue motion and control the movement of the robotic arm. The advantage of
the proposed method is that it does not require the learning of the tissue
motion prior to scanning and can deal with free-form deformation. We deployed
this framework on the da Vinci surgical robot using the da Vinci Research Kit
(dVRK) for Ultrasound tissue scanning. Since the framework does not rely on
information from the Ultrasound data, it can be easily extended to other
probe-based imaging modalities.Comment: 7 pages, 5 figures, ICRA 202
Learning Robotic Ultrasound Scanning Skills via Human Demonstrations and Guided Explorations
Medical ultrasound has become a routine examination approach nowadays and is
widely adopted for different medical applications, so it is desired to have a
robotic ultrasound system to perform the ultrasound scanning autonomously.
However, the ultrasound scanning skill is considerably complex, which highly
depends on the experience of the ultrasound physician. In this paper, we
propose a learning-based approach to learn the robotic ultrasound scanning
skills from human demonstrations. First, the robotic ultrasound scanning skill
is encapsulated into a high-dimensional multi-modal model, which takes the
ultrasound images, the pose/position of the probe and the contact force into
account. Second, we leverage the power of imitation learning to train the
multi-modal model with the training data collected from the demonstrations of
experienced ultrasound physicians. Finally, a post-optimization procedure with
guided explorations is proposed to further improve the performance of the
learned model. Robotic experiments are conducted to validate the advantages of
our proposed framework and the learned models
A Passive Variable Impedance Control Strategy with Viscoelastic Parameters Estimation of Soft Tissues for Safe Ultrasonography
In the context of telehealth, robotic approaches have proven a valuable
solution to in-person visits in remote areas, with decreased costs for patients
and infection risks. In particular, in ultrasonography, robots have the
potential to reproduce the skills required to acquire high-quality images while
reducing the sonographer's physical efforts. In this paper, we address the
control of the interaction of the probe with the patient's body, a critical
aspect of ensuring safe and effective ultrasonography. We introduce a novel
approach based on variable impedance control, allowing real-time optimisation
of a compliant controller parameters during ultrasound procedures. This
optimisation is formulated as a quadratic programming problem and incorporates
physical constraints derived from viscoelastic parameter estimations. Safety
and passivity constraints, including an energy tank, are also integrated to
minimise potential risks during human-robot interaction. The proposed method's
efficacy is demonstrated through experiments on a patient dummy torso,
highlighting its potential for achieving safe behaviour and accurate force
control during ultrasound procedures, even in cases of contact loss.Comment: 7 pages, 7 figures, submitted to ICRA 202
Learning Ultrasound Scanning Skills from Human Demonstrations
Recently, the robotic ultrasound system has become an emerging topic owing to
the widespread use of medical ultrasound. However, it is still a challenging
task to model and to transfer the ultrasound skill from an ultrasound
physician. In this paper, we propose a learning-based framework to acquire
ultrasound scanning skills from human demonstrations. First, the ultrasound
scanning skills are encapsulated into a high-dimensional multi-modal model in
terms of interactions among ultrasound images, the probe pose and the contact
force. The parameters of the model are learned using the data collected from
skilled sonographers' demonstrations. Second, a sampling-based strategy is
proposed with the learned model to adjust the extracorporeal ultrasound
scanning process to guide a newbie sonographer or a robot arm. Finally, the
robustness of the proposed framework is validated with the experiments on real
data from sonographers
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