Robot-Assisted Optical Ultrasound Scanning

Dwyer, George; Alles, Erwin J.; Colchester, Richard J.; Iyengar, Keshav; Desjardins, Adrien E.; Stoyanov, Danail

doi:10.1109/tmrb.2021.3118293

Cited by 2 publications

(2 citation statements)

References 28 publications

(40 reference statements)

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“…By fabricating these coatings and interferometers on the tips of optical fibres, highly miniaturized imaging devices can be fabricated. These devices, which typically comprise two optical fibres, one for generation and one for reception, are well-suited to integration into medical devices, such as catheters and needles [6,7].…”

Section: Introductionmentioning

confidence: 99%

Single optical fibre ultrasound transducer for rotational imaging

Colchester,

Zhang,

Beard

et al. 2023

Biomedical Imaging and Sensing Conference

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All-optical ultrasound imaging has emerged as paradigm that is well-suited to highly miniaturized applications. With this technique, ultrasound is both generated and received using light, allowing the use of optical fibres for device fabrication. Ultrasound generation typically requires multimode light delivery to provide the pulse energies needed for high pressure generation. Whilst ultrasound reception typically requires single mode light delivery to provide high sensitivity. This means that devices have typically comprised two optical fibres, one multimode for ultrasound generation and one single mode for ultrasound reception. Recently, we demonstrated an all-optical ultrasound imaging device based on a single dual-clad optical fibre. In this work, we build on our previous study and demonstrate rotational imaging with this device. Two copper wire phantoms were imaged, and the wire were well-resolved, demonstrating a signal-to-noise >20 dB. The presented device and system are well-suited for use in minimally invasive and intravascular imaging applications and future work will focus on translating the device.

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Section: Introductionmentioning

confidence: 99%

Single optical fibre ultrasound transducer for rotational imaging

Colchester,

Zhang,

Beard

et al. 2023

Biomedical Imaging and Sensing Conference

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“…1, the overall shape of the system can elongate, bend and twist to navigate through complex and narrow environments encountered in medicine, such as blood vessels, bronchi or other cavities in the body [7]. Furthermore, the working channel can be exploited for optical imaging and ultrasound [8].…”

Section: Introductionmentioning

confidence: 99%

Sim2Real Transfer of Reinforcement Learning for Concentric Tube Robots

Iyengar

Sadati

Bergeles

et al. 2023

IEEE Robot. Autom. Lett.

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Concentric Tube Robots (CTRs) are promising for minimally invasive interventions due to their miniature diameter, high dexterity, and compliance with soft tissue. CTRs comprise individual pre-curved tubes usually composed of NiTi and are arranged concentrically. As each tube is relatively rotated and translated, the backbone elongates, twists, and bends with a dexterity that is advantageous for confined spaces. Tube interactions, unmodelled phenomena, and inaccurate tube parameter estimation make physical modeling of CTRs challenging, complicating in turn kinematics and control. Deep reinforcement learning (RL) has been investigated as a solution. However, hardware validation has remained a challenge due to differences between the simulation and hardware domains. With simulation-only data, in this work, domain randomization is proposed as a strategy for translation to hardware of a simulation policy with no additionally acquired physical training data. The differences in simulation and hardware forward kinematics accuracy and precision are characterized by errors of 14.74±8.87 mm or 26.61±17.00 % robot length. We showcase that the proposed domain randomization approach reduces errors by 56 % in mean errors as compared to no domain randomization. Furthermore, we demonstrate path following capability in hardware with a line path with resulting errors of 4.37 ± 2.39 mm or 5.61 ± 3.11 % robot length.

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Robot-Assisted Optical Ultrasound Scanning

Cited by 2 publications

References 28 publications

Single optical fibre ultrasound transducer for rotational imaging

Single optical fibre ultrasound transducer for rotational imaging

Sim2Real Transfer of Reinforcement Learning for Concentric Tube Robots

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