Catheters and guidewires are increasingly used to navigate tortuous paths offering minimal invasive access to deeply seated locations in the body. Steering these instruments is highly challenging among others due to poor awareness of the configuration such instrument takes on in the body. To address this difficulty research in physical intelligence has been conducted. The aim is to delegate part of the control problem locally and have the instrument determine itself how to effectively interact with its physical environment. To enable such smart behaviour this paper presents a compact FBG (fiber Bragg grating) based drive system for controlling the bending of the distal tip of a steerable catheter. The design process establishes key constraints for selecting an appropriate FBG fiber based on the selected backbone’s characteristics. Force estimation is done using the strain measured via the fiber with a root mean square error (RMSE) of 0.05 N which is then used to train a Long Short-Term Memory (LSTM) network to detect possible contact with the surroundings using the force prediction of the trained model. The trained model was able to predict the force with an RMSE of 0.012 N in a non-contact scenario. The results indicate that the proposed system incorporating FBG sensing, pneumatic artificial muscle (PAM) actuation, and LSTM based contact detection offers a promising pathway for more precise and versatile catheter manipulation in minimally invasive interventions.

Endovascular intervention is a minimally invasive method for treating cardiovascular diseases. Although fluoroscopy, known for real-time catheter visualization, is commonly used, it exposes patients and physicians to ionizing radiation and lacks depth perception due to its 2D nature. To address these limitations, a study was conducted using teleoperation and 3D visualization techniques. This in-vitro study involved the use of a robotic catheter system and aimed to evaluate user performance through both subjective and objective measures. The focus was on determining the most effective modes of interaction. Three interactive modes for guiding robotic catheters were compared in the study: 1) Mode GM, using a gamepad for control and a standard 2D monitor for visual feedback; 2) Mode GH, with a gamepad for control and HoloLens providing 3D visualization; and 3) Mode HH, where HoloLens serves as both control input and visualization device. Mode GH outperformed other modalities in subjective metrics, except for mental demand. It exhibited a median tracking error of 4.72 mm, a median targeting error of 1.01 mm, a median duration of 82.34 s, and a median natural logarithm of dimensionless squared jerk of 40.38 in the in-vitro study. Mode GH showed 8.5%, 4.7%, 6.5%, and 3.9% improvements over Mode GM and 1.5%, 33.6%, 34.9%, and 8.1% over Mode HH for tracking error, targeting error, duration, and dimensionless squared jerk, respectively. To sum up, the user study emphasizes the potential benefits of employing HoloLens for enhanced 3D visualization in catheterization. The user study also illustrates the advantages of using a gamepad for catheter teleoperation, including user-friendliness and passive haptic feedback, compared to HoloLens. To further gauge the potential of using a more traditional joystick as a control input device, an additional study utilizing the Haption Virtuose robot was conducted. It reveals the potential for achieving smoother trajectories, with a 38.9% reduction in total path length compared to a gamepad, potentially due to its larger range of motion and single-handed control.

In this article, a deep learning method for the shape sensing of continuum robots based on multicore fiber bragg grating (FBG) fiber is introduced. The proposed method, based on an artificial neural network (ANN), differs from traditional approaches, where accurate shape reconstruction requires a tedious characterization of many characteristic parameters. A further limitation of traditional approaches is that they require either multiple fibers, whose location relative to the centerline must be precisely known (calibrated), or a single multicore fiber whose position typically coincides with the neutral line. The proposed method addresses this limitation and, thus, allows shape sensing based on a single multicore fiber placed off-center. This helps in miniaturizing and leaves the central channel available for other purposes. The proposed approach was compared to a recent state-of-the-art model-based shape sensing approach. A two-degree-of-freedom benchtop fluidics-driven catheter system was built to validate the proposed ANN. The proposed ANN-based shape sensing approach was evaluated on a 40-mm-long steerable continuum robot in both 3-D free-space and 2-D constrained environments, yielding an average shape sensing error of 0.24 and 0.49 mm, respectively. With these results, the superiority of the proposed approach compared to the recent model-based shape sensing method was demonstrated.

Minimally invasive surgery is now a well established field in surgery but continuous efforts are made to reduce invasiveness even further. This paper proposes a novel concept of small-diameter multi-arm robot for SinglePort Access Surgery. The concept introduces a combination of backbone and actuation principles in a macro-micro fashion to achieve an excellent decoupling of the triangulation platform (macro) and of the end-effectors (micro). Concentric tube robots are used for the triangulation platform, while compliant fluidic-actuated bending segments are used as end-effectors. The fluidic actuation is advantageous as it minimally interferes with the triangulation platform. The triangulation platform on the other hand provides a stable base for the end-effectors such that large distal actuation bandwidth can be achieved. A specific embodiment for Spina Bifida repair is developed and proposed. The surgical and technical requirements as well as the mechanical design are presented in details. A first prototype is built and characterization experiments are conducted to evaluate its performance.

This paper deals with an OCT (optical coherence tomography) geometric calibration method. OCT medical imaging system has received a growing interest during the last two decades. In medical purposes, OCT images are generally called optical biopsies which allows in-vivo investigation almost similar to a histopathological study. The physician can rely on the OCT images to establish a rapid and direct diagnosis. But the OCT images formation suffered numerous distortions due in particular to the optical path, from the source to the viewed sample passing through the two reflecting mirrors and a scan objective. The obtained optical biopsies include several spectral and geometric distortions. The proposed calibration model aims to compensate the geometrical ones. More precisely, two models were developed allowing the correction of both 2D images (B-Scan slices) and 3D images (volume). These models were experimentally validated (in both artificial and biological samples) using a spectral domain OCT system. It has demonstrated a significant enhancement of the OCT images accuracy.

At the moment, surgeons struggle curing a widespread eye disease known as retinal vein occlusion where clots obstruct the retinal vessels. Latter vascular disorder involves black spots in people's eyesight and lead eventually to blindness. A recent promising treatment consists in flushing a thrombolytic agent inside the clotted retinal vessels. The surgery implies puncturing vessels ranging from 50 to 400 microns diameter on the backside of the eye, namely the retina. Latest research succeeded in tackling several challenges around this operation: the surgeon's hand tremor and the high precision required amongst other requirements. Despite several breakthroughs, the surgeon only relies on a microscope to perform the surgery through the patient eye's lens, giving poor depth perception to properly puncture the retinal vessels. This way, the surgeon is most likely to pierce through the vessel and inject the thrombolytic drug under the retina, which would endanger the person's eyesight. In this paper, we investigate the use of a novel bio-impedance sensor developed for eye surgery. Together with this new sensor, a detection algorithm has been developed to detect the puncture and double puncture events to give a feedback to the operator of the system. As far as we are aware of, such technology doesn't exist yet in eye surgery to tackle the depth perception question. This paper aims at demonstrating the benefits of this technology.

Retinal Vein Occlusion is a common retinal vascular disorder which can cause severe loss of vision. Retinal vein cannulation followed by injection of an anti-coagulant into the affected vein is a promising treatment. However, given the scale and fragility of the surgical workfield, this procedure is considered too high-risk to perform manually. A first successful robot-assisted procedure has been demonstrated. Even though successful, the procedure remains extremely challenging. This paper aims at providing a solution for the limited perception of instrument-tissue interaction forces as well as depth estimation during retinal vein cannulation. The development of a novel combined force and distance sensing cannulation needle relying on Fiber Bragg grating (FBG) and Optical Coherence Tomography (OCT) A-scan technology is reported. The design, the manufacturing process, the calibration method, and the experimental characterization of the produced sensor are discussed. The functionality of the combined sensing modalities and the real-time distance estimation algorithm are validated respectively on in-vitro and ex-vivo models.

This paper proposes the design of a vision-guided control law for microrobotic-assisted biomedical applications. More precisely, the developed control law is to servo an optical coherence tomography (OCT) system in real-time to carry out repetitive optical biopsy tasks. The OCT images are simultaneously used to perform the optical biopsy and to control the sample holder (microrobotic work-flow). Instead of extracting visual features from the OCT images, the vision-based controller uses the concept of frequency domain information to compute relative motion between two successive OCT B-scan (cross-section) images. Therefore, the visual controller is designed to minimize the error between current and desired images by controlling the microrobotic platform. The proposed approach was experimentally validated, demonstrating more than satisfactory results especially in terms of accuracy, convergence, and robustness.

This paper presents the development of a new visual servoing control law using global information image. It consists in the use of the wavelet spatial coefficients as the visual signal to design visual controller. More precisely, we introduce the concept of multiple resolution interaction matrix that links the time-variation of wavelet coefficients to the robot spatial velocity and the associated task function controller. The proposed controller was successfully validated using a 6 degrees-of-freedom 3PPSR parallel kinematic manipulator in an eye-to-hand configuration. Experimental results show the accuracy, efficiency and show good behavior of such features.

This paper addresses the design of a partitioned vision-guided scheme for repetitive optical biopsies. More precisely, our approach uses two image modalities to perform 6 degrees of freedom (DOF) positioning task. The development aims to partition the control into 3 DOF controlled by the B-scan images acquired with an optical coherence tomography (OCT) system and the remaining 3 DOF controlled by the white light images provided by a CCD camera. Moreover, for the control and instead of conventional visual features (e.g., points, lines, moments, etc.) extracted using algorithms combined with visual tracking approaches, our visual servoing method uses the multiresolution wavelet coefficients. The developed method was experimentally validated using a parallel kinematic structure equipped with a Telesto-II OCT benchtop. The validation task consisted of an automatic spatial repositioning of the robotic structure to precisely retrieve the position of an initial optical biopsy. Several tests are achieved, which clearly demonstrate the reliability of the proposed controller.

This paper deals with mutual information-based numerical and physical registration of white light images vs. fluorescence images for microrobotic laser microphonosurgery of the vocal folds. More precisely, it presents two techniques: a numerical registration of multimodal images and a vision feedback control for positioning an endoscope with regards to a preoperative image (fluorescence image). Nelder-Mead Simplex for nonlinear optimization is used to minimize the cost-function. The proposed methods are successfully validated in an experimental set-up using preoperative fluorescence images and real-time white light images of the vocal folds.