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Fibre-optic based time-resolved fluorescence spectroscopy (TRFS) is an advanced spectroscopy technique that generates sample-specific spectral-temporal signature, characterising variations in fluorescence in real-time. As such, it can be used to interrogate tissue autofluorescence. Recent advancements in TRFS technology, including the development of devices that simultaneously measure high-resolution spectral and temporal fluorescence, paired with novel analysis methods extracting information from these multidimensional measurements effectively, provide additional insight into the underlying autofluorescence features of a sample. This study demonstrates, using both simulated data and endogenous fluorophores measured bench-side, that the shape of the spectral fluorescence lifetime, or fluorescence lifetimes estimated over high-resolution spectral channels across a broad range, is influenced by the relative abundance of underlying fluorophores in mixed systems and their respective environment. This study, furthermore, explores the properties of the spectral fluorescence lifetime in paired lung tissue deemed either abnormal or normal by pathologists. We observe that, on average, the shape of the spectral fluorescence lifetime at multiple locations sampled on 14 abnormal lung tissue, compared to multiple locations sampled on the respective paired normal lung tissue, shows more variability; and, while not statistically significant, the average spectral fluorescence lifetime in abnormal tissue is consistently lower over every wavelength than the normal tissue.

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In this paper, we propose a novel biomechanics-aware robot-assisted steerable drilling framework with the goal of addressing common complications of spinal fixation procedures occurring due to the rigidity of drilling instruments and implants. This framework is composed of two main unique modules to design a robotic system including (i) a Patient-Specific Biomechanics-aware Trajectory Selection Module used to analyze the stress and strain distribution along an implanted pedicle screw in a generic drilling trajectory (linear and/or curved) and obtain an optimal trajectory; and (ii) a complementary semi-autonomous robotic drilling module that consists of a novel Concentric Tube Steerable Drilling Robot (CT-SDR) integrated with a seven degree-of-freedom robotic manipulator. This semi-autonomous robot-assisted steerable drilling system follows a multi-step drilling procedure to accurately and reliably execute the optimal hybrid drilling trajectory (HDT) obtained by the Trajectory Selection Module. Performance of the proposed framework has been thoroughly analyzed on simulated bone materials by drilling various trajectories obtained from the finite element-based Selection Module using Quantitative Computed Tomography (QCT) scans of a real patient's vertebra.

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Spinal fixation with rigid pedicle screws have shown to be an effective treatment for many patients. However, this surgical option has been proved to be insufficient and will eventually fail for patients experiencing osteoporosis. This failure is mainly attributed to the lack of dexterity in the existing rigid drilling instruments and the complex anatomy of vertebrae, forcing surgeons to implant rigid pedicle screws within the osteoporotic regions of anatomy. To address this problem, in this article, we present the design, fabrication, and evaluation of a unique flexible yet structurally strong concentric tube steerable drilling robot (CT-SDR). The CT-SDR is capable of drilling smooth and accurate curved trajectories through hard tissues without experiencing buckling and failure; thus enabling the use of novel flexible pedicle screws for the next generation of spinal fixation procedures. Particularly, by decoupling the control of bending and insertion degrees of freedom (DoF) of the CT-SDR, we present a robotic system that (i) is intuitive to steer as it does not require an on-the-fly control algorithm for the bending DoF, and (ii) is able to address the contradictory requirements of structural stiffness and dexterity of a flexible robot interacting with the hard tissue. The robust and repeatable performance of the proposed CT-SDR have been experimentally evaluated by conducting various drilling procedures on simulated bone materials and animal bone samples. Experimental results indicate drilling times as low as 35 seconds for curved trajectories with 41 mm length and remarkable steering accuracy with a maximum 2% deviation error.

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In this paper, we design and develop a novel robotic bronchoscope for sampling of the distal lung in mechanically-ventilated (MV) patients in critical care units. Despite the high cost and attributable morbidity and mortality of MV patients with pneumonia which approaches 40%, sampling of the distal lung in MV patients suffering from range of lung diseases such as Covid-19 is not standardised, lacks reproducibility and requires expert operators. We propose a robotic bronchoscope that enables repeatable sampling and guidance to distal lung pathologies by overcoming significant challenges that are encountered whilst performing bronchoscopy in MV patients, namely, limited dexterity, large size of the bronchoscope obstructing ventilation, and poor anatomical registration. We have developed a robotic bronchoscope with 7 Degrees of Freedom (DoFs), an outer diameter of 4.5 mm and inner working channel of 2 mm. The prototype is a push/pull actuated continuum robot capable of dexterous manipulation inside the lung and visualisation/sampling of the distal airways. A prototype of the robot is engineered and a mechanics-based model of the robotic bronchoscope is developed. Furthermore, we develop a novel numerical solver that improves the computational efficiency of the model and facilitates the deployment of the robot. Experiments are performed to verify the design and evaluate accuracy and computational cost of the model. Results demonstrate that the model can predict the shape of the robot in <0.011s with a mean error of 1.76 cm, enabling the future deployment of a robotic bronchoscope in MV patients.

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This article presents a medical robotic system for deep orbital interventions, with a focus on Optic Nerve Sheath Fenestration (ONSF). ONSF is a currently invasive ophthalmic surgical approach that can reduce potentially blinding elevated hydrostatic intracranial pressure on the optic disc via an incision on the optic nerve. The prototype is a multi-arm system capable of dexterous manipulation and visualization of the optic nerve area, allowing for a minimally invasive approach. Each arm is an independently controlled concentric tube robot collimated by a bespoke guide that is secured on the eye sclera via sutures. In this article, we consider the robot's end-effector design in order to reach/navigate the optic nerve according to the clinical requirements of ONSF. A prototype of the robot was engineered, and its ability to penetrate the optic nerve was analysed by conducting ex vivo experiments on porcine optic nerves and comparing their stiffness to human ones. The robot was successfully deployed in a custom-made realistic eye phantom. Our simulation studies and experimental results demonstrate that the robot can successfully navigate to the operation site and carry out the intervention.

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Robotic-assisted needle steering can enhance the accuracy of needle-based interventions. Application of current needle steering techniques are restricted by the limited deflection curvature of needles. Here, a novel steerable needle with improved curvature is developed and used with an online motion planner to steer the needle along curved paths inside tissue. The needle is developed by carving series of small notches on the shaft of a standard needle. The notches decrease the needle flexural stiffness, allowing the needle to follow tightly curved paths with small radius of curvature. In this paper, first, a finite element model of the notched needle deflection in tissue is presented. Next, the model is used to estimate the optimal location for the notches on needle's shaft for achieving a desired curvature. Finally, an ultrasound-guided motion planner for needle steering inside tissue is developed and used to demonstrate the capability of the notched needle in achieving high curvature and maneuvering around obstacles in tissue. We simulated a clinical scenario in brachytherapy, where the target is obstructed by the pubic bone and cannot be reached using regular needles. Experimental results show that the target can be reached using the notched needle with a mean accuracy of 1.2 mm. Thus, the proposed needle enables future research on needle steering toward deeper or more difficult-to-reach targets.

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The performance of needle-based interventions depends on the accuracy of needle tip positioning. Here, a novel needle steering strategy is proposed that enhances accuracy of needle steering. In our approach the surgeon is in charge of needle insertion to ensure the safety of operation, while the needle tip bevel location is robotically controlled to minimize the targeting error. The system has two main components: (1) a real-time predictor for estimating future needle deflection as it is steered inside soft tissue, and (2) an online motion planner that calculates control decisions and steers the needle toward the target by iterative optimization of the needle deflection predictions. The predictor uses the ultrasound-based curvature information to estimate the needle deflection. Given the specification of anatomical obstacles and a target from preoperative images, the motion planner uses the deflection predictions to estimate control actions, i.e., the depth(s) at which the needle should be rotated to reach the target. Ex-vivo needle insertions are performed with and without obstacle to validate our approach. The results demonstrate the needle steering strategy guides the needle to the targets with a maximum error of 1.22 mm.

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