This study aims to validate the advantage of the new engineering method to maneuver multi-section robotic bronchoscope with first person view control in transbronchial biopsy. Six physician operators were recruited and tasked to operate a manual and a robotic bronchoscope to the peripheral area placed in patient-derived lung phantoms. The metrics collected were the furthest generation count of the airway the bronchoscope reached, force incurred to the phantoms, and NASA-Task Load Index. The furthest generation count of the airway the physicians reached using the manual and the robotic bronchoscopes were 6.6 +/- 1.2th and 6.7 +/- 0.8th. Robotic bronchoscopes successfully reached the 5th generation count into the peripheral area of the airway, while the manual bronchoscope typically failed earlier in the 3rd generation. More force was incurred to the airway when the manual bronchoscope was used (0.24 +/- 0.20 [N]) than the robotic bronchoscope was applied (0.18 +/- 0.22 [N], p<0.05). The manual bronchoscope imposed more physical demand than the robotic bronchoscope by NASA-TLX score (55 +/- 24 vs 19 +/- 16, p<0.05). These results indicate that a robotic bronchoscope facilitates the advancement of the bronchoscope to the peripheral area with less physical demand to physician operators. The metrics collected in this study would expect to be used as a benchmark for the future development of robotic bronchoscopes.

Navigated bronchoscopy for the lung biopsy using an electro-magnetic (EM) sensor is often inaccurate due to patient breathing movement during procedures. The objective of this study is to evaluate whether registration of neural network- generated depth images can localize the bronchoscope in navigated bronchoscopy negating the need for EM sensor and error caused by breathing motion. [Methods] Dual CNN-generated depth images followed chained ICP registration were validated in the study. Accuracy was measured by the error between the location after registration and the location of the standard electromagnetic sensor. Difference in accuracy between regions that the neural networks had trained on (seen regions) and regions the networks had never encountered (unseen regions) was validated. [Results] The data collected points to the success of the bronchoscopic localization. Overall mean error of accuracy was 8.75 mm and the overall standard deviation was 4.76mm. For the seen region, the mean error was 6.10mm and the standard deviation was 2.65mm. For the unseen region, the mean error was 11.6mm and the standard deviation was 4.87mm. The results of the two-sample t-test shows that there is a statistically significant difference between the unseen and the seen region. [Conclusion] The results for registration demonstrate that this technique has potential to be implemented in navigational bronchoscopy. The technique produced less error than the electromagnetic sensor in practice, especially accounting for the estimated practical error due to experimental setup.

Current standard workflows of ultrasound (US)-guided needle insertion require physicians to use their both hands: holding the US probe to locate interested areas with the non-dominant hand and the needle with the dominant hand. This is due to the separation of functionalities for localization and needle insertion. This requirement does not only make the procedure cumbersome, but also limits the reliability of guidance given that the positional relationship between the needle and US images is unknown and interpreted with their experience and assumption. Although the US-guided needle insertion may be assisted through navigation systems, recovery of the positional relationship between the needle and US images requires the usage of external tracking systems and image-based tracking algorisms that may involve the registration inaccuracy. Therefore, there is an unmet need for the solution that provides a simple and intuitive needle localization and insertion to improve the conventional US-guided procedure. In this work, we propose a new device concept solution based on the ring-arrayed forward-viewing (RAF) ultrasound imaging system. The proposed system is comprised with ring-arrayed transducers and an open whole inside the ring where the needle can be inserted. The ring array provides forward-viewing US images, where the needle path is always maintained at the center of the reconstructed image without requiring any registration. As the proof of concept, we designed single-circle ring-arrayed configurations with different radiuses and visualized point targets using the forward-viewing US imaging through simulations and phantom experiments. The results demonstrated the successful target visualization and indicates the ring-arrayed US imaging has a potential to improve the US-guided needle insertion procedure to be simpler and more intuitive.

PURPOSE: Magnetic resonance imaging (MRI)-guided percutaneous cryotherapy of abdominal lesions, an established procedure, uses MRI to guide and monitor the cryoablation of lesions. Methods to precisely guide cryotherapy probes with a minimum amount of trial-and-error are yet to be established. To aid physicians in attaining precise probe alignment without trial-and-error, a body-mounted motorized cryotherapy-probe alignment device (BMCPAD) with motion compensation was clinically tested in this study. The study also compared the contribution of body motion and organ motion compensation to the guidance accuracy of a body-mounted probe alignment device. METHODS: The accuracy of guidance using the BMCPAD was prospectively measured during MRI-guided percutaneous cryotherapies before insertion of the probes. Clinical parameters including patient age, types of anesthesia, depths of the target, and organ sites of target were collected. By using MR images of the target organs and fiducial markers embedded in the BMCPAD, we retrospectively simulated the guidance accuracy with body motion compensation, organ motion compensation, and no compensation. The collected data were analyzed to test the impact of motion compensation on the guidance accuracy. RESULTS: Thirty-seven physical guidance of probes were prospectively recorded for sixteen completed cases. The accuracy of physical guidance using the BMCPAD was 13.4 ± 11.1 mm. The simulated accuracy of guidance with body motion compensation, organ motion compensation, and no compensation was 2.4 ± 2.9 mm, 2.2 ± 1.6 mm, and 3.5 ± 2.9 mm, respectively. Data analysis revealed that the body motion compensation and organ motion compensation individually impacted the improvement in the accuracy of simulated guidance. Moreover, the difference in the accuracy of guidance either by body motion compensation or organ motion compensation was not statistically significant. The major clinical parameters impacting the accuracy of guidance were the body and organ motions. Patient age, types of anesthesia, depths of the target, and organ sites of target did not influence the accuracy of guidance using BMCPAD. The magnitude of body surface movement and organ movement exhibited mutual statistical correlation. CONCLUSIONS: The BMCPAD demonstrated guidance accuracy comparable to that of previously reported devices for CT-guided procedures. The analysis using simulated motion compensation revealed that body motion compensation and organ motion compensation individually impact the improvement in the accuracy of device-guided cryotherapy probe alignment. Considering the correlation between body and organ movements, we also determined that body motion compensation using the ring fiducial markers in the BMCPAD can be solely used to address both body and organ motions in MRI-guided cryotherapy.

BACKGROUND: In a combined endoscopic third ventriculostomy (ETV) and endoscopic tumor biopsy (ETB) procedure, an optimal tool trajectory is mandatory to minimize trauma to surrounding cerebral tissue. OBJECTIVE: This paper presents wire-driven multi-section robot with push-pull wire. The robot is tested to attain follow-the-leader (FTL) motion to place surgical instruments through narrow passages while minimizing the trauma to tissues. METHODS: A wire-driven continuum robot with six sub-sections was developed and its kinematic model was proposed to achieve FTL motion. An accuracy test to assess the robot's ability to attain FTL motion along a set of elementary curved trajectory was performed. We also used hydrocephalus ventricular model created from human subject data to generate five ETV/ETB trajectories and conducted a study assessing the accuracy of the FTL motion along these clinically desirable trajectories. RESULTS: In the test with elementary curved paths, the maximal deviation of the robot was increased from 0.47 mm at 30 turn to 1.78 mm at 180 in a simple C-shaped curve. S-shaped FTL motion had lesser deviation ranging from 0.16 to 0.18 mm. In the phantom study, the greatest tip deviation was 1.45 mm, and the greatest path deviation was 1.23 mm. CONCLUSION: We present the application of a continuum robot with FTL motion to perform a combined ETV/ETB procedure. The validation study using human subject data indicated that the accuracy of FTL motion is relatively high. The study indicated that FTL motion may be useful tool for combined ETV and ETB.