Evaluation of User Performance in Simulation-Based Diagnostic Cerebral Angiography Training

Simulation of anatomically complex procedures, such as angiography, is becoming more practical, however, computer-based modules require extensive research to assess their effectiveness. We organized two training schemas – alternating cases and consistent cases – and hypothesized that the alternating practice cases would be beneficial to test performance. Eight residents (4 radiology/4 neurosurgery) and 8 anatomy graduate students were trained on the Simbionix™ simulator in order to assess skill acquisition in diagnostic cerebral angiography over 8 sessions. We found that participants improve on total procedure time and total fluoroscopy time (p\u3c0.05), but not on contrast injected or roadmaps created. There were no significant differences between alternating and consistent training types. Additional work needs to be done with higher sample numbers and visuospatial scores as criteria

Catheter segmentation in X-ray fluoroscopy using synthetic data and transfer learning with light U-nets

Background and objectivesAutomated segmentation and tracking of surgical instruments and catheters under X-ray fluoroscopy hold the potential for enhanced image guidance in catheter-based endovascular procedures. This article presents a novel method for real-time segmentation of catheters and guidewires in 2d X-ray images. We employ Convolutional Neural Networks (CNNs) and propose a transfer learning approach, using synthetic fluoroscopic images, to develop a lightweight version of the U-Net architecture. Our strategy, requiring a small amount of manually annotated data, streamlines the training process and results in a U-Net model, which achieves comparable performance to the state-of-the-art segmentation, with a decreased number of trainable parameters. MethodsThe proposed transfer learning approach exploits high-fidelity synthetic images generated from real fluroscopic backgrounds. We implement a two-stage process, initial end-to-end training and fine-tuning, to develop two versions of our model, using synthetic and phantom fluoroscopic images independently. A small number of manually annotated in-vivo images is employed to fine-tune the deepest 7 layers of the U-Net architecture, producing a network specialized for pixel-wise catheter/guidewire segmentation. The network takes as input a single grayscale image and outputs the segmentation result as a binary mask against the background. ResultsEvaluation is carried out with images from in-vivo fluoroscopic video sequences from six endovascular procedures, with different surgical setups. We validate the effectiveness of developing the U-Net models using synthetic data, in tests where fine-tuning and testing in-vivo takes place both by dividing data from all procedures into independent fine-tuning/testing subsets as well as by using different in-vivo sequences. Accurate catheter/guidewire segmentation (average Dice coefficient of ~ 0.55, ~ 0.26 and ~ 0.17) is obtained with both U-Net models. Compared to the state-of-the-art CNN models, the proposed U-Net achieves comparable performance ( ± 5% average Dice coefficients) in terms of segmentation accuracy, while yielding a 84% reduction of the testing time. This adds flexibility for real-time operation and makes our network adaptable to increased input resolution. ConclusionsThis work presents a new approach in the development of CNN models for pixel-wise segmentation of surgical catheters in X-ray fluoroscopy, exploiting synthetic images and transfer learning. Our methodology reduces the need for manually annotating large volumes of data for training. This represents an important advantage, given that manual pixel-wise annotations is a key bottleneck in developing CNN segmentation models. Combined with a simplified U-Net model, our work yields significant advantages compared to current state-of-the-art solutions

NUMERICAL DESIGN OF STEERABLE GUIDEWIRES

Biomedical devices are an integral part of the medical industry nowadays. With the increase in cases of heart disease, catheterization procedures are becoming more frequent. Small-scale actuators are needed for the guidance of small-scale catheters and guidewires to remote targets in the human body. Numerical modelling is needed to guide the experiments in developing such steerable devices and to optimize their design. Here, we designed small-scale steerable guidewires by first developing bending actuators and then assembling them with guidewires. The actuators use materials with strain response to electric potential in a very low voltage range that is not harmful to the human body. Our work examined the layered strip configuration for the structure of actuators and identified trends to maximize the bending deformations. Using the commercial software Abaqus, we developed a finite element model based on Piezoelectric actuation to simulate various combinations of materials and geometries and to optimize the design of the actuator and the steerable guidewires. We also developed an analytical model for the actuators and showed that the simulation results are in agreement with the analytical model. Parameters like thickness, length, and different geometrical combinations and their effect on bending were compared. This numerical model can be customized for different materials that can be used for designing these actuators in future

Modeling and Force Estimation of Cardiac Catheters for Haptics-enabled Tele-intervention

Robot-assisted cardiovascular intervention (RCI) systems have shown success in reducing the x-ray exposure to surgeons and patients during cardiovascular interventional procedures. RCI systems typically are teleoperated systems with leader-follower architecture. With such system architecture, the surgeon is placed out of the x-ray exposure zone and uses a console to control the robot remotely. Despite its success in reducing x-ray exposure, clinicians have identified the lack of force feedback as to its main technological limitation that can lead to vascular perforation of the patient’s vessels and even their death. The objective of this thesis was to develop, verify, and validate mechatronics technology for real-time accurate and robust haptic feedback rendering for RCI systems. To attain the thesis objective, first, a thorough review of the state-of-the-art clinical requirements, modeling approaches and methods, and current knowledge gaps for the provision of force feedback for RCI systems was performed. Afterward, a real-time tip force estimation method based on image-based shape-sensing and learning-from-simulation was developed and validated. The learning-based model was fairly accurate but required a large database for training which was computationally expensive. Next, a new mechanistic model, i.e., finite arc method (FAM) for soft robots was proposed, formulated, solved, and validated that allowed for fast and accurate modeling of catheter deformation. With FAM, the required training database for the proposed learning-from-simulation method would be generated with high speed and accuracy. In the end, to robustly relay the estimated forces from real-time imaging from the follower robot to the leader haptic device, a novel impedance-based force feedback rendering modality was proposed and implemented on a representative teleoperated RCI system for experimental validation. The proposed method was compared with the classical direct force reflection method and showed enhanced stability, robustness, and accuracy in the presence of communication disruption. The results of this thesis showed that the performance of the proposed integrated force feedback rendering system was in fair compliance with the clinical requirements and had superior robustness compared to the classical direct force reflection method