This article introduces an innovative approach to simultaneously estimate time-domain and spatiotemporal faults in nonlinear distributed parameter systems (NDPSs)nonlinear distributed parameter systems (NDPSs) under external disturbances. First, the establishment of an iterative learning observer that accounts for both temporal and spatial changes is presented. Next, a fault estimation law is devised utilizing a distinct full iterative learning (FIL)full iterative learning (FIL) technique, facilitating rapid and precise estimation of fault signals while mitigating the impact of external disturbances. Furthermore, the adoption of the $\lambda $ -norm method aids in simplifying the determination of convergence conditions and gain matrix calculations. Lastly, comprehensive simulation results validate the efficacy of the developed approach, underscoring its adeptness in efficiently and precisely estimating faults across both time and spatiotemporal domains.

In this article, a distributed fault estimation (DFE) approach for switched interconnected nonlinear systems (SINSs) with time delays and external disturbances is proposed using a novel segmented iterative learning scheme (SILS). First, through the utilization of interrelated information among subsystems, a distributed iterative learning observer is developed to enhance the accuracy of fault estimation results, which can realize the fault estimation of all subsystems under time delays and external disturbances. Simultaneously, to facilitate rapid fault information tracking and significantly reduce sensitivity to interference, a new SILS-based fault estimation law is constructed by combining the idea of segmented design with the method of variable gain. Then, an assessment of the convergence of the established fault estimation methodology is conducted, and the configurations of observer gain matrices and iterative learning gain matrices are duly accomplished. Finally, simulation results are showcased to demonstrate the superiority and feasibility of the developed fault estimation approach.

The interlocking of intramedullary nails is a technically demanding procedure which involves a considerable amount of X-ray acquisitions; one study lists as many as 48 to successfully complete the procedure and fix screws into 4-6 mm distal holes of the nail. We propose to design an augmented radiolucent drill to assist surgeons in completing the distal locking procedure without any additional X-ray acquisitions. Using an augmented reality fluoroscope that coregisters optical and X-ray images, we exploit solely the optical images to detect the augmented radiolucent drill and estimate its tip position in real-time. Consequently, the surgeons will be able to maintain the down the beam positioning required to drill the screws into the nail holes successfully. To evaluate the accuracy of the proposed augmented drill, we perform a preclinical study involving six surgeons and ask them to perform distal locking on dry bone phantoms. Surgeons completed distal locking 98.3% of the time using only a single X-ray image with an average navigation time of 1.4 ± 0.9 min per hole.

Visualizing 2D and 3D anatomical information simultaneously within an X-ray image is challenging. Multiple monitors are required in the operating room to enable visualization of anatomical data by the surgeon. Consequently, this results in an interruption during the operation for proper assessment of information. In this paper, we introduce an interactive visualization of the 3D data from new perspectives including visualizing a virtual mirror from the same viewpoint as the X-ray source. The main contribution is our development of a complete angiographic visualization system that displays simultaneous 2D X-ray and 3D anatomical information in a common monitor for the surgeon in the operating room. No previous works have conceived the integration of the virtual mirror into the projection geometry of a C-arm fluoroscope. In total, 24 participants were asked to assess the benefits of the angiographic virtual mirror with different colour-depth encodings. The results of our feasibility study show a clear improvement when deciphering the true positions of aneurysms in X-ray. Lastly, color depth encoding improves correspondence between the 3D vasculature displayed in the virtual mirror to their projection images in X-ray.

For trauma and orthopedic surgery, maneuvering a mobile C-arm fluoroscope into a desired position to acquire an X-ray is a routine surgical task. The precision and ease of use of the C-arm becomes even more important for advanced interventional imaging techniques such as parallax-free X-ray image stitching. Today's standard mobile C-arms have been modeled with only five degrees of freedom (DOF), which definitely restricts their motions in 3-D Cartesian space. In this paper, we present a method to model both the mobile C-arm and patient's table as an integrated kinematic chain having six DOF without constraining table position. The closed-form solutions for the inverse kinematics problem are derived in order to obtain the required values for all C-arm joint and table movements to position the fluoroscope at a desired pose. The modeling method and the closed-form solutions can be applied to general isocentric or nonisocentric mobile C-arms. By achieving this we develop an efficient and intuitive inverse kinematics-based method for parallax-free panoramic X-ray imaging. In addition, we implement a 6-DOF C-arm system from a low-cost mobile fluoroscope to optimally acquire X-ray images based solely on the computation of the required movement for each joint by solving the inverse kinematics on a continuous basis. Through simulation experimentation, we demonstrate that the 6-DOF C-arm model has a larger working space than the 5-DOF model. C-arm repositioning experiments show the practicality and accuracy of our 6-DOF C-arm system. We also evaluate the novel parallax-free X-ray stitching method on phantom and dry bones. Using five trials, results show that parallax-free panoramas generated by our method are of high visual quality and within clinical tolerances for accurate evaluation of long bone geometry (i.e., image and metric measurement errors are less than 1% compared to ground-truth).

We propose a novel method to compute the poses of randomly positioned square markers in one world coordinate frame from multiple camera views, by taking the predicted accuracy of the camera pose estimation for each marker into account. The problem of computing the best closed-form solution of the world pose of each marker is modeled as all-pair shortest path problem in graph theory. The computed world poses are further optimized by minimizing the geometric distances in images. Experimental results show that incorporating the predicted accuracy of the pose estimation for each marker yields constant high quality calibration results independent of the order of image sequences compared to cases when this knowledge is not used.