1,671 research outputs found

    Recent trends, technical concepts and components of computer-assisted orthopedic surgery systems: A comprehensive review

    Get PDF
    Computer-assisted orthopedic surgery (CAOS) systems have become one of the most important and challenging types of system in clinical orthopedics, as they enable precise treatment of musculoskeletal diseases, employing modern clinical navigation systems and surgical tools. This paper brings a comprehensive review of recent trends and possibilities of CAOS systems. There are three types of the surgical planning systems, including: systems based on the volumetric images (computer tomography (CT), magnetic resonance imaging (MRI) or ultrasound images), further systems utilize either 2D or 3D fluoroscopic images, and the last one utilizes the kinetic information about the joints and morphological information about the target bones. This complex review is focused on three fundamental aspects of CAOS systems: their essential components, types of CAOS systems, and mechanical tools used in CAOS systems. In this review, we also outline the possibilities for using ultrasound computer-assisted orthopedic surgery (UCAOS) systems as an alternative to conventionally used CAOS systems.Web of Science1923art. no. 519

    AN AUTOMATED, DEEP LEARNING APPROACH TO SYSTEMATICALLY & SEQUENTIALLY DERIVE THREE-DIMENSIONAL KNEE KINEMATICS DIRECTLY FROM TWO-DIMENSIONAL FLUOROSCOPIC VIDEO

    Get PDF
    Total knee arthroplasty (TKA), also known as total knee replacement, is a surgical procedure to replace damaged parts of the knee joint with artificial components. It aims to relieve pain and improve knee function. TKA can improve knee kinematics and reduce pain, but it may also cause altered joint mechanics and complications. Proper patient selection, implant design, and surgical technique are important for successful outcomes. Kinematics analysis plays a vital role in TKA by evaluating knee joint movement and mechanics. It helps assess surgery success, guides implant and technique selection, informs implant design improvements, detects problems early, and improves patient outcomes. However, evaluating the kinematics of patients using conventional approaches presents significant challenges. The reliance on 3D CAD models limits applicability, as not all patients have access to such models. Moreover, the manual and time-consuming nature of the process makes it impractical for timely evaluations. Furthermore, the evaluation is confined to laboratory settings, limiting its feasibility in various locations. This study aims to address these limitations by introducing a new methodology for analyzing in vivo 3D kinematics using an automated deep learning approach. The proposed methodology involves several steps, starting with image segmentation of the femur and tibia using a robust deep learning approach. Subsequently, 3D reconstruction of the implants is performed, followed by automated registration. Finally, efficient knee kinematics modeling is conducted. The final kinematics results showed potential for reducing workload and increasing efficiency. The algorithms demonstrated high speed and accuracy, which could enable real-time TKA kinematics analysis in the operating room or clinical settings. Unlike previous studies that relied on sponsorships and limited patient samples, this algorithm allows the analysis of any patient, anywhere, and at any time, accommodating larger subject populations and complete fluoroscopic sequences. Although further improvements can be made, the study showcases the potential of machine learning to expand access to TKA analysis tools and advance biomedical engineering applications

    3D Reconstruction of Interventional Material from Very Few X-Ray Projections for Interventional Image Guidance

    Get PDF
    Today, minimally invasive endovascular interventions are usually guided by 2D fluoroscopy, i.e. a live 2D X-ray image. However, 3D fluoroscopy, i.e. a live 3D image reconstructed from a stream of 2D X-ray images, could improve spatial awareness. 3D fluoroscopy is, however, not used today, since no appropriate 3D reconstruction algorithm is known. Existing algorithms for the real-time reconstruction of interventional material (guidewires, stents, catheters, etc.) are either only capable of reconstructing a single guidewire or catheter, or use too many X-ray images and therefore too much dose per 3D reconstruction. The goal of this thesis was to reconstruct complex arrangements of interventional material from as few X-ray images as possible. To this end, a previously proposed algorithm for the reconstruction of interventional material from four X-ray images was adapted. Five key improvements allowed to reduce the number of X-ray images per 3D reconstruction from four to two: a) use of temporal information in a rotating imaging setup, b) separate reconstruction of different types of interventional material enabled by the computation of semantic interventional material extraction images, c) compensation of stent motion by spatial transformer networks, d) per-projection backprojection and e) binarization of the guidewire extraction images. While previously only single curves could be reconstructed from two newly acquired X-ray images, the proposed pipeline can reconstruct stents and even stent-guidewire combinations. Submillimeter reconstruction accuracy was demonstrated on measured X-ray images of interventional material inside an anthropomorphic phantom with simulated respiratory motion. Measurements of the dose area product rate of the proposed 3D reconstruction pipeline indicate a dose burden roughly similar to that of 2D fluoroscopy

    X23D-Intraoperative 3D Lumbar Spine Shape Reconstruction Based on Sparse Multi-View X-ray Data

    Full text link
    Visual assessment based on intraoperative 2D X-rays remains the predominant aid for intraoperative decision-making, surgical guidance, and error prevention. However, correctly assessing the 3D shape of complex anatomies, such as the spine, based on planar fluoroscopic images remains a challenge even for experienced surgeons. This work proposes a novel deep learning-based method to intraoperatively estimate the 3D shape of patients' lumbar vertebrae directly from sparse, multi-view X-ray data. High-quality and accurate 3D reconstructions were achieved with a learned multi-view stereo machine approach capable of incorporating the X-ray calibration parameters in the neural network. This strategy allowed a priori knowledge of the spinal shape to be acquired while preserving patient specificity and achieving a higher accuracy compared to the state of the art. Our method was trained and evaluated on 17,420 fluoroscopy images that were digitally reconstructed from the public CTSpine1K dataset. As evaluated by unseen data, we achieved an 88% average F1 score and a 71% surface score. Furthermore, by utilizing the calibration parameters of the input X-rays, our method outperformed a counterpart method in the state of the art by 22% in terms of surface score. This increase in accuracy opens new possibilities for surgical navigation and intraoperative decision-making solely based on intraoperative data, especially in surgical applications where the acquisition of 3D image data is not part of the standard clinical workflow
    corecore