Augmented Reality Based Surgical Navigation of Complex Pelvic Osteotomies

first_page loading... settings Open AccessArticle Augmented Reality Based Surgical Navigation of Complex Pelvic Osteotomies—A Feasibility Study on Cadavers by Joëlle Ackermann 1,2,† [ORCID] , Florentin Liebmann 1,2,*,† [ORCID] , Armando Hoch 3 [ORCID] , Jess G. Snedeker 2,3, Mazda Farshad 3, Stefan Rahm 3, Patrick O. Zingg 3 and Philipp Fürnstahl 1 1 Research in Orthopedic Computer Science, Balgrist University Hospital, University of Zurich, 8008 Zurich, Switzerland 2 Laboratory for Orthopaedic Biomechanics, ETH Zurich, 8093 Zurich, Switzerland 3 Department of Orthopedics, Balgrist University Hospital, University of Zurich, 8008 Zurich, Switzerland * Author to whom correspondence should be addressed. † These authors contributed equally to this work. Academic Editor: Jiro Tanaka Appl. Sci. 2021, 11(3), 1228; https://doi.org/10.3390/app11031228 Received: 20 December 2020 / Revised: 13 January 2021 / Accepted: 25 January 2021 / Published: 29 January 2021 (This article belongs to the Special Issue Artificial Intelligence (AI) and Virtual Reality (VR) in Biomechanics) Download PDF Browse Figures Citation Export Abstract Augmented reality (AR)-based surgical navigation may offer new possibilities for safe and accurate surgical execution of complex osteotomies. In this study we investigated the feasibility of navigating the periacetabular osteotomy of Ganz (PAO), known as one of the most complex orthopedic interventions, on two cadaveric pelves under realistic operating room conditions. Preoperative planning was conducted on computed tomography (CT)-reconstructed 3D models using an in-house developed software, which allowed creating cutting plane objects for planning of the osteotomies and reorientation of the acetabular fragment. An AR application was developed comprising point-based registration, motion compensation and guidance for osteotomies as well as fragment reorientation. Navigation accuracy was evaluated on CT-reconstructed 3D models, resulting in an error of 10.8 mm for osteotomy starting points and 5.4° for osteotomy directions. The reorientation errors were 6.7°, 7.0° and 0.9° for the x-, y- and z-axis, respectively. Average postoperative error of LCE angle was 4.5°. Our study demonstrated that the AR-based execution of complex osteotomies is feasible. Fragment realignment navigation needs further improvement, although it is more accurate than the state of the art in PAO surgery

Doctor of Philosophy

dissertationAltered mechanics are believed to initiate osteoarthritis in hips with acetabular dysplasia. Periacetabular osteotomy (PAO) is the preferred surgical treatment; however, it is unknown if the procedure normalizes joint anatomy and mechanics. Changes in three-dimensional (3D) morphology and chondrolabral mechanics were quantified after PAO. Finite element (FE) models demonstrated that PAO improved the distribution of coverage, reduced stress, increased congruity, and prevented cartilage thinning. However, changes in mechanics were not consistent. In fact, one patient exhibited increased stress after surgery, which was believed to be a result of over-correction. Therefore, methods to integrate morphologic and biomechanical analysis with clinical care could standardize outcomes of PAO. FE simulations are time-intensive and require significant computing resources. Therefore, the second aim was to implement an efficient method to estimate mechanics. An enhanced discrete element analysis (DEA) model of the hip that accurately incorporated cartilage geometry and efficiently calculated stress was developed and analyzed. Although DEA model estimates predicted elevated magnitudes of contact stress, the distribution corresponded well with FE models. As a computationally efficient platform, DEA could assist in diagnosis and surgical planning. Imaging is a precursor to analyzing morphology and biomechanics. Ideally, an imaging protocol would visualize bone and soft-tissue at high resolution without ionizing radiation. Magnetic resonance imaging (MRI) with 3D dual-echo-steady-state (DESS) is a promising sequence to image the hip noninvasively, but its accuracy has not been quantified. Therefore, the final aim was to implement and validate the use of 3D DESS MRI in the hip. Using direct measurements of cartilage thickness as the standard, 3D DESS MRI imaged cartilage to ~0.5 mm of the physical measurements with 95% confidence, which is comparable to the most accurate hip imaging protocol presented to date. In summary, this dissertation provided unique insights into the morphologic and biomechanical features following PAO. In the future, DEA could be combined with 3D DESS MRI to efficiently analyze contact stress distributions. These methods could be incorporated into preoperative planning software, where the algorithm would predict the optimal relocation of the acetabulum to maximize femoral head coverage while minimizing contact stress, and thereby improve long-term outcomes of PAO

Patient-specific modelling in orthopedics: from image to surgery

In orthopedic surgery, to decide upon intervention and how it can be optimized, surgeons usually rely on subjective analysis of medical images of the patient, obtained from computed tomography, magnetic resonance imaging, ultrasound or other techniques. Recent advancements in computational performance, image analysis and in silico modeling techniques have started to revolutionize clinical practice through the development of quantitative tools, including patient#specific models aiming at improving clinical diagnosis and surgical treatment. Anatomical and surgical landmarks as well as features extraction can be automated allowing for the creation of general or patient-specific models based on statistical shape models. Preoperative virtual planning and rapid prototyping tools allow the implementation of customized surgical solutions in real clinical environments. In the present chapter we discuss the applications of some of these techniques in orthopedics and present new computer-aided tools that can take us from image analysis to customized surgical treatment

Interventional 2D/3D Registration with Contextual Pose Update

Traditional intensity-based 2D/3D registration requires near-perfect initialization in order for image similarity metrics to yield meaningful gradient updates of X-ray pose. They depend on image appearance rather than content, and therefore, fail in revealing large pose offsets that substantially alter the appearance of the same structure. We complement traditional similarity metrics with a convolutional neural network-based (CNN-based) similarity function that captures large-range pose relations by extracting both local and contextual information, and proposes meaningful X-ray pose updates without the need for accurate initialization. Our CNN accepts a target X-ray image and a digitally reconstructed radiograph at the current pose estimate as input and iteratively outputs pose updates on the Riemannian Manifold. It integrates seamlessly with conventional image-based registration frameworks. Long-range relations are captured primarily by our CNN-based method while short-range offsets can be recovered accurately with an image similarity-based method. On both synthetic and real X-ray images of the pelvis, we demonstrate that the proposed method can successfully recover large rotational and translational offsets, irrespective of initialization