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

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

Fluoroscopy-based tracking of femoral kinematics with statistical shape models

Precise knee kinematics assessment helps to diagnose knee pathologies and to improve the design of customized prosthetic components. The first step in identifying knee kinematics is to assess the femoral motion in the anatomical frame. However, no work has been done on pathological femurs, whose shape can be highly different from healthy ones

Towards multiple 3D bone surface identification and reconstruction using few 2D X-ray images for intraoperative applications

This article discusses a possible method to use a small number, e.g. 5, of conventional 2D X-ray images to reconstruct multiple 3D bone surfaces intraoperatively. Each bone’s edge contours in X-ray images are automatically identified. Sparse 3D landmark points of each bone are automatically reconstructed by pairing the 2D X-ray images. The reconstructed landmark point distribution on a surface is approximately optimal covering main characteristics of the surface. A statistical shape model, dense point distribution model (DPDM), is then used to fit the reconstructed optimal landmarks vertices to reconstruct a full surface of each bone separately. The reconstructed surfaces can then be visualised and manipulated by surgeons or used by surgical robotic systems

Generation of 3D shape, density, cortical thickness and finite element mesh of proximal femur from a DXA image

Areal bone mineral density (aBMD), as measured by dual-energy X-ray absorptiometry (DXA), predicts hip fracture risk only moderately. Simulation of bone mechanics based on DXA imaging of the proximal femur, may help to improve the prediction accuracy. Therefore, we collected three (1-3) image sets, including CT images and DXA images of 34 proximal cadaver femurs (set1, including 30 males, 4 females), 35 clinical patient CT images of the hip (set 2, including 27 males, 8 females) and both CT and DXA images of clinical patients (set 3, including 12 female patients). All CT images were segmented manually and landmarks were placed on both femurs and pelvises. Two separate statistical appearance models (SAMs) were built using the CT images of the femurs and pelvises in sets 1 and 2, respectively. The 3D shape of the femur was reconstructed from the DXA image by matching the SAMs with the DXA images. The orientation and modes of variation of the SAMs were adjusted to minimize the sum of the absolute differences between the projection of the SAMs and a DXA image. The mesh quality and the location of the SAMs with respect to the manually placed control points on the DXA image were used as additional constraints. Then, finite element (FE) models were built from the reconstructed shapes. Mean point-to-surface distance between the reconstructed shape and CT image was 1.0mm for cadaver femurs in set 1 (leave-one-out test) and 1.4mm for clinical subjects in set 3. The reconstructed volumetric BMD showed a mean absolute difference of 140 and 185mg/cm3 for set 1 and set 3 respectively. The generation of the SAM and the limitation of using only one 2D image were found to be the most significant sources of errors in the shape reconstruction. The noise in the DXA images had only small effect on the accuracy of the shape reconstruction. DXA-based FE simulation was able to explain 85% of the CT-predicted strength of the femur in stance loading. The present method can be used to accurately reconstruct the 3D shape and internal density of the femur from 2D DXA images. This may help to derive new information from clinical DXA images by producing patient-specific FE models for mechanical simulation of femoral bone mechanics

3D Shape Reconstruction of Knee Bones from Low Radiation X-ray Images Using Deep Learning

Understanding the bone kinematics of the human knee during dynamic motions is necessary to evaluate the pathological conditions, design knee prosthesis, orthosis and surgical treatments such as knee arthroplasty. Also, knee bone kinematics is essential to assess the biofidelity of the computational models. Kinematics of the human knee has been reported in the literature either using in vitro or in vivo methodologies. In vivo methodology is widely preferred due to biomechanical accuracies. However, it is challenging to obtain the kinematic data in vivo due to limitations in existing methods. One of the several existing methods used in such application is using X-ray fluoroscopy imaging, which allows for the non-invasive quantification of bone kinematics. In the fluoroscopy imaging method, due to procedural simplicity and low radiation exposure, single-plane fluoroscopy (SF) is the preferred tool to study the in vivo kinematics of the knee joint. Evaluation of the three-dimensional (3D) kinematics from the SF imagery is possible only if prior knowledge of the shape of the knee bones is available. The standard technique for acquiring the knee shape is to either segment Magnetic Resonance (MR) images, which is expensive to procure, or Computed Tomography (CT) images, which exposes the subjects to a heavy dose of ionizing radiation. Additionally, both the segmentation procedures are time-consuming and labour-intensive. An alternative technique that is rarely used is to reconstruct the knee shape from the SF images. It is less expensive than MR imaging, exposes the subjects to relatively lower radiation than CT imaging, and since the kinematic study and the shape reconstruction could be carried out using the same device, it could save a considerable amount of time for the researchers and the subjects. However, due to low exposure levels, SF images are often characterized by a low signal-to-noise ratio, making it difficult to extract the required information to reconstruct the shape accurately. In comparison to conventional X-ray images, SF images are of lower quality and have less detail. Additionally, existing methods for reconstructing the shape of the knee remain generally inconvenient since they need a highly controlled system: images must be captured from a calibrated device, care must be taken while positioning the subject's knee in the X-ray field to ensure image consistency, and user intervention and expert knowledge is required for 3D reconstruction. In an attempt to simplify the existing process, this thesis proposes a new methodology to reconstruct the 3D shape of the knee bones from multiple uncalibrated SF images using deep learning. During the image acquisition using the SF, the subjects in this approach can freely rotate their leg (in a fully extended, knee-locked position), resulting in several images captured in arbitrary poses. Relevant features are extracted from these images using a novel feature extraction technique before feeding it to a custom-built Convolutional Neural Network (CNN). The network, without further optimization, directly outputs a meshed 3D surface model of the subject's knee joint. The whole procedure could be completed in a few minutes. The robust feature extraction technique can effectively extract relevant information from a range of image qualities. When tested on eight unseen sets of SF images with known true geometry, the network reconstructed knee shape models with a shape error (RMSE) of 1.91± 0.30 mm for the femur, 2.3± 0.36 mm for the tibia and 3.3± 0.53 mm for the patella. The error was calculated after rigidly aligning (scale, rotation, and translation) each of the reconstructed shape models with the corresponding known true geometry (obtained through MRI segmentation). Based on a previous study that examined the influence of reconstructed shape accuracy on the precision of the evaluation of tibiofemoral kinematics, the shape accuracy of the proposed methodology might be adequate to precisely track the bone kinematics, although further investigation is required

From medical data to simple virtual mock-up of scapulo-humeral joint

The surgical operations of shoulder joint are guided by various principles: osteosynthesis in the case of fracture, osteotomy in order to correct a deformation or to modify the functioning of the joint, or implementation of articular prosthesis. At the end of the twentieth century, many innovations in the domains of biomechanics and orthopedic surgery have been performed. Nevertheless, theoretical and practical problems may appear during the operation (visual field of surgeon is very limited, quality and shape of the bone is variable depending on the patient). Biomechanical criteria of success are defined for each intervention. For example, the installation with success of prosthetic implant will be estimated according to the degree of mobility of the new articulation, the movements of this articulation being function of the shape of the prosthesis and of its position on its osseous support. It is not always easy to optimize the preparation of the surgical operation for every patient, and a preliminary computer simulation would allow helping the surgeon in its choices and its preparation of the intervention. The techniques of virtual reality allow a high degree of immersion and allow envisaging the development of a navigation device during the operating act

Three Dimensional Nonlinear Statistical Modeling Framework for Morphological Analysis

This dissertation describes a novel three-dimensional (3D) morphometric analysis framework for building statistical shape models and identifying shape differences between populations. This research generalizes the use of anatomical atlases on more complex anatomy as in case of irregular, flat bones, and bones with deformity and irregular bone growth. The foundations for this framework are: 1) Anatomical atlases which allow the creation of homologues anatomical models across populations; 2) Statistical representation for output models in a compact form to capture both local and global shape variation across populations; 3) Shape Analysis using automated 3D landmarking and surface matching. The proposed framework has various applications in clinical, forensic and physical anthropology fields. Extensive research has been published in peer-reviewed image processing, forensic anthropology, physical anthropology, biomedical engineering, and clinical orthopedics conferences and journals. The forthcoming discussion of existing methods for morphometric analysis, including manual and semi-automatic methods, addresses the need for automation of morphometric analysis and statistical atlases. Explanations of these existing methods for the construction of statistical shape models, including benefits and limitations of each method, provide evidence of the necessity for such a novel algorithm. A novel approach was taken to achieve accurate point correspondence in case of irregular and deformed anatomy. This was achieved using a scale space approach to detect prominent scale invariant features. These features were then matched and registered using a novel multi-scale method, utilizing both coordinate data as well as shape descriptors, followed by an overall surface deformation using a new constrained free-form deformation. Applications of output statistical atlases are discussed, including forensic applications for the skull sexing, as well as physical anthropology applications, such as asymmetry in clavicles. Clinical applications in pelvis reconstruction and studying of lumbar kinematics and studying thickness of bone and soft tissue are also discussed