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

Reconstruction of Patient-Specific Bone Models from X-Ray Radiography

The availability of a patient‐specific bone model has become an increasingly invaluable addition to orthopedic case evaluation and planning [1]. Utilized within a wide range of specialized visualization and analysis tools, such models provide unprecedented wealth of bone shape information previously unattainable using traditional radiographic imaging [2]. In this work, a novel bone reconstruction method from two or more x‐ray images is described. This method is superior to previous attempts in terms of accuracy and repeatability. The new technique accurately models the radiological scene in a way that eliminates the need for expensive multi‐planar radiographic imaging systems. It is also flexible enough to allow for both short and long film imaging using standard radiological protocols, which makes the technology easily utilized in standard clinical setups

Stochastic PCA-based bone models from inverse transform sampling: Proof of concept for mandibles and proximal femurs

Principal components analysis is a powerful technique which can be used to reduce data dimensionality. With reference to three-dimensional bone shape models, it can be used to generate an unlimited number of models, defined by thousands of nodes, from a limited (less than twenty) number of scalars. The full procedure has been here described in detail and tested. Two databases were used as input data: the first database comprised 40 mandibles, while the second one comprised 98 proximal femurs. The “average shape” and principal components that were required to cover at least 90% of the whole variance were identified for both bones, as well as the statistical distributions of the respective principal components weights. Fifteen principal components sufficed to describe the mandibular shape, while nine components sufficed to describe the proximal femur morphology. A routine has been set up to generate any number of mandible or proximal femur geometries, according to the actual statistical shape distributions. The set-up procedure can be generalized to any bone shape given a sufficiently large database of the respective 3D shapes

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

A Morphing Technique Applied to Lung Motions in Radiotherapy: Preliminary Results

Organ motion leads to dosimetric uncertainties during a patient’s treatment. Much work has been done to quantify the dosimetric effects of lung movement during radiation treatment. There is a particular need for a good description and prediction of organ motion. To describe lung motion more precisely, we have examined the possibility of using a computer technique: a morphing algorithm. Morphing is an iterative method which consists of blending one image into another image. To evaluate the use of morphing, Four Dimensions Computed Tomography (4DCT) acquisition of a patient was performed. The lungs were automatically segmented for different phases, and morphing was performed using the end-inspiration and the end-expiration phase scans only. Intermediate morphing files were compared with 4DCT intermediate images. The results showed good agreement between morphing images and 4DCT images: fewer than 2 % of the 512 by 256 voxels were wrongly classified as belonging/not belonging to a lung section. This paper presents preliminary results, and our morphing algorithm needs improvement. We can infer that morphing offers considerable advantages in terms of radiation protection of the patient during the diagnosis phase, handling of artifacts, definition of organ contours and description of organ motion

3D shape reconstruction of the femur from planar X-ray images using statistical shape and appearance models

Major trauma is a condition that can result in severe bone damage. Customised orthopaedic reconstruction allows for limb salvage surgery and helps to restore joint alignment. For the best possible outcome three dimensional (3D) medical imaging is necessary, but its availability and access, especially in developing countries, can be challenging. In this study, 3D bone shapes of the femur reconstructed from planar radiographs representing bone defects were evaluated for use in orthopaedic surgery. Statistical shape and appearance models generated from 40 cadaveric X-ray computed tomography (CT) images were used to reconstruct 3D bone shapes. The reconstruction simulated bone defects of between 0% and 50% of the whole bone, and the prediction accuracy using anterior–posterior (AP) and anterior–posterior/medial–lateral (AP/ML) X-rays were compared. As error metrics for the comparison, measures evaluating the distance between contour lines of the projections as well as a measure comparing similarities in image intensities were used. The results were evaluated using the root-mean-square distance for surface error as well as differences in commonly used anatomical measures, including bow, femoral neck, diaphyseal–condylar and version angles between reconstructed surfaces from the shape model and the intact shape reconstructed from the CT image. The reconstructions had average surface errors between 1.59 and 3.59 mm with reconstructions using the contour error metric from the AP/ML directions being the most accurate. Predictions of bow and femoral neck angles were well below the clinical threshold accuracy of 3°, diaphyseal–condylar angles were around the threshold of 3° and only version angle predictions of between 5.3° and 9.3° were above the clinical threshold, but below the range reported in clinical practice using computer navigation (i.e., 17° internal to 15° external rotation). This study shows that the reconstructions from partly available planar images using statistical shape and appearance models had an accuracy which would support their potential use in orthopaedic reconstruction

Stochastic PCA-based bone models from inverse transform sampling: proof of concept for mandibles and proximal femurs

Principal components analysis is a powerful technique which can be used to reduce data dimensionality. With reference to three-dimensional bone shape models, it can be used to generate an unlimited number of models, defined by thousands of nodes, from a limited (less than twenty) number of scalars. The full procedure has been here described in detail and tested. Two databases were used as input data: the first database comprised 40 mandibles, while the second one comprised 98 proximal femurs. The “average shape” and principal components that were required to cover at least 90% of the whole variance were identified for both bones, as well as the statistical distributions of the respective principal components weights. Fifteen principal components sufficed to describe the mandibular shape, while nine components sufficed to describe the proximal femur morphology. A routine has been set up to generate any number of mandible or proximal femur geometries, according to the actual statistical shape distributions. The set-up procedure can be generalized to any bone shape given a sufficiently large database of the respective 3D shapes

A Novel Free Form Femoral Cutting Guide

Knee arthoplasty is a common procedure that requires the removal of damaged bone and cartilage from the distal femur so that a reconstructive implant may be installed. Traditionally, a five planar resection has been accomplished with a universal cutting box and navigated with either metal jigs or optically tracked computer navigation systems. Free form, or curved, resections have been made possible with surgical robots which control the resection pathway and serve as the navigation system. The free form femoral cutting guide serves as a non powered framework to guide a standard surgical drill along an anatomically defined pathway, resulting in the removal of distal femoral cartilage. It is fixed via attachment to a bone mounted base component, which is positioned with a patient specific jig. To operate, the surgeon slides the surgical drill along a pair of interlocked tracks. One track controls motion in the anteroposterior (AP) direction and one track controls motion in the mediolateral (ML) direction. Combining both motions results in the removal of cartilage from the area of the distal femur for unilateral or total knee arthoplasty

Computer Assisted Navigation in Knee Arthroplasty

Computer assisted surgery (CAS) was used to improve the positioning of implants during total knee arthroplasty (TKA). Most studies have reported that computer assisted navigation reduced the outliers of alignment and component malpositioning. However, additional sophisticated studies are necessary to determine if the improvement of alignment will improve long-term clinical results and increase the survival rate of the implant. Knowledge of CAS-TKA technology and understanding the advantages and limitations of navigation are crucial to the successful application of the CAS technique in TKA. In this article, we review the components of navigation, classification of the system, surgical method, potential error, clinical results, advantages, and disadvantages

A multibody model for the optimization of hip arthroplasty in relation to range of movement

Abstract Background The dislocation of the prosthesized hip is a relevant postoperative complication; this adverse outcome is dependent on the specific patient anatomy and on the artificial joint design. The geometry of the reconstructed hip is one of the key factors and it is usually designed at the time of the pre-operative planning when the stem model and size, the head diameter and its offset, and the acetabular cup orientation are selected. Aims In this work, the authors have developed a numerical model to support the pre-operative planning, allowing assessing the hip range of motion, once the geometry of the implant has been defined. Methods A multi-body model of a prosthesized hip has been developed, and a dislocating movement has been applied; the software is able to assess the entity of displacements and of applied forces which can produce hip dislocation. Results As a proof of concept, multiple combinations of geometric factors have been examined that are the head diameter, the acetabular cup anteversion and its inclination, reaching a total number of 675 configurations. This software is able to analyse and compare all configurations in few minutes. Conclusion The developed numerical model can be a support to quickly compare a great number of solutions from the point of view of hip stability, reaching a comprehensive view of all possibilities, and giving a contribute to the final aim that is surgery optimization, in relation to each specific patient