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

An integrated approach for reconstructing a surface model of the proximal femur from sparse input data and a multi-resolution point distribution model: an in vitro study

Background: Accurate reconstruction of a patient-specific surface model of the proximal femur from preoperatively or intraoperatively available sparse data plays an important role in planning and supporting various computer-assisted surgical procedures. Methods: In this paper, we present an integrated approach using a multi-resolution point distribution model (MR-PDM) to reconstruct a patient-specific surface model of the proximal femur from sparse input data, which may consist of sparse point data or a limited number of calibrated X-ray images. Depending on the modality of the input data, our approach chooses different PDMs. When 3D sparse points are used, which may be obtained intraoperatively via a pointer-based digitization or from a calibrated ultrasound, a fine level point distribution model (FL-PDM) is used in the reconstruction process. In contrast, when calibrated X-ray images are used, which may be obtained preoperatively or intraoperatively, a coarse level point distribution model (CL-PDM) will be used. Results: The present approach was verified on 31 femurs. Three different types of input data, i.e., sparse points, calibrated fluoroscopic images, and calibrated X-ray radiographs, were used in our experiments to reconstruct a surface model of the associated bone. Our experimental results demonstrate promising accuracy of the present approach. Conclusions: A multi-resolution point distribution model facilitate the reconstruction of a patient-specific surface model of the proximal femur from sparse input dat

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

Estimating and abstracting the 3D structure of feline bones using neural networks on X-ray (2D) images

Computing 3D bone models using traditional Computed Tomography (CT) requires a high-radiation dose, cost and time. We present a fully automated, domain-agnostic method for estimating the 3D structure of a bone from a pair of 2D X-ray images. Our triplet loss-trained neural network extracts a 128-dimensional embedding of the 2D X-ray images. A classifier then finds the most closely matching 3D bone shape from a predefined set of shapes. Our predictions have an average root mean square (RMS) distance of 1.08 mm between the predicted and true shapes, making our approach more accurate than the average achieved by eight other examined 3D bone reconstruction approaches. Each embedding extracted from a 2D bone image is optimized to uniquely identify the 3D bone CT from which the 2D image originated and can serve as a kind of fingerprint of each bone; possible applications include faster, image content-based bone database searches for forensic purposes

Statistical atlas based registration and planning for ablating bone tumors in minimally invasive interventions

Bone tumor ablation has been a viable treatment in a minimally invasive way compared with surgical resections. In this paper, two key challenges in the computer-Assisted bone tumor ablation have been addressed: 1) establishing the spatial transformation of patient's tumor with respect to a global map of the patient using a minimum number of intra-operative images and 2) optimal treatment planning for large tumors. Statistical atlas is employed to construct the global reference map. The atlas is deformably registered to a pair of intra-operative fluoroscopy images, constructing a patient-specific model, in order to reduce the radiation exposure to the sensitive patients such as pregnant and infants. The optimal treatment planning system incorporates clinical constraints on ablations and trajectories using a multiple objective optimization, which obtains optimal trajectory planning and ablation coverage using integer programming. The proposed system is presented and validated by experiments. © 2012 IEEE.published_or_final_versio

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

Doctor of Philosophy

dissertationGeometric abnormalities of the human hip joint, as found in femoroacetabular impingement (FAI) and acetabular dysplasia, alter hip biomechanics and may be the primary causes of osteoarthritis in young adults. However, empirical evidence of direct correlations between abnormal geometry, altered biomechanics, and osteoarthritis is scarce. Also, clinical measures used to diagnose FAI and dysplasia still have substantial limitations, including questions about their reliability, assumptions about hip joint geometry and their ability to definitively distinguish pathologic from normal hips. The goals of this dissertation are twofold. First, a set of tools are presented and applied to quantify three-dimensional (3D) anatomical differences between hips with FAI and control subjects. The 3D tools were developed, validated and applied to patients with a subtype of FAI, called cam FAI, to improve basic understanding of the spectrum of FAI deformities, and to provide meaningful new metrics of morphology that are relatable to current diagnostic methods and translate easily for clinical use. The second goal of this dissertation is to improve our understanding of intra-articular hip contact mechanics as well as hip joint kinematics and muscle forces. To do so, a finite element study of intraarticular cartilage contact mechanics was completed with a cohort of live human subjects, using a validated modeling protocol. Finally, musculoskeletal modeling was used with gait data from healthy subjects and acetabular dysplasia patients to provide preliminary estimates of hip joint kinematics, kinetics, and muscle forces and compare differences between the groups. The translational methods of this dissertation utilized techniques from orthopaedics, computer science, physical therapy, mechanics, and medical imaging. Results from this dissertation offer new insight into the complex pathomechanics and pathomorphology of FAI and acetabular dysplasia. Application and extension of the work of this dissertation has the potential to help establish links between FAI and dysplasia with osteoarthritis and to improve patient care

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

Interlandmark measurements from lodox statscan images with application to femoral neck anteversion assessment

Includes abstract.Includes bibliographical references.Clinicians often take measurements between anatomical landmarks on X-ray radiographs for diagnosis and treatment planning, for example in orthopaedics and orthodontics. X-ray images, however, overlap three-dimensional internal structures onto a two-dimensional plane during image formation. Depth information is therefore lost and measurements do not truly reflect spatial relationships. The main aim of this study was to develop an inter-landmark measurement tool for the Lodox Statscan digital radiography system. X-ray stereophotogrammetry was applied to Statscan images to enable three-dimensional point localization for inter-landmark measurement using two-dimensional radiographs. This technique requires images of the anatomical region of interest to be acquired from different perspectives as well as a suitable calibration tool to map image coordinates to real world coordinates. The Statscan is suited to the technique because it is capable of axial rotations for multiview imaging. Three-dimensional coordinate reconstruction and inter-landmark measurements were taken using a planar object and a dry pelvis specimen in order to assess the intra-observer measurement accuracy, reliability and precision. The system yielded average (X, Y, Z) coordinate reconstruction accuracy of (0.08 0.12 0.34) mm and resultant coordinate reconstruction accuracy within 0.4mm (range 0.3mm – 0.6mm). Inter-landmark measurements within 2mm for lengths and 1.80 for angles were obtained, with average accuracies of 0.4mm (range 0.0mm – 2.0 mm) and 0.30 (range 0.0 – 1.8)0 respectively. The results also showed excellent overall precision of (0.5mm, 0.10) and were highly reliable when all landmarks were completely visible in both images. Femoral neck anteversion measurement on Statscan images was also explored using 30 dry right adult femurs. This was done in order to assess the feasibility of the algorithm for a clinical application. For this investigation, four methods were tested to determine the optimal landmarks for measurement and the measurement process involved calculation of virtual landmarks. The method that yielded the best results produced all measurements within 10 of reference values and the measurements were highly reliable with very good precision within 0.10. The average accuracy was within 0.40 (range 0.10 –0.80).In conclusion, X-ray stereophotogrammetry enables accurate, reliable and precise inter-landmark measurements for the Lodox Statscan X-ray imaging system. The machine may therefore be used as an inter-landmark measurement tool for routine clinical applications