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

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

Association of incident hip fracture with the estimated femoral strength by finite element analysis of DXA scans in the Osteoporotic Fractures in Men (MrOS) study

Finite element model can estimate bone strength better than BMD. This study used such a model to determine its association with hip fracture risk and found that the strength estimate provided limited improvement over the hip BMDs in predicting femoral neck (FN) fracture risk only. INTRODUCTION: Bone fractures occur only when it is loaded beyond its ultimate strength. The goal of this study was to determine the association of femoral strength, as estimated by finite element (FE) analysis of DXA scans, with incident hip fracture as a single condition or with femoral neck (FN) and trochanter (TR) fractures separately in older men. METHODS: This prospective case-cohort study included 91 FN and 64 TR fracture cases and a random sample of 500 men (14 had a hip fracture) from the Osteoporotic Fractures in Men study during a mean ± SD follow-up of 7.7 ± 2.2 years. We analysed the baseline DXA scans of the hip using a validated plane-stress, linear-elastic FE model of the proximal femur and estimated the femoral strength during a sideways fall. RESULTS: The estimated strength was significantly (P < 0.05) associated with hip fracture independent of the TR and total hip (TH) BMDs but not FN BMD, and combining the strength with BMD did not improve the hip fracture prediction. The strength estimate was associated with FN fractures independent of the FN, TR and TH BMDs; the age-BMI-BMD adjusted hazard ratio (95% CI) per SD decrease of the strength was 1.68 (1.07-2.64), 2.38 (1.57, 3.61) and 2.04 (1.34, 3.11), respectively. This association with FN fracture was as strong as FN BMD (Harrell's C index for the strength 0.81 vs. FN BMD 0.81) and stronger than TR and TH BMDs (0.8 vs. 0.78 and 0.81 vs. 0.79). The strength's association with TR fracture was not independent of hip BMD. CONCLUSIONS: Although the strength estimate provided additional information over the hip BMDs, its improvement in predictive ability over the hip BMDs was confined to FN fracture only and limited

Kreiranje zapreminskog 3D modela karlične kosti čoveka u uslovima nepotpunih ulaznih volumetrijskih podataka

Human hip bone represents a very complex morphological structure of irregular shape, resulting from the fusion of three primarily stand-alone bones. So, obtaining an accurate 3D model is a complex process, with the condition that a sufficiently dense point cloud is provided. In the first phase of the research, the hip bone model is obtained in reverse engineering process. The procedure itself is time consuming and requires a dedicated CT scan. So, the method of parametric regions is developed. The method allows obtaining a 3D model, even in cases where the volumetric data are not complete or when obtaining data is only possible from a two-dimensional, 2D scans. Anatomical landmarks (a total of 34) were defined at the bones. These anatomical landmarks are interconnected by parameters. Therefore, 58 parameters were defined, classified in two groups: parameters whose values are measured (21), and parameters whose values are obtained on the basis of regression equations (37). The points on the curves obtained by cutting the polygonal model with planes that are passing through given parameters are defined. The points at the parts of the polygonal models which describe the edges of the hip bone and the points that belong to certain parts of the bone are also defined. Parts of the surface that are bound with parameters represent the regions. In order to automate the processes three VBA macros are developed. The results were tested at the arbitrarily selected hip bone, using the methodology for creating the prediction model, which allows the creation of complete polygonal model or each region separately. It is also possible to create parts of the region - sub-regions, on the external or internal side of the hip bone or on both of its sides. Neighboring regions may be brought together. Connecting the region with parts of the obtained polygonal surfaces which describe the edges of the hip bone is also enabled