Patient-Specific Biomechanical Modeling of Bone Strength Using Statistically-Derived Fabric Tensors

Lekadir, Karim; Noble, Christopher; Hazrati, Javad; Hoogendoorn, Corné; Rietbergen, Bert van; Taylor, Zeike A.; Frangi, Alejandro F.

doi:10.1007/s10439-015-1432-2

Cited by 16 publications

(14 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The present study lacks the experimental data to validate the role of predicted bone fabric in computational models for calculation of bone strength. However, previously reported bone fabric prediction accuracies [ 3 , 6 , 29 , 35 ] are in the similar range of prediction accuracy reported here. Hence, we expect corresponding improvements in bone strength predictions.…”

Section: Discussionsupporting

confidence: 89%

A novel registration-based methodology for prediction of trabecular bone fabric from clinical QCT: A comprehensive analysis

Chandran

Zysset

2017

PLoS ONE

View full text Add to dashboard Cite

Osteoporosis leads to hip fractures in aging populations and is diagnosed by modern medical imaging techniques such as quantitative computed tomography (QCT). Hip fracture sites involve trabecular bone, whose strength is determined by volume fraction and orientation, known as fabric. However, bone fabric cannot be reliably assessed in clinical QCT images of proximal femur. Accordingly, we propose a novel registration-based estimation of bone fabric designed to preserve tensor properties of bone fabric and to map bone fabric by a global and local decomposition of the gradient of a non-rigid image registration transformation. Furthermore, no comprehensive analysis on the critical components of this methodology has been previously conducted. Hence, the aim of this work was to identify the best registration-based strategy to assign bone fabric to the QCT image of a patient’s proximal femur. The normalized correlation coefficient and curvature-based regularization were used for image-based registration and the Frobenius norm of the stretch tensor of the local gradient was selected to quantify the distance among the proximal femora in the population. Based on this distance, closest, farthest and mean femora with a distinction of sex were chosen as alternative atlases to evaluate their influence on bone fabric prediction. Second, we analyzed different tensor mapping schemes for bone fabric prediction: identity, rotation-only, rotation and stretch tensor. Third, we investigated the use of a population average fabric atlas. A leave one out (LOO) evaluation study was performed with a dual QCT and HR-pQCT database of 36 pairs of human femora. The quality of the fabric prediction was assessed with three metrics, the tensor norm (TN) error, the degree of anisotropy (DA) error and the angular deviation of the principal tensor direction (PTD). The closest femur atlas (CTP) with a full rotation (CR) for fabric mapping delivered the best results with a TN error of 7.3 ± 0.9%, a DA error of 6.6 ± 1.3% and a PTD error of 25 ± 2°. The closest to the population mean femur atlas (MTP) using the same mapping scheme yielded only slightly higher errors than CTP for substantially less computing efforts. The population average fabric atlas yielded substantially higher errors than the MTP with the CR mapping scheme. Accounting for sex did not bring any significant improvements. The identified fabric mapping methodology will be exploited in patient-specific QCT-based finite element analysis of the proximal femur to improve the prediction of hip fracture risk.

show abstract

Section: Discussionsupporting

confidence: 89%

A novel registration-based methodology for prediction of trabecular bone fabric from clinical QCT: A comprehensive analysis

Chandran

Zysset

2017

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Alternatively, trabecular fabric anisotropy can be estimated from the HR-pQCT image of a dissected femur with multiple approaches [41,42,43,44,45]. Taghizadeh et al [45] showed that averaged fabric anisotropy is a close approximation of patient-specific anisotropy and can be used in FE models.…”

Section: Introductionmentioning

confidence: 99%

Mapping anisotropy improves QCT-based finite element estimation of hip strength in pooled stance and side-fall load configurations

Panyasantisuk

Dall’Ara

Pretterklieber

et al. 2018

Medical Engineering & Physics

View full text Add to dashboard Cite

Hip fractures are one of the most severe consequences of osteoporosis. Compared to the clinical standard of DXA-based aBMD at the femoral neck, QCT-based FEA delivers a better surrogate of femoral strength and gains acceptance for the calculation of hip fracture risk when a CT reconstruction is available. Isotropic, homogenised voxel-based, finite element (hvFE) models are widely used to estimate femoral strength in cross-sectional and longitudinal clinical studies. However, fabric anisotropy is a classical feature of the architecture of the proximal femur and the second determinant of the homogenised mechanical properties of trabecular bone. Due to the limited resolution, fabric anisotropy cannot be derived from clinical CT reconstructions. Alternatively, fabric anisotropy can be extracted from HR-pQCT images of cadaveric femora. In this study, fabric anisotropy from HR-pQCT images was mapped onto QCT-based hvFE models of 71 human proximal femora for which both HR-pQCT and QCT images were available. Stiffness and ultimate load computed from anisotropic hvFE models were compared with previous biomechanical tests in both stance and side-fall configurations. The influence of using the femur-specific versus a mean fabric distribution on the hvFE predictions was assessed. Femur-specific and mean fabric enhance the prediction of experimental ultimate force for the pooled, i.e. stance and side-fall, (isotropic: r=0.81, femur-specific fabric: r=0.88, mean fabric: r=0.86,p<0.001) but not for the individual configurations. Fabric anisotropy significantly improves bone strength prediction for the pooled configurations, and mapped fabric provides a comparable prediction to true fabric. The mapping of fabric anisotropy is therefore expected to help generate more accurate QCT-based hvFE models of the proximal femur for personalised or multiple load configurations.

show abstract

“…Lekadir et al, 2014Lekadir et al, , 2015Clayden et al, 2016), use of computational models to produce virtual images of unobservable features (e.g. Nørgaard et al, 2016;Lekadir et al, 2016), or computational imaging approaches that incorporate prior knowledge into image acquisition or reconstruction leading, for instance, to agile or portable imaging/sensing systems (York et al, 2011;Coskun and Ozcan, 2014). Such models provide a framework for interpolating between, and extrapolating from the sparse observational states (spatially, temporally, and functionally) afforded by images.…”

Section: The Trend: From Data To Wisdom and Backmentioning

confidence: 99%

Precision Imaging: more descriptive, predictive and integrative imaging

Frangi

Taylor

Gooya

2016

Medical Image Analysis

Self Cite

View full text Add to dashboard Cite

Medical image analysis has grown into a matured field challenged by progress made across all medical imaging technologies and more recent breakthroughs in biological imaging. The cross-fertilisation between medical image analysis, biomedical imaging physics and technology, and domain knowledge from medicine and biology has spurred a truly interdisciplinary effort that stretched outside the original boundaries of the disciplines that gave birth to this field and created stimulating and enriching synergies. Consideration on how the field has evolved and the experience of the work carried out over the last 15 years in our centre, has led us to envision a future emphasis of medical imaging in Precision Imaging. Precision Imaging is not a new discipline but rather a distinct emphasis in medical imaging borne at the cross-roads between, and unifying the efforts behind mechanistic and phenomenological modelbased imaging. It captures three main directions in the effort to deal with the information deluge in imaging sciences, and thus achieve wisdom from data, information, and knowledge. Precision Imaging is finally characterised by being descriptive, predictive and integrative about the imaged object. This paper provides a brief and personal perspective on how the field has evolved, summarises and formalises our vision of Precision Imaging for Precision Medicine, and highlights some connections with past research and current trends in the field.

show abstract

Patient-Specific Biomechanical Modeling of Bone Strength Using Statistically-Derived Fabric Tensors

Cited by 16 publications

References 62 publications

A novel registration-based methodology for prediction of trabecular bone fabric from clinical QCT: A comprehensive analysis

A novel registration-based methodology for prediction of trabecular bone fabric from clinical QCT: A comprehensive analysis

Mapping anisotropy improves QCT-based finite element estimation of hip strength in pooled stance and side-fall load configurations

Precision Imaging: more descriptive, predictive and integrative imaging

Contact Info

Product

Resources

About