Fracture toughness of human femoral neck: effect of microstructure, composition, and age

Yeni, Y. A.; Norman, Timothy L.

doi:10.1016/s8756-3282(00)00258-1

Cited by 66 publications

(59 citation statements)

References 31 publications

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“…Due to its clinical significance, the femoral neck has been the focus of numerous studies, which have examined the relationship between cortical microstructure and strength. Cortical porosity is an important factor influencing the fracture toughness of this structure (Yeni and Norman, 2000). Differences in the distribution of cortical porosity in the femoral neck, with increased porosity concentrated in the anterior region have been associated with increased fracture risk (Bell et al, 1999).…”

Section: Potential Applications Of 3d Analysismentioning

confidence: 99%

Quantitative 3D analysis of the canal network in cortical bone by micro‐computed tomography

Cooper¹,

Turinsky²,

Sensen³

et al. 2003

The Anatomical Record Part B

171

155

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Cortical bone is perforated by an interconnected network of porous canals that facilitate the distribution of neurovascular structures throughout the cortex. This network is an integral component of cortical microstructure and, therefore, undergoes continual change throughout life as the cortex is remodeled. To date, the investigation of cortical microstructure, including the canal network, has largely been limited to the two-dimensional (2D) realm due to methodological hurdles. Thanks to continuing improvements in scan resolution, micro-computed tomography (muCT) is the first nondestructive imaging technology capable of resolving cortical canals. Like its application to trabecular bone, muCT provides an efficient means of quantifying aspects of 3D architecture of the canal network. Our aim here is to introduce the use of muCT for this application by providing examples, discussing some of the parameters that can be acquired, and relating these to research applications. Although several parameters developed for the analysis of trabecular microstructure are suitable for the analysis of cortical porosity, the algorithm used to estimate connectivity is not. We adapt existing algorithms based on skeletonization for this task. We believe that 3D analysis of the dimensions and architecture of the canal network will provide novel information relevant to many aspects of bone biology. For example, parameters related to the size, spacing, and volume of the canals may be particularly useful for investigation of the mechanical properties of bone. Alternatively, parameters describing the 3D architecture of the canal network, such as connectivity between the canals, may provide a means of evaluating cumulative remodeling related change.

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Section: Potential Applications Of 3d Analysismentioning

confidence: 99%

Quantitative 3D analysis of the canal network in cortical bone by micro‐computed tomography

Cooper¹,

Turinsky²,

Sensen³

et al. 2003

The Anatomical Record Part B

171

155

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show abstract

“…Among these factors, porosity has been demonstrated to be a major determinant of strength, stiffness, and fracture toughness of cortical tissue. [3][4][5][6][7][8][9] Cortical bone porosity is responsive to disease, therapy, and metabolic alterations. Further, cortical pore structure is dynamic, becoming increasingly interconnected and convoluted as age progresses.…”

Section: Introductionmentioning

confidence: 99%

Structural analysis of cortical porosity applied to HR-pQCT data

et al. 2013

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Purpose:The investigation of cortical porosity is an important aspect of understanding biological, pathoetiological, and biomechanical processes occurring within the skeleton. With the emergence of HR-pQCT as a noninvasive tool suitable for clinical use, cortical porosity at appendicular sites can be directly visualized in vivo. The aim of this study was to introduce a novel topological analysis of the cortical pore network for HR-pQCT data and determine the influence of resolution on measures of cortical pore network microstructure and topology. Methods: Cadaveric radii were scanned using HR-pQCT at two different voxel sizes (41 and 82 μm) and also using μCT at a voxel size of 18 μm. HR-pQCT and μCT image sets were spatially coregistered. Segmentation and quantification of cortical porosity (Ct.Po) and mean pore diameter (Ct.Po.Dm) were achieved using an established extended cortical analysis technique. Topological classification of individual pores was performed using topology-preserving skeletonization and multicolor dilation algorithms. Based on the pore skeleton topological classification, the following parameters were quantified: total number of planar surface-skeleton canals (N.Slabs), tubular curve-skeleton canals (N.Tubes), and junction elements (N.Junctions), mean slab volume (Slab.Vol), mean tube volume (Tube.Vol), mean slab orientation (Slab.θ ), mean tube orientation (Tube.θ ), N.Slabs/N.Tubes, and integral (total) slab volume/integral tube volume (iSlab.Vol/iTube.Vol). An in vivo reproducibility study was also conducted to assess short-term precision of the topology parameters. Precision error was characterized using root mean square coefficient of variation (RMSCV%). , though proportional bias was evident in these correlations. Weak correlations were seen for iSlab.Vol/iTube.Vol at both voxel sizes (41: r 2 = 0.52, p < 0.01; 82: r 2 = 0.39, p < 0.05). Slab.Vol was significantly correlated to μCT data at 41 μm (r 2 = 0.60, p < 0.01) but not at 82 μm, while Tube.Vol was significantly correlated at both voxel sizes (41: r 2 = 0.79, p < 0.001; 82: r 2 = 0.68, p < 0.01). In vivo precision error for these parameters ranged from 2.31 to 9.68 RMSCV%. Conclusions: Strong correlations between μCT-and HR-pQCT-derived measurements were found, particularly in HR-pQCT images obtained at 41 μm. These data are in agreement with our previous study investigating the effect of voxel size on standard HR-pQCT metrics of trabecular and cortical microstructure, and extend our previous findings to include topological descriptors of the cortical pore network.

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“…Numerous studies have established that there is a significant deterioration in the toughness of bone with age (e.g., [6,7,8,9,10]); nevertheless, a mechanistic framework for describing how the microstructure affects the failure of bone is still lacking. What is needed is an understanding of (i) how aging, disease or therapeutic treatment can affect the structure of bone, defined broadly from nano-through micro-to macro-scopic size-scales, and (ii) how this specifically affects the mechanisms responsible for the deformation and fracture of bone.…”

Section: Introductionmentioning

confidence: 99%

Fracture, aging, and disease in bone

2006

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From a public health perspective, developing a detailed mechanistic understanding of the well-known increase in fracture risk of human bone with age is essential. This also represents a challenge from materials science and fracture mechanics viewpoints. Bone has a complex, hierarchical structure with characteristic features ranging from nanometer to macroscopic dimensions; it is therefore significantly more complex than most engineering materials. Nevertheless, by examining the micro-/nano-structural changes accompanying the process of aging using appropriate multiscale experimental methods and relating them to fracture mechanics data, it is possible to obtain a quantitative picture of how bone resists fracture. As human cortical bone exhibits rising ex vivo crack-growth resistance with crack extension, its fracture toughness must be evaluated in terms of resistance-curve (R-curve) behavior. While the crack initiation toughness declines with age, the more striking finding is that the crack-growth toughness declines even more significantly and is essentially absent in bone from donors exceeding 85 years in age. To explain such an age-induced deterioration in the toughness of bone, we evaluate its fracture properties at multiple length scales, specifically at the molecular and nano dimensions using pico-force atomic-force microscopy, nanoindentation and vibrational spectroscopies, at the microscale using electron microscopy and hard/soft x-ray computed tomography, and at the macroscale using R-curve measurements. We show that the reduction in crackgrowth toughness is associated primarily with a degradation in the degree of extrinsic toughening, in particular involving crack bridging, and that this occurs at relatively coarse size-scales in the range of tens to hundreds of micrometers. Finally, we briefly describe how specific clinical treatments, e.g., with steroid hormones to treat various inflammatory conditions, can prematurely damage bone, thereby reducing its fracture resistance, whereas regulating the level of the cytokine TGF-β can offer significant improvements in the stiffness, strength and toughness of bone, and as such may be considered as a therapeutic target to treat increased bone fragility induced by aging, drugs, and disease. *

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Fracture toughness of human femoral neck: effect of microstructure, composition, and age

Cited by 66 publications

References 31 publications

Quantitative 3D analysis of the canal network in cortical bone by micro‐computed tomography

Quantitative 3D analysis of the canal network in cortical bone by micro‐computed tomography

Structural analysis of cortical porosity applied to HR-pQCT data

Fracture, aging, and disease in bone

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