Novel magnetic resonance technique for characterizing mesoscale structure of trabecular bone

Nguyen, Chantal; Schlesinger, Kimberly J.; James, Timothy W.; James, Kristin M.; Sah, Robert L.; Masuda, Koichi; Carlson, Jean M.

doi:10.1098/rsos.180563

Cited by 5 publications

(5 citation statements)

References 26 publications

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“…In clinical applications, high-resolution in vivo measurements are increasingly appreciated as necessary for the evaluation of bone fragility. Innovative techniques for high-resolution data acquisition of fine tissue structure are already in development [39], as well as techniques for in vivo mechanical assessment such as reference point indentation [40]. The methods developed in this paper aim to complement advances in medical diagnostic measurements by identifying biomarkers that may be useful to target using clinical procedures.…”

Section: Discussionmentioning

confidence: 99%

Network models for characterization of trabecular bone

Mondal

Nguyen

et al. 2019

Phys. Rev. E

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Trabecular bone is a lightweight, compliant material organized as a web of struts and rods (trabeculae) that erode with age and the onset of bone diseases like osteoporosis, leading to increased fracture risk. The traditional diagnostic marker of osteoporosis, bone mineral density (BMD), has been shown in ex vivo experiments to correlate poorly with fracture resistance when considered on its own, while structural features in conjunction with BMD can explain more of the variation in trabecular bone strength. We develop a network-based model of trabecular bone by creating graphs from micro-CT images of human bone, with weighted links representing trabeculae and nodes representing branch points. These graphs enable calculation of quantitative network metrics to characterize trabecular structure. We also create finite element models of the networks in which each link is represented by a beam, facilitating analysis of the mechanical response of the bone samples to simulated loading. We examine the structural and mechanical properties of trabecular bone at the scale of individual trabeculae (of order 0.1 mm) and at the scale of selected volumes of interest (approximately a few mm), referred to as VOIs. At the VOI scale, we find significant correlations between the stiffness of VOIs and ten different structural metrics. Individually, the volume fraction of each VOI is most strongly correlated to the stiffness of the VOI. We use multiple linear regression to identify the smallest subset of variables needed to capture the variation in stiffness. In a linear fit, we find that node degree, weighted node degree, Z-orientation, weighted Z-orientation, trabecular spacing, link length, and the number of links are the structural metrics that are most significant (p < 0.05) in capturing the variation of stiffness in trabecular networks.

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Section: Discussionmentioning

confidence: 99%

Network models for characterization of trabecular bone

Mondal

Nguyen

et al. 2019

Phys. Rev. E

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“…Radiomics, a form of MR image post‐processing that highlights textures in the image, has been previously reported 12‐16 . However, no degree of image analysis will produce textural detail at resolution finer than what was initially acquired—higher resolution raw data are required to increase measurement resolution.…”

Section: Theorymentioning

confidence: 99%

“…Radiomics, a form of MR image post-processing that highlights textures in the image, has been previously reported. [12][13][14][15][16] However, no degree of image analysis will produce textural detail at resolution finer than what was initially acquiredhigher resolution raw data are required to increase measurement resolution. Currently, resolution achievable in a clinical setting by MRI or CT is limited by blurring resulting from unavoidable involuntary patient motion during the multiple excitations required for to generate an image.…”

Section: Theorymentioning

confidence: 99%

MR method for measuring microscopic histologic soft tissue textures

Sonn

Fan

Kunder

et al. 2021

Magnetic Resonance in Med

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Provide a direct, non-invasive diagnostic measure of microscopic tissue texture in the size scale between tens of microns and the much larger scale measurable by clinical imaging. This paper presents a method and data demonstrating the ability to measure these microscopic pathologic tissue textures (histology) in the presence of subject motion in an MR scanner. This size range is vital to diagnosing a wide range of diseases. Theory/Methods: MR micro-Texture (MRµT) resolves these textures by a combination of measuring a targeted set of k-values to characterize texture-as in diffraction analysis of materials, performing a selective internal excitation to isolate a volume of interest (VOI), applying a high k-value phase encode to the excited spins in the VOI, and acquiring each individual k-value data point in a single excitation-providing motion immunity and extended acquisition time for maximizing signal-to-noise ratio. Additional k-value measurements from the same tissue can be made to characterize the tissue texture in the VOI-there is no need for these additional measurements to be spatially coherent as there is no image to be reconstructed. This method was applied to phantoms and tissue specimens including human prostate tissue. Results: Data demonstrating resolution <50 µm, motion immunity, and clearly differentiating between normal and cancerous tissue textures are presented. Conclusion: The data reveal textural differences not resolvable by standard MR imaging. As MRµT is a pulse sequence, it is directly translatable to MRI scanners currently in clinical practice to meet the need for further improvement in cancer imaging.

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“…This is primarily because there is a lack of suitable medical tools available in the market to directly leverage these results. On the contrary, macroscale and, especially, mesoscale tissue levels are deepened in clinic thanks to the proper technologies, such as dual X-ray absorptiometry (DXA), high resolution computed tomography (CT) and magnetic resonance imaging (MRI) [4][5][6]. In light of the potential clinical implications, researchers are diligently directing their efforts towards augmenting the comprehension of the morphological arrangement and mechanical response of bone tissue at the mesoscale.…”

Section: Introductionmentioning

confidence: 99%

Effect of trabecular architectures on the mechanical response in osteoporotic and healthy human bone

Bregoli,

Biffi,

Tuissi

et al. 2024

Med Biol Eng Comput

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Research at the mesoscale bone trabeculae arrangement yields intriguing results that, due to their clinical resolution, can be applied in clinical field, contributing significantly to the diagnosis of bone-related diseases. While the literature offers quantitative morphometric parameters for a thorough characterization of the mesoscale bone network, there is a gap in understanding relationships among them, particularly in the context of various bone pathologies. This research aims to bridge these gaps by offering a quantitative evaluation of the interplay among morphometric parameters and mechanical response at mesoscale in osteoporotic and non-osteoporotic bones. Bone mechanical response, dependent on trabecular arrangement, is defined by apparent stiffness, computationally calculated using the Gibson-Ashby model. Key findings indicate that: (i) in addition to bone density, measured using X-ray absorptiometry, trabecular connectivity density, trabecular spacing and degree of anisotropy are crucial parameters for characterize osteoporosis state; (ii) apparent stiffness values exhibit strong correlations with bone density and connectivity density; (iii) connectivity density and degree of anisotropy result the best predictors of mechanical response. Despite the inherent heterogeneity in bone structure, suggesting the potential benefit of a larger sample size in the future, this approach presents a valuable method to enhance discrimination between osteoporotic and non-osteoporotic samples. Graphical Abstract

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Novel magnetic resonance technique for characterizing mesoscale structure of trabecular bone

Cited by 5 publications

References 26 publications

Network models for characterization of trabecular bone

Network models for characterization of trabecular bone

MR method for measuring microscopic histologic soft tissue textures

Effect of trabecular architectures on the mechanical response in osteoporotic and healthy human bone

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