Characterization of Trabecular Bone Microarchitecture and Mechanical Properties Using Bone Surface Curvature Distributions.

IF 5 3区 医学 Q1 ENGINEERING, BIOMEDICAL
Pengwei Xiao, Caroline Schilling, Xiaodu Wang
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引用次数: 0

Abstract

Understanding bone surface curvatures is crucial for the advancement of bone material design, as these curvatures play a significant role in the mechanical behavior and functionality of bone structures. Previous studies have demonstrated that bone surface curvature distributions could be used to characterize bone geometry and have been proposed as key parameters for biomimetic microstructure design and optimization. However, understanding of how bone surface curvature distributions correlate with bone microstructure and mechanical properties remains limited. This study hypothesized that bone surface curvature distributions could be used to predict the microstructure as well as mechanical properties of trabecular bone. To test the hypothesis, a convolutional neural network (CNN) model was trained and validated to predict the histomorphometric parameters (e.g., BV/TV, BS, Tb.Th, DA, Conn.D, and SMI), geometric parameters (e.g., plate area PA, plate thickness PT, rod length RL, rod diameter RD, plate-to-plate nearest neighbor distance NNDPP, rod-to-rod nearest neighbor distance NNDRR, plate number PN, and rod number RN), as well as the apparent stiffness tensor of trabecular bone using various bone surface curvature distributions, including maximum principal curvature distribution, minimum principal curvature distribution, Gaussian curvature distribution, and mean curvature distribution. The results showed that the surface curvature distribution-based deep learning model achieved high fidelity in predicting the major histomorphometric parameters and geometric parameters as well as the stiffness tenor of trabecular bone, thus supporting the hypothesis of this study. The findings of this study underscore the importance of incorporating bone surface curvature analysis in the design of synthetic bone materials and implants.

利用骨表面曲率分布表征骨小梁微结构和力学性能
了解骨表面曲率对于推进骨材料设计至关重要,因为这些曲率在骨结构的机械行为和功能性方面发挥着重要作用。以往的研究表明,骨表面曲率分布可用于表征骨的几何形状,并被提议作为生物仿生微结构设计和优化的关键参数。然而,人们对骨表面曲率分布如何与骨微结构和机械性能相关的了解仍然有限。本研究假设骨表面曲率分布可用于预测骨小梁的微观结构和机械性能。为了验证这一假设,对卷积神经网络(CNN)模型进行了训练和验证,以预测组织形态计量参数(例如,BV/TV、BS、Tb.Th、DA、Conn.D 和 SMI)、几何参数(例如,骨板面积 PA、骨板厚度 PT、骨板厚度 SMI、骨板厚度 D 和骨板厚度 SMI)、板面积 PA、板厚 PT、杆长 RL、杆径 RD、板到板近邻距离 NNDPP、杆到杆近邻距离 NNDRR、板数 PN 和杆数 RN),以及骨小梁的表观刚度张量。结果表明,基于表面曲率分布的深度学习模型在预测骨小梁的主要组织形态参数和几何参数以及刚度阶差方面实现了高保真,从而支持了本研究的假设。本研究的结果强调了将骨表面曲率分析纳入合成骨材料和植入物设计的重要性。
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来源期刊
Journal of Functional Biomaterials
Journal of Functional Biomaterials Engineering-Biomedical Engineering
CiteScore
4.60
自引率
4.20%
发文量
226
审稿时长
11 weeks
期刊介绍: Journal of Functional Biomaterials (JFB, ISSN 2079-4983) is an international and interdisciplinary scientific journal that publishes regular research papers (articles), reviews and short communications about applications of materials for biomedical use. JFB covers subjects from chemistry, pharmacy, biology, physics over to engineering. The journal focuses on the preparation, performance and use of functional biomaterials in biomedical devices and their behaviour in physiological environments. Our aim is to encourage scientists to publish their results in as much detail as possible. Therefore, there is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. Several topical special issues will be published. Scope: adhesion, adsorption, biocompatibility, biohybrid materials, bio-inert materials, biomaterials, biomedical devices, biomimetic materials, bone repair, cardiovascular devices, ceramics, composite materials, dental implants, dental materials, drug delivery systems, functional biopolymers, glasses, hyper branched polymers, molecularly imprinted polymers (MIPs), nanomedicine, nanoparticles, nanotechnology, natural materials, self-assembly smart materials, stimuli responsive materials, surface modification, tissue devices, tissue engineering, tissue-derived materials, urological devices.
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