Hoffseth Kevin F, Simkin Jennifer, Busse Emily, Stewart Kennon, Watt James, Chapple Andrew, Hargrove Aaron, Sammarco Mimi C
Department of Biological & Agricultural Engineering, Louisiana State University, 149 E.B. Doran Building, Baton Rouge, LA 70803, USA.
Department of Orthopaedic Surgery, Louisiana State University Health Sciences Center, New Orleans, 533 Bolivar Street, New Orleans, LA 70112, USA.
Bone. 2021 Mar;144:115776. doi: 10.1016/j.bone.2020.115776. Epub 2020 Dec 2.
Bone regeneration is a critical area of research impacting treatment of diseases such as osteoporosis, age-related decline, and orthopaedic implants. A crucial question in bone regeneration is that of bone architectural quality, or how "good" is the regenerated bone tissue structurally? Current methods address typical long bone architecture, however there exists a need for improved ability to quantify structurally relevant parameters of bone in non-standard bone shapes. Here we present a new analysis approach based on open-source semi-automatic methods combining image processing, solid modeling, and numerical calculations to analyze bone tissue at a more granular level using μCT image data from a mouse digit model of bone regeneration. Examining interior architecture, growth patterning, spatial mineral content, and mineral density distribution, these methods are then applied to two types of 6-month old mouse digits - 1) those prior to amputation injury (unamputated) and 2) those 42 days after amputation when bone has regenerated. Results show regenerated digits exhibit increased inner void fraction, decreased patterning, different patterns of spatial mineral distribution, and increased mineral density values when compared to unamputated bone. Our approach demonstrates the utility of this new analysis technique in assessment of non-standard bone models, such as the regenerated bone of the digit, and aims to bring a deeper level of analysis with an open-source, integrative platform to the greater bone community.
骨再生是一个关键的研究领域，对骨质疏松症、与年龄相关的衰退以及骨科植入物等疾病的治疗具有重要影响。骨再生中的一个关键问题是骨结构质量问题，即再生骨组织在结构上有多“好”？目前的方法针对的是典型的长骨结构，然而，对于量化非标准骨形状中与结构相关的骨参数，仍需要提高能力。在此，我们提出一种基于开源半自动方法的新分析方法，该方法结合图像处理、实体建模和数值计算，使用来自骨再生小鼠指骨模型的μCT图像数据，在更精细的层面上分析骨组织。通过检查内部结构、生长模式、空间矿物质含量和矿物质密度分布，这些方法随后应用于两种6个月大的小鼠指骨——1）截肢损伤前的指骨（未截肢）和2）截肢后42天骨已再生的指骨。结果表明，与未截肢的骨相比，再生指骨的内部孔隙率增加、模式减少、空间矿物质分布模式不同且矿物质密度值增加。我们的方法证明了这种新分析技术在评估非标准骨模型（如指骨的再生骨）中的实用性，旨在通过一个开源的综合平台为更广泛的骨科学界带来更深入的分析水平。
