A new approach to analyzing regenerated bone quality in the mouse digit amputation model using semi-automatic processing of microCT data.
Bone pattern
Bone quality
Morphology
Regeneration
Semi-automatic
microCT
Journal
Bone
ISSN: 1873-2763
Titre abrégé: Bone
Pays: United States
ID NLM: 8504048
Informations de publication
Date de publication:
03 2021
03 2021
Historique:
received:
07
08
2020
revised:
04
11
2020
accepted:
25
11
2020
pubmed:
5
12
2020
medline:
6
7
2021
entrez:
4
12
2020
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 33276153
pii: S8756-3282(20)30564-0
doi: 10.1016/j.bone.2020.115776
pmc: PMC7906109
mid: NIHMS1665033
pii:
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
115776Subventions
Organisme : NIGMS NIH HHS
ID : P20 GM103629
Pays : United States
Informations de copyright
Copyright © 2020. Published by Elsevier Inc.
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