Literature DB >> 26455998

Surface roughness and thickness analysis of contrast-enhanced articular cartilage using mesh parameterization.

T Maerz1, M D Newton2, H W T Matthew3, K C Baker4.   

Abstract

OBJECTIVE: Articular cartilage (AC) morphology is an important metric for characterizing degeneration. We propose a novel morphologic analysis using mesh parameterization, enabling the use of surface roughness and thickness metrics to characterize degeneration in a rodent model of post-traumatic osteoarthritis.
METHODS: Six rats underwent anterior cruciate ligament transection (ACL-T) and six were controls (Control). At 4-weeks, femora and tibiae were harvested and imaged using contrast-enhanced micro-computed tomography (μCT). Cartilage surfaces were manually outlined, and 2-dimensional thickness maps were generated using mesh parameterization and analyzed by thickness and surface roughness (Sa). The parameterization technique was validated against the direct distance transform (DDT) and histologic AC thickness from sagittal Safranin-O/Fast-Green sections. Parameterization and DDT measurements were also validated using known, virtual shapes with zero, one, and two planes of curvature.
RESULTS: Parameterization had 0.00-6.26% error and DDT had 5.06-12.02% error in determining thicknesses of known shapes. Parameterization thickness correlated highly to DDT thickness (femur: r = 0.978, P < 0.001; tibia: r = 0.992, P < 0.001) and histologic thickness (femur: r = 0.952, P < 0.001; tibia: r = 0.959, P < 0.001). Thickness maps enabled visualization and quantification of AC degeneration. ACL-T samples displayed general thickening of cartilage, with adjacent regions of thickening and thinning on the medial femoral condyle. Compared to Control, ACL-T thickness was higher in the whole femur, whole tibia, and all compartments and sub-compartments. Sa was higher in the whole femur and medial and lateral condyle, and the whole tibia and medial and lateral plateau. The largest increases in Sa were observed on the medial femoral condyle.
CONCLUSIONS: Cartilage analysis using parameterization effectively characterized early degeneration in AC, including sub-compartmental thickening/thinning, and is a powerful tool for assessing degeneration in preclinical osteoarthritis.
Copyright © 2015 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Cartilage morphology; Contrast-enhanced μCT; Image analysis; Mesh parameterization; Post-traumatic osteoarthritis; Surface roughness

Mesh:

Substances:

Year:  2015        PMID: 26455998     DOI: 10.1016/j.joca.2015.09.006

Source DB:  PubMed          Journal:  Osteoarthritis Cartilage        ISSN: 1063-4584            Impact factor:   6.576


  4 in total

1.  Quantification of Cartilage Surface Degeneration by Curvature Analysis Using 3D Scanning in a Rabbit Model.

Authors:  Dawei Liang; Tomohiro Onodera; Masanari Hamasaki; Ryosuke Hishimura; Kentaro Homan; Liang Xu; Yuan Tian; Satoshi Kanai; Norimasa Iwasaki
Journal:  Cartilage       Date:  2021-11-20       Impact factor: 3.117

2.  Comparison of Different Approaches for Measuring Tibial Cartilage Thickness.

Authors:  Jennifer Maier; Marianne Black; Serena Bonaretti; Bastian Bier; Bjoern Eskofier; Jang-Hwan Choi; Marc Levenston; Garry Gold; Rebecca Fahrig; Andreas Maier
Journal:  J Integr Bioinform       Date:  2017-07-28

3.  Quantifying Complex Micro-Topography of Degenerated Articular Cartilage Surface by Contrast-Enhanced Micro-Computed Tomography and Parametric Analyses.

Authors:  Tuomo Ylitalo; Mikko A J Finnilä; Harpal K Gahunia; Sakari S Karhula; Heikki Suhonen; Maarit Valkealahti; Petri Lehenkari; Edward Haeggström; Kenneth P H Pritzker; Simo Saarakkala; Heikki J Nieminen
Journal:  J Orthop Res       Date:  2019-03-05       Impact factor: 3.494

4.  Impact of Controlling Abnormal Joint Movement on the Effectiveness of Subsequent Exercise Intervention in Mouse Models of Early Knee Osteoarthritis.

Authors:  Yuichiro Oka; Kenji Murata; Takuma Kano; Kaichi Ozone; Kohei Arakawa; Takanori Kokubun; Naohiko Kanemura
Journal:  Cartilage       Date:  2019-11-13       Impact factor: 3.117

  4 in total

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