Automatic quantification of local and global articular cartilage surface curvature: biomarkers for osteoarthritis?
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Automatic quantification of local and global articular cartilage surface curvature : biomarkers for osteoarthritis? / Folkesson, Jenny; Dam, Erik B; Olsen, Ole F; Karsdal, Morten A; Pettersen, Paola C; Christiansen, Claus.
In: Magnetic Resonance in Medicine, Vol. 59, No. 6, 06.2008, p. 1340-6.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Automatic quantification of local and global articular cartilage surface curvature
T2 - biomarkers for osteoarthritis?
AU - Folkesson, Jenny
AU - Dam, Erik B
AU - Olsen, Ole F
AU - Karsdal, Morten A
AU - Pettersen, Paola C
AU - Christiansen, Claus
N1 - Copyright (c) 2008 Wiley-Liss, Inc.
PY - 2008/6
Y1 - 2008/6
N2 - The objective of this study was to quantitatively assess the surface curvature of the articular cartilage from low-field magnetic resonance imaging (MRI) data, and to investigate its role in populations with varying radiographic signs of osteoarthritis (OA), cross-sectionally and longitudinally. The curvature of the articular surface of the medial tibial compartment was estimated both on fine and coarse scales using two different automatic methods which are both developed from an automatic 3D segmentation algorithm. Cross-sectionally (n=288), the surface curvature for both the fine- and coarse-scale estimates were significantly higher in the OA population compared with the healthy population, with P<0.001 and P<0.001, respectively. For the longitudinal study (n=245), there was a significant increase in fine-scale curvature for healthy and borderline OA populations (P<0.001), and in coarse-scale curvature for severe OA populations (P<0.05). Fine-scale curvature could predict progressors using the estimates of those healthy at baseline (P<0.001). The inter-scan precision was 2.2 and 6.5 (mean CV) for the fine- and coarse scale curvature measures, respectively. The results showed that quantitative curvature estimates from low-field MRI at different scales could potentially become biomarkers targeted at different stages of OA.
AB - The objective of this study was to quantitatively assess the surface curvature of the articular cartilage from low-field magnetic resonance imaging (MRI) data, and to investigate its role in populations with varying radiographic signs of osteoarthritis (OA), cross-sectionally and longitudinally. The curvature of the articular surface of the medial tibial compartment was estimated both on fine and coarse scales using two different automatic methods which are both developed from an automatic 3D segmentation algorithm. Cross-sectionally (n=288), the surface curvature for both the fine- and coarse-scale estimates were significantly higher in the OA population compared with the healthy population, with P<0.001 and P<0.001, respectively. For the longitudinal study (n=245), there was a significant increase in fine-scale curvature for healthy and borderline OA populations (P<0.001), and in coarse-scale curvature for severe OA populations (P<0.05). Fine-scale curvature could predict progressors using the estimates of those healthy at baseline (P<0.001). The inter-scan precision was 2.2 and 6.5 (mean CV) for the fine- and coarse scale curvature measures, respectively. The results showed that quantitative curvature estimates from low-field MRI at different scales could potentially become biomarkers targeted at different stages of OA.
KW - Adult
KW - Aged
KW - Aged, 80 and over
KW - Biomarkers
KW - Cartilage, Articular
KW - Cross-Sectional Studies
KW - Female
KW - Humans
KW - Image Processing, Computer-Assisted
KW - Longitudinal Studies
KW - Magnetic Resonance Imaging
KW - Male
KW - Middle Aged
KW - Osteoarthritis, Knee
KW - Reproducibility of Results
KW - Surface Properties
KW - Journal Article
U2 - 10.1002/mrm.21560
DO - 10.1002/mrm.21560
M3 - Journal article
C2 - 18506845
VL - 59
SP - 1340
EP - 1346
JO - Magnetic Resonance in Medicine
JF - Magnetic Resonance in Medicine
SN - 0740-3194
IS - 6
ER -
ID: 187555030