Automatic morphometric cartilage quantification in the medial tibial plateau from MRI for osteoarthritis grading

Dam, Erik B.; Folkesson, Jenny; Pettersen, P.C.; Christiansen, Claus

doi:10.1016/j.joca.2007.01.013

Cited by 37 publications

(37 citation statements)

References 43 publications

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“…Quantitative measures of surface curvature and joint incongruity have also been determined from MR images [37] and were observed to discriminate between subjects with various radiographic OA grades cross-sectionally at 0.2 T [38, 39]. Curvature estimates at different scales (at 0.2 T) were reported to be associated with the magnitude of cartilage loss longitudinally [40] and cartilage homogeneity (quantified by measuring entropy from the distribution of signal intensities in tibial cartilage from 0.2 T gradient echo images) was reported to discriminate between subjects without and with early radiographic OA [18].…”

Section: Cartilage Morphology Outcome Variablesand Regions Of Intementioning

confidence: 99%

“…The use of peripheral MRI scanners at lower field strengths potentially permits more widespread distribution of this technology, especially when access to high-field MRI is limited. Quantitative cartilage measurement at 0.2 T have also been proposed [18, 27, 28, 38–40] but have not been validated versus external standards or measurement at higher field strength. However, they were shown to display substantially larger precision errors than measurements performed at higher field strength.…”

Section: Imaging Protocols For Measurement Ofcartilage Morphology mentioning

confidence: 99%

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Quantitative Cartilage Imaging in Knee Osteoarthritis

Eckstein

Wirth

2011

Arthritis

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Quantitative measures of cartilage morphology (i.e., thickness) represent potentially powerful surrogate endpoints in osteoarthritis (OA). These can be used to identify risk factors of structural disease progression and can facilitate the clinical efficacy testing of structure modifying drugs in OA. This paper focuses on quantitative imaging of articular cartilage morphology in the knee, and will specifically deal with different cartilage morphology outcome variables and regions of interest, the relative performance and relationship between cartilage morphology measures, reference values for MRI-based knee cartilage morphometry, imaging protocols for measurement of cartilage morphology (including those used in the Osteoarthritis Initiative), sensitivity to change observed in knee OA, spatial patterns of cartilage loss as derived by subregional analysis, comparison of MRI changes with radiographic changes, risk factors of MRI-based cartilage loss in knee OA, the correlation of MRI-based cartilage loss with clinical outcomes, treatment response in knee OA, and future directions of the field.

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Section: Cartilage Morphology Outcome Variablesand Regions Of Intementioning

confidence: 99%

Section: Imaging Protocols For Measurement Ofcartilage Morphology mentioning

confidence: 99%

Quantitative Cartilage Imaging in Knee Osteoarthritis

Eckstein

Wirth

2011

Arthritis

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“…If manual labor is connected with the analysis and quantification of MRI data in clinical studies, one more cost factor is introduced. Recently, we published a fully automatic framework for cartilage quantification (27), with disease markers such as volume, thickness, and surface area showing significant differences between healthy and OA populations which suggests that low-field MRI analysis can become a useful tool in clinical studies of OA. In that study as well, we compare findings from low-field MRI data with the Kellgren-Lawrence index which is a well-established score for signs of OA.…”

Section: The Mri Sequencementioning

confidence: 99%

Automatic quantification of local and global articular cartilage surface curvature: Biomarkers for osteoarthritis?

Folkesson

Dam

Olsen

et al. 2008

Magnetic Resonance in Med

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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% (

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“…Slices were acquired in non-load bearing supine position. For more detail on the population and the scans, see [21].…”

Section: Population and Image Acquisitionmentioning

confidence: 99%

Surface smoothness: cartilage biomarkers for knee OA beyond the radiologist

Tummala

Dam

2010

SPIE Proceedings

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Fully automatic imaging biomarkers may allow quantification of patho-physiological processes that a radiologist would not be able to assess reliably. This can introduce new insight but is problematic to validate due to lack of meaningful ground truth expert measurements. Rather than quantification accuracy, such novel markers must therefore be validated against clinically meaningful end-goals such as the ability to allow correct diagnosis. We present a method for automatic cartilage surface smoothness quantification in the knee joint. The quantification is based on a curvature flow method used on tibial and femoral cartilage compartments resulting from an automatic segmentation scheme. These smoothness estimates are validated for their ability to diagnose osteoarthritis and compared to smoothness estimates based on manual expert segmentations and to conventional cartilage volume quantification. We demonstrate that the fully automatic markers eliminate the time required for radiologist annotations, and in addition provide a diagnostic marker superior to the evaluated semi-manual markers.

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Automatic morphometric cartilage quantification in the medial tibial plateau from MRI for osteoarthritis grading

Cited by 37 publications

References 43 publications

Quantitative Cartilage Imaging in Knee Osteoarthritis

Quantitative Cartilage Imaging in Knee Osteoarthritis

Automatic quantification of local and global articular cartilage surface curvature: Biomarkers for osteoarthritis?

Surface smoothness: cartilage biomarkers for knee OA beyond the radiologist

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