Longitudinal changes in intermuscular fat volume and quadriceps muscle volume in the thighs of women with knee osteoarthritis

Beattie, Karen A.; MacIntyre, Norma J.; Ramadan, Khaled; Inglis, Dean; Maly, Monica R.

doi:10.1002/acr.20628

Cited by 52 publications

(47 citation statements)

References 38 publications

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“…A reduction in quadriceps (but not hamstring or adductor) CSAs and isometric strength was observed in frequently painful versus contralateral painless knees with the same radiographic stage,123 but no side-difference between knees with and without JSN 124. Two-year rates of change in quadriceps CSAs were similar in participants with and without radiographic OA,121 and in knees with and without radiographic JSN 124. However, OA knees with longitudinal JSW and cartilage loss above a defined threshold (structural progressors) displayed greater concurrent quadriceps CSA loss (−2.8±7.9%) than matched, non-progressive controls (−1.8±6.8%); further change in CSAs was less variable than that in muscle strength 125.…”

Section: Imaging Publications From Full Cohort Releasesmentioning

confidence: 80%

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Imaging research results from the Osteoarthritis Initiative (OAI): a review and lessons learned 10 years after start of enrolment

Eckstein

Kwoh

Link³

2014

Ann Rheum Dis

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The Osteoarthritis Initiative (OAI) is a multicentre, prospective, observational, cohort study of knee osteoarthritis (OA) that began recruitment in 2004. The OAI provides public access to clinical and image data, enabling researchers to examine risk factors/predictors and the natural history of knee OA incidence and progression, and the qualification of imaging and other biomarkers. In this narrative review, we report imaging findings and lessons learned 10 years after enrolment has started. A literature search for full text articles published from the OAI was performed up to 31 December 2013 using Pubmed and the OAI web page. We summarise the rationale, design and imaging protocol of the OAI, and the history of OAI publications. We review studies from early partial, and later full OAI public data releases. The latter are structured by imaging method and tissue, reviewing radiography and then MRI findings on cartilage morphology, cartilage lesions and composition (T2), bone, meniscus, muscle and adipose tissue. Finally, analyses directly comparing findings from MRI and radiography are summarised. Ten years after the first participants were enrolled and first papers published, the OAI has become an invaluable resource to the OA research community. It has fuelled novel methodological approaches of analysing images, and has provided a wealth of information on OA pathophysiology. Continued collection and public release of long-term observations will help imaging measures to gain scientific and regulatory acceptance as 'prognostic' or 'efficacy of intervention' biomarkers, potentially enabling shorter and more efficient clinical trials that can test structure-modifying therapeutic interventions (NCT00080171).

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Section: Imaging Publications From Full Cohort Releasesmentioning

confidence: 80%

“…Semiautomated segmentation algorithms for thigh muscles119–121 and interstitial fat121 122 have been proposed (figure 2G-–I), and muscle cross-sectional areas (CSA) were confirmed to correlate with isometric strength123 124 (table 3). …”

Section: Imaging Publications From Full Cohort Releasesmentioning

confidence: 97%

Imaging research results from the Osteoarthritis Initiative (OAI): a review and lessons learned 10 years after start of enrolment

Eckstein

Kwoh

Link³

2014

Ann Rheum Dis

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“…Where large signal contrasts are located, pools of water will be restricted. To date, this algorithm has only been applied to magnetic resonance imaging muscle scans (12,17), but the precision of the technique in different cohorts has yet to be demonstrated using pQCT images.…”

Section: Introductionmentioning

confidence: 99%

“…Both muscle cross-sectional area (MCSA) and MD can be computed with the manufacturer Stratec software (Orthometrix Inc., White Plains, NY) using a linear attenuation thresholdguided edge detection algorithm. Accurate representation of these features depends on precise delineation of the musclesubcutaneous fat boundary, which can be guided by the fascia line enveloping the leg muscles (12). In some cases, this boundary is not apparent and in more extreme cases, it is obscured by fatty infiltration into muscle.…”

Section: Introductionmentioning

confidence: 99%

Improving Reliability of pQCT-Derived Muscle Area and Density Measures Using a Watershed Algorithm for Muscle and Fat Segmentation

Wong

Hummel

Moore

et al. 2015

Journal of Clinical Densitometry

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In peripheral quantitative computed tomography scans of the calf muscles, segmentation of muscles from subcutaneous fat is challenged by muscle fat infiltration. Threshold-based edge detection segmentation by manufacturer software fails when muscle boundaries are not smooth. This study compared the test-retest precision error for muscle-fat segmentation using the threshold-based edge detection method vs manual segmentation guided by the watershed algorithm. Three clinical populations were investigated: younger adults, older adults, and adults with spinal cord injury (SCI). The watershed segmentation method yielded lower precision error (1.18%-2.01%) and higher (p<0.001) muscle density values (70.2±9.2 mg/cm3) compared with threshold-based edge detection segmentation (1.77%-4.06% error, 67.4±10.3 mg/cm3). This was particularly true for adults with SCI (precision error improved by 1.56% and 2.64% for muscle area and density, respectively). However, both methods still provided acceptable precision with error well under 5%. Bland-Altman analyses showed that the major discrepancies between the segmentation methods were found mostly among participants with SCI where more muscle fat infiltration was present. When examining a population where fatty infiltration into muscle is expected, the watershed algorithm is recommended for muscle density and area measurement to enable the detection of smaller change effect sizes.

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“…Therefore, in this paper, we present a new robust and automated framework to perform 3D segmentation of inter-MAT, intra-MAT and muscle tissues in the thigh using volumetric mid-thigh axial MR/CT images and assess the fraction of fat to muscle. Since the primary anatomical tissues (muscle, fat/marrow, bone) in the thigh region can be classified by their pixel intensity [10], we model the tissues in the thigh region using Gaussian mixture model (GMM). Furthermore, we estimate unknown parameters by a variational Bayesian inference [11].…”

Section: Introductionmentioning

confidence: 99%

An Automated and Robust Framework for Quantification of Muscle and Fat in the Thigh

Yan

Zhang

Belaroussi

et al. 2014

2014 22nd International Conference on Pattern Recognition

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The tissue quantification in the thigh (e.g. crosssectional areas of adipose tissue and muscle) is important, since their quantities reflect adverse metabolic effects and muscle function. Traditional manual analysis is time-consuming and operator-dependent, especially in the case of multi-slices or 3D datasets. In clinical trials, there are a large amount of datasets acquired from magnetic resonance imaging (MRI) or X-ray computed tomography (CT) that requires automatic labeling of individual tissues. Since most segmentation algorithms are not suited for different modalities, we present an automatic and robust framework for the quantitative assessment of muscle and fat tissues on 3D MR or CT data. In our framework, a variational Bayesian Gaussian mixture model is used to cluster regions of interest in images into adipose tissues (fat and marrow), muscle, bone and background. The identification of each cluster is based on marrow detection. Furthermore, we use a combination of parametric and geodesic active contour models to distinguish different adipose tissues in 3D images. To validate our proposed framework, we have conducted preliminary experiments on five volumetric mid-thigh axial datasets of MR and CT images from clinical trials.

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Longitudinal changes in intermuscular fat volume and quadriceps muscle volume in the thighs of women with knee osteoarthritis

Cited by 52 publications

References 38 publications

Imaging research results from the Osteoarthritis Initiative (OAI): a review and lessons learned 10 years after start of enrolment

Imaging research results from the Osteoarthritis Initiative (OAI): a review and lessons learned 10 years after start of enrolment

Improving Reliability of pQCT-Derived Muscle Area and Density Measures Using a Watershed Algorithm for Muscle and Fat Segmentation

An Automated and Robust Framework for Quantification of Muscle and Fat in the Thigh

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