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Age-related Changes of Thigh Muscle Fat Infiltration in Subjects of the OAI MRI Cohort with and without Radiographic Osteoarthritis

*Yu, H.J. PhD, **Tan, C. MS, **Yan, Z. MS, *Miller C.G. PhD, *Fuerst T. PhD, Engelke K. PhD, **Metaxas, D. PhD * BioClinica, Inc., Newtown USA and Germany  ** Rutgers University, Piscataway USA

BACKGROUND
Increasing age is a known risk factor for knee osteoarthritis (OA), which results in increased muscle weakness and decreased mobility. Inter- (inter-MAT) and intra-muscular adipose tissues (intra-MAT) are defined as adipose tissue visible between muscle groups and muscle fibers, respectively. Volume and distribution of thigh muscle tissue and of inter- and intra-MAT reflect adverse metabolic effects and muscle function.

OBJECTIVES
1. Propose a robust and highly automated algorithm for the quantitative volume assessment of thigh muscle and inter- and intra-MAT; reduce processing time and operator-dependent precision errors of traditional manual analyses, especially of 3D datasets.
2. Apply this technique to the Osteoarthritis Initiative (OAI) MRI data to compare changes of thigh muscle fat infiltration in subjects with and without non-radiographic osteoarthritis.

METHODS
An automatic quantification framework consisting of 5 major steps was developed (Figure 1): 1) intensity inhomogeneity correction; 2) subcutaneous adipose tissue (SAT) removal; 3) tissue labeling for bone, marrow, fat and muscle; 4) inter- and intra-MAT classification; 5) measurement of volume. Figure 2 shows T1 weighted MRI images of four different subjects together with the segmentation results.

The OAI database was queried for subjects with the bilateral KLG scores and mid-thigh axial T1-weighted MRIs (5 mm slice thickness) at baseline. Subjects with bilateral KLG score 0 or 1 were labeled as non-ROA; those with KLG score >2 as ROA.  355 out of 4,796 participants were identified, 197 non-ROA subjects (65 male, 132 female; age: 45-77) and 158 ROA subjects (63 male, 95 female; age: 45-79). The left leg was processed after image QC. Volume (15 slices) was calculated for total thigh muscle, SAT, and inter- and intra-MAT.

RESULTS
Correlations with age for muscle volume normalized for BMI, for the sum of inter- and intra-MAT and separately for intra-MAT both normalized for muscle volume are shown in Fig 3.

CONCLUSION
In both groups, muscle volume normalized to BMI decreased with age while adipose tissue volume increased. Differences in age related changes between subjects with and without ROA were inconsistent, although numerically correlations with age were slightly higher in the ROA group. The proposed framework provides a fairly automated approach for quantitative thigh tissue assessment in T1 weighted MRI images. Ongoing development efforts include reduction of operator interactions to correct segmentation defects. It will also be of interest to assess individual muscles separately.

SPONSOR: BioClinica Inc.
DICLOSURE STATEMENT: None; ACKNOWLEDGMENT: None
CORRESPONDENCE ADDRESS: huijing.yu@bioclinica.com

A strong agreement was shown between BSI and TBM (ρ ≥ 0.97 with lower bound of 95%CI  ≥ 0.96, see Fig. 1) and led to the same volume changes in whole brain and lateral ventricles in ROSAS NC, MCI-nc, MCI-c and AD. When assessing volume changes in whole brain, lateral ventricles and hippocampus (see Fig. 2), ROSAS controls showed less atrophy than ADNI2 controls, which is likely explained by stricter inclusion criteria (MMSE ≥ 26 for ROSAS and ≥ 24 for ADNI2). In addition, MCI subgroups always followed the same pattern of atrophy rate for all structures: EMCI (ADNI2) < MCI-nc (ROSAS) < LMCI (ADNI2) < MCI-c (ROSAS), while ROSAS and ADNI2 AD were similar. Baseline HCV was weakly correlated to brain atrophy and moderately correlated to hippocampal atrophy (see Fig. 3). When looking at MCI subjects in particular, a cut-off HCV of 7000 mm3 was significantly associated with conversion to AD (Table 3). The ROSAS cohort provides additional reference values for the design of future clinical trials.

REFERENCES
Belaroussi et al., Labeling of brain MRI images using atlas propagation and classification-based nearest-neighbor transform, Poster session, AAN 2011

Leung et al., Consistent multi-time-point brain atrophy estimation from the boundary shift integral, Neuroimage 2012

Freeborough et al., The boundary shift integral: an accurate and robust measure of cerebral volume changes from registered repeat MRI, IEEE Trans Med Imaging, 1997

Vercauteren et al., Symmetric log-domain diffeomorphic Registration: a demons-based approach, Med Image Comput Comput Assist Interv, 2008

Belaroussi et al., Multi-Atlas hippocampus segmentation refined with intensity-based tissue classification, Poster session, AAIC 2012

Barnes et al., Automatic calculation of hippocampal atrophy rates using a hippocampal template and the boundary shift integral, Neurobiology of aging, 2006

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