A Valid and Precise Semiautomated Method for Quantifying Intermuscular Fat Intramuscular Fat in Lower Leg Magnetic Resonance Images.

The accumulation of INTERmuscular fat and INTRAmuscular fat (IMF) has been a hallmark of individuals with diabetes, those with mobility impairments such as spinal cord injuries and is known to increase with aging. An elevated amount of IMF has been associated with fractures and frailty, but the imprecision of IMF measurement has so far limited the ability to observe more consistent clinical associations. Magnetic resonance imaging has been recognized as the gold standard for portraying these features, yet reliable methods for quantifying IMF on magnetic resonance imaging is far from standardized. Previous investigators used manual segmentation guided by histogram-based region-growing, but these techniques are subjective and have not demonstrated reliability. Others applied fuzzy classification, machine learning, and atlas-based segmentation methods, but each is limited by the complexity of implementation or by the need for a learning set, which must be established each time a new disease cohort is examined. In this paper, a simple convergent iterative threshold-optimizing algorithm was explored. The goal of the algorithm is to enable IMF quantification from plain fast spin echo (FSE) T1-weighted MR images or from water-saturated images. The algorithm can be programmed into Matlab easily, and is semiautomated, thus minimizing the subjectivity of threshold-selection. In 110 participants from 3 cohort studies, IMF area measurement demonstrated a high degree of reproducibility with errors well within the 5% benchmark for intraobserver, interobserver, and test-retest analyses; in contrast to manual segmentation which already yielded over 20% error for intraobserver analysis. This algorithm showed validity against manual segmentations (r > 0.85). The simplicity of this technique lends itself to be applied to fast spin echo images commonly ordered as part of standard of care and does not require more advanced fat-water separated images.

Bone Marrow and Muscle Fat Infiltration Are Correlated among Postmenopausal Women With Osteoporosis: The AMBERS Cohort Study.

Bone and muscle have shown to interact, but little is known about fat within bone and muscle. Clinical studies have isolated fat within bone and muscle using MRI. In this cross-sectional study, we hypothesized that bone marrow adiposity and muscle adiposity are related and that this relationship is associated with osteoporosis. Postmenopausal women aged 60 to 85 years were recruited as part of the Appendicular Muscle and Bone Extension Research Study (AMBERS). Participants completed dual-energy X-ray absorptiometry (DXA) of the hip and spine to diagnose osteoporosis. Muscle adiposity was measured with MRI at the 66% site of the leg. Fat segmentation was achieved using a semi-automated iterative threshold-optimizing algorithm (error < 5%). Peripheral quantitative computed tomography measured marrow density of the 4% distal tibia (surrogate for marrow fat) by threshold-based, edge-detection segmentations and by examining residuals from trabecular bone density regressed on trabecular tissue mineral density. Muscle adiposity from MRI was regressed on marrow density using linear regression. Models were further examined with an interaction with osteoporosis status. Among 312 women (aged 75.4 ± 5.9 years, body mass index [BMI] 29.5 ± 5.7 kg/m2 ), a larger amount of muscle fat was associated with lower marrow density at the 66% mid-tibia (B = 84.08 [27.56], p = 0.002) and at the 4% distal tibia (B = 129.17 [55.96], p = 0.022) after accounting for age, height, weight, average daily energy expenditure, hypertension, and diabetes. Interactions of this relationship with osteoporosis status were also significant. Upon probing these interactions, the relationships were significant only in women with osteoporosis but not in those without osteoporosis. Fat from bone marrow and muscle may be related to one another through the same phenomenon, which is likely also responsible for osteoporosis, but independent of hypertension and diabetes. More research should focus on the potential abnormalities in muscle and bone fat metabolism and mesenchymal cell commitment to fat within patients with osteoporosis. © 2019 American Society for Bone and Mineral Research.