Assessments of 3D geometry, bone density distribution and bone strength improve hip fracture risk prediction but currently require the use of computed tomography (CT). 2D-to-3D statistical shape and appearance model (SSAM) reconstructions from dual-energy X-ray absorptiometry (DXA) projections have been proposed as surrogates for CT-based models. Several studies have used 3D-DXAs reconstructed by 3D-Shaper’s software method, but no study has independently cross-validated 3D-DXAs on
in vivo
clinical images. The first aim of this study was to evaluate the extent 3D-DXA is applicable across a diverse population. 120 paired DXA and CT images from three ethnicities (age: 20–85; aBMD:0.611–1.214 g/cm
2
) were analysed for differences in bone volume (BV), bone mineral content, vertex-to-surface distance, volumetric bone mineral density (vBMD) distribution and predicted bone strength. No subject or parameter groups showed outlier behaviour, as correlations for all results were above
R
2
> 0.76. However, differences were observed for volumetric measurements (BV: slope = 0.89, bias = 9.73 cm
3
; vBMD: slope = 0.88, bias = 6.27 g/cm
3
) and bone strength. To improve the performance, we introduced an image-processing pipeline (named as 3D-DXA*) that calibrated the linear-regression slopes and intercepts for BV, total vBMD and the minimum fall strength (N = 96). Validation results (N = 24) in 11 fall orientations showed successful correction of proportional bias to unitary slope and zero intercept (before: slopes = 0.62–0.79, bias = 0.44–0.86 kN; after: slopes = 0.88–1.03, bias = 0.12–0.70 kN). However, Pearson correlations degraded by 0.01–0.02 for 2 cases and prediction scatter (RMSE) expanded by 3–29% for 10 fall orientations. Sub-analysis showed 3D-DXA* reduced differences in vBMD distribution with the CT models, but cohort-specific differences remained. This is the first study to independently evaluate 3D-DXAs on multi-ethnic data across a wide age, including previously unstudied cohorts, and more than doubling the number of subjects used in prior validation works. These results provide detailed insights into the accuracy and limitations of DXA-based 3D reconstruction methods.
Vee San Cheong, Dheeraj Jha, Alexander Baker et al.· Scientific Reports· 0 citations
Abstract Low BMD and impaired bone strength are established risk factors for fractures in older adults. Decreased muscle size also contributes to fracture risk; however, the relationship between muscle size and bone density, microarchitecture, and strength using state-of-the-art assessment methods is not clear. In The Study of Muscle, Mobility and Aging, muscle size was assessed using whole-body muscle mass (kg, deuterated creatine [D3Cr] dilution method) and thigh muscle volume (L, by MRI). We investigated cross-sectional associations between baseline D3Cr muscle mass and MRI thigh muscle volume with bone volumetric density, microarchitecture, and strength from HR-pQCT at the distal tibia (DT) and radius (DR), and hip areal BMD from DXA at the first annual follow-up visit (year 1). Muscle and bone parameters were standardized within sex and analyses were stratified by sex. Linear regression models were adjusted for age, race, weight, ≥1 alcoholic drink/wk, ever cigarette smoker, total activity from wrist-worn accelerometry, multimorbidity count (0-11), arthritis, and tibia or ulna length. In 181 women (age 76.2 ± 4.7 yr, 86% White) and 118 men (age 76.1 ± 4.3 yr, 93% White), higher thigh muscle volume (per SD: 1.1 L women; 1.5 L men) was associated with higher DT and DR failure load (p < .05). Greater thigh muscle volume was associated with higher DXA total hip BMD in men but not in women. Greater D3Cr muscle mass (per SD: 4.4 kg women; 5.5 kg men) was associated with higher DT and DR failure load (p < .05) in women only. Associations of muscle size with microarchitecture were variable and differed by sex. Given that failure load is a strong predictor of fracture risk, future studies should investigate whether interventions that target muscle size may impact fracture risk in older adults.
Nina Z. Heilmann, Kerri S. Freeland, L. Roe et al.· JBMR Plus· 0 citations
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