[Show abstract][Hide abstract] ABSTRACT: Loss of subcutaneous fat, decreased muscle cross-sectional area (CSA) and increased muscle adiposity are related to declining physical function and disability in the elderly, but there is little information about the relationship of these tissue changes to hip fracture. Thus we have compared body composition measures in women with hip fractures to age-matched controls, using quantitative computed tomography (QCT) imaging of the hip to characterize total adiposity, muscle CSA and muscle attenuation coefficient, a measure of adiposity.
45 Chinese women (mean age 74.71+/-5.94) with hip fractures were compared to 66 healthy control subjects (mean age 70.70+/-4.66). Hip QCT scans were analyzed to compute total adipose CSA as well as CSA and attenuation values of muscle groups in the CT scan field of view, including hip extensors, abductors, adductors and flexors. The total femur areal BMD (aBMD) was estimated from the QCT images. Logistic regression was employed to compare body composition measures between fracture subjects and controls after adjustment for age, height, BMI and aBMD. Receiver-operator curve (ROC) analyses determined whether combinations of aBMD and body composition had higher area under curve (AUC) than aBMD alone.
Fracture subjects had lower fat CSA (p<0.0001) than controls but had higher muscle adiposity as indicated by lower attenuation in the adductor, abductor and flexor groups (0.00001<p<0.02). Fracture subjects also had lower extensor and adductor CSA values (p<0.0001). After age and BMI adjustment, the total fat CSA, the extensor and adductor CSA values, and the adductor attenuation values remained significantly lower in the fracture subjects (0.001<p<0.05). In ROC analyses, models combining aBMD with soft tissue measures had higher AUC than models containing only BMD (0.001<p<0.05). Combining body composition with skeletal measures may improve fracture prediction compared to bone measures alone.
[Show abstract][Hide abstract] ABSTRACT: We aim to define a biomechanically-guided region of interest inside the
proximal femur for improving fracture risk prediction based on bone
density measurements. The central hypothesis is that by identifying and
focusing on the proximal femoral tissues strongly associated with hip
fracture risk, we can provide a better densitometric evaluation of
fracture risk compared to current evaluations based on anatomically
defined regions of interest using DXA or CT. To achieve this, we have
constructed a hip statistical atlas of quantitative computed tomography
(QCT) images by applying rigid and non-rigid inter-subject image
registration to transform hip QCT scans of 15 fractured patients and 15
controls into a common reference space, and performed voxel-by-voxel
t-tests between the two groups to identify bone tissues that showed the
strongest relevance to hip fracture. Based on identification of this
fracture-relevant tissue volume, we have generated a
biomechanically-guided region of interest (B-ROI). We have applied BMD
measured from this new region of interest to discriminate the fractured
patients and controls, and compared it to BMD measured in the total
proximal femur. For the femur ROI approach, the BMD values of the
fractured patients and the controls had an overlap of 60
mg/cm3, and only 1 out of 15 fractured patients had BMD below
the overlap region; for the B-ROI approach, a much narrower BMD overlap
region of 28 mg/cm3 was observed, and 11 out of 15 fractured
patients had BMDs below the overlap region.
Full-text · Article · Mar 2008 · Proceedings of SPIE - The International Society for Optical Engineering
[Show abstract][Hide abstract] ABSTRACT: We have developed a general framework which employs quantitative computed tomography (QCT) imaging and inter-subject image registration to model the three-dimensional structure of the hip, with the goal of quantifying changes in the spatial distribution of bone as it is affected by aging, drug treatment or mechanical unloading. We have adapted rigid and non-rigid inter-subject registration techniques to transform groups of hip QCT scans into a common reference space and to construct composite proximal femoral models. We have applied this technique to a longitudinal study of 16 astronauts who on average, incurred high losses of hip bone density during spaceflights of 4-6 months on the International Space Station (ISS). We compared the pre-flight and post-flight composite hip models, and observed the gradients of the bone loss distribution. We performed paired t-tests, on a voxel by voxel basis, corrected for multiple comparisons using false discovery rate (FDR), and observed regions inside the proximal femur that showed the most significant bone loss. To validate our registration algorithm, we selected the 16 pre-flight scans and manually marked 4 landmarks for each scan. After registration, the average distance between the mapped landmarks and the corresponding landmarks in the target scan was 2.56 mm. The average error due to manual landmark identification was 1.70 mm.
[Show abstract][Hide abstract] ABSTRACT: To identify regions inside the hip experiencing the most significant bone loss due to long-duration spaceflight, we have employed inter-subject registration to integrate hip quantitative computed tomography (QCT) scans and constructed the pre- and post-flight composite hip models for 16 astronauts, who experienced 4-6 months of spaceflights on the International Space Station. To achieve this, we applied automatic volumetric rigid and non-rigid inter-subject registration techniques to transform the two groups of hip QCT scans into a common reference hip space. Statistical comparison of the preand post-flight composite models illustrated tissue regions that showed the most serious bone loss inside the proximal femur. Based on the 16 subjects, such regions also showed statistical significance in bone loss according to voxel-by-voxel t-test and false discovery rate (FDR) analysis. By emphasizing on such most responsive regions, we can potentially develop more sensitive bone measurement methods for detecting and analyzing bone response to environmental and other factors, such as aging and osteoporosis drug therapies