We aim to capture and apply 3-dimensional bone fragility features for fracture risk estimation. Using inter-subject image
registration, we constructed a hip QCT atlas comprising 37 patients with hip fractures and 38 age-matched controls. In
the hip atlas space, we performed principal component analysis to identify the principal components (eigen images) that
showed association with hip fracture. To develop and test a hip fracture risk model based on the principal components,
we randomly divided the 75 QCT scans into two groups, one serving as the training set and the other as the test set. We
applied this model to estimate a fracture risk index for each test subject, and used the fracture risk indices to discriminate
the fracture patients and controls. To evaluate the fracture discrimination efficacy, we performed ROC analysis and
calculated the AUC (area under curve). When using the first group as the training group and the second as the test group,
the AUC was 0.880, compared to conventional fracture risk estimation methods based on bone densitometry, which had
AUC values ranging between 0.782 and 0.871. When using the second group as the training group, the AUC was 0.839,
compared to densitometric methods with AUC values ranging between 0.767 and 0.807. Our results demonstrate that
principal components derived from hip QCT atlas are associated with hip fracture. Use of such features may provide new
quantitative measures of interest to osteoporosis.
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.
We recently studied bone loss in crewmembers making 4 to 6 months flights on the International Space Station. We employed Quantitative Computed Tomography (QCT) technology (Lang et. al., J Bone Miner Res. 2004; v. 19, p. 1006), which made measurements of both cortical and trabecular bone loss that could not be obtained by using 2-dimensional
dual x-ray absorptiometry (DXA) imaging technology. To further investigate the bone loss after spaceflight, we have
developed image registration technologies to align serial scans so that bone changes can be directly visualized in a subregional level, which can provide more detailed information for understanding bone physiology during long-term spaceflight. To achieve effective and robust registration when large bone changes exist, we have developed technical adaptations to standard registration methods. Our automated image registration is mutual-information based. We have applied an automatically adaptive binning method in calculating the mutual information. After the pre- and post-flight scans are geometrically aligned, the interior bone changes can be clearly visualized. Image registration can also be applied to Finite Element Modeling (FEM) to compare bone strength change, where consistent loading conditions must be applied to serial scans.
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