Proc. SPIE. 7262, Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging
KEYWORDS: Image processing algorithms and systems, Signal to noise ratio, Image segmentation, Image resolution, 3D modeling, Image registration, Bone, In vivo imaging, Spatial resolution, 3D image processing
Recently, micro-magnetic resonance imaging (μMRI) in conjunction with micro-finite element analysis has shown great
potential in estimating mechanical properties - stiffness and elastic moduli - of bone in patients at risk of osteoporosis.
Due to limited spatial resolution and signal-to-noise ratio achievable in vivo, the validity of estimated properties is often
established by comparison to those derived from high-resolution micro-CT (μCT) images of cadaveric specimens. For
accurate comparison of mechanical parameters derived from μMR and μCT images, analyzed 3D volumes have to be
closely matched. The alignment of the micro structure (and the cortex) is often hampered by the fundamental differences
of μMR and μCT images and variations in marrow content and cortical bone thickness. Here we present an intensity
cross-correlation based registration algorithm coupled with segmentation for registering 3D tibial specimen images
acquired by μMRI and μCT in the context of finite-element modeling to assess the bone's mechanical constants. The
algorithm first generates three translational and three rotational parameters required to align segmented μMR and CT
images from sub regions with high micro-structural similarities. These transformation parameters are then used to
register the grayscale μMR and μCT images, which include both the cortex and trabecular bone. The intensity crosscorrelation
maximization based registration algorithm described here is suitable for 3D rigid-body image registration
applications where through-plane rotations are known to be relatively small. The close alignment of the resulting images
is demonstrated quantitatively based on a voxel-overlap measure and qualitatively using visual inspection of the micro
Trabecular bone (TB) consists of a network of interconnected struts and plates occurring near the joints of long bones and in the axial skeleton. In response to mechanical stresses it remodels such that trabeculae are aligned with the major stress lines, thus leading to a highly anisotropic network. Beside bone volume fraction, anisotropy and topological indices are known to be strong predictor of the TB mechanical competence. In osteoporosis, the most common bone disorder, the remodeling balance is perturbed due to increased resorption, resulting in net bone loss accompanied by architectural deterioration, leading to fragile bone and increased fracture risk. In vertebral osteoporosis, preferential loss of transverse trabeculae leads to increased anisotropy and change in topology, hence exact measurements of these parameters are of paramount interest. Current in vivo imaging yields voxel size comparable to TB thickness, thus resulting in inherently fuzzy representations. The commonly used methods for anisotropy require binarization which is difficult to achieve in the limited spatial resolution regime where the intensity histogram is mono-modal. Here, we present a new tensor scale (t-scale) based TB architectural measures that (1) obviates binarization, and (2) yields localized measures. We evaluate the performance of this method on micro-CT images of vertebral bone and test the hypothesis that the method, along with BMD and other structural parameters, allows prediction of TB’s mechanical competence. Toward this goal, we estimate Young’s modulus (YM) of (13mm)3 vertebral TB samples under uniaxial loading and examine linear correlation of different t-scale parameters computed via micro-CT imaging .
The spatial autocorrelation analysis method represents a powerful, new approach to quantitative characterization of structurally quasi-periodic anisotropic materials such as trabecular bone (TB). The method is applicable to grayscale images and thus does not require any preprocessing, such as segmentation which is difficult to achieve in the limited resolution regime of in vivo imaging. The 3D autocorrelation function (ACF) can be efficiently calculated using the Fourier transform. The resulting trabecular thickness and spacing measurements are robust to the presence of noise and produce values within the expected range as determined by other methods from μCT and μMRI datasets. TB features found from the ACF are shown to correlate well with those determined by the Fuzzy Distance transform (FDT) in the transverse plane, i.e. the plane orthogonal to bone’s major axis. The method is further shown to be applicable to in-vivo μMRI data. Using the ACF, we examine data acquired in a previous study aimed at evaluating the structural implications of male hypogonadism characterized by testosterone deficiency and reduced bone mass. Specifically, we consider the hypothesis that eugonadal and hypogonadal men differ in the anisotropy of their trabecular networks. The analysis indicates a significant difference in trabecular bone thickness and longitudinal spacing between the control group and the testosterone deficient group. We conclude that spatial autocorrelation analysis is able to characterize the 3D structure and anisotropy of trabecular bone and provides new insight into the structural changes associated with osteoporotic trabecular bone loss.