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 <i>in vivo</i>, 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
Independent of overall bone density, 3D trabecular bone (TB) architecture has been shown to play an important role in
conferring strength to the skeleton. Advances in imaging technologies such as micro-computed tomography (CT) and
micro-magnetic resonance (MR) now permit in vivo imaging of the 3D trabecular network in the distal extremities.
However, various experimental factors preclude a straightforward analysis of the 3D trabecular structure on the basis of
these in vivo images. For MRI, these factors include blurring due to patient motion, partial volume effects, and
measurement noise. While a variety of techniques have been developed to deal with the problem of patient motion, the
second and third issues are inherent limitations of the modality. To address these issues, we have developed a series of
robust processing steps to be applied to a 3D MR image and leading to a 3D skeleton that accurately represents the
trabecular bone structure. Here we describe the algorithm, provide illustrations of its use with both specimen and in vivo
micro-MR images, and discuss the accuracy and quantify the relationship between the original bone structure and the
resulting 3D skeleton volume.
Registration of 3D images acquired from different imaging modalities such as micro-magnetic resonance imaging (µMRI) and micro-computed tomography (µCT) are of interest in a number of medical imaging applications. Most general-purpose multimodality registration algorithms tend to be computationally intensive and do not take advantage of the shape of the imaging volume. Multimodality trabecular bone (TB) images of cylindrical cores, for example, tend to be misaligned along and around the axial direction more than that around other directions. Additionally, TB images acquired by µMRI can differ substantially from those acquired by µCT due to apparent trabecular thickening from magnetic susceptibility boundary effects and non-linear intensity correspondence. However, they share very similar contrast characteristics since the images essentially represent a binary tomographic system. The directional misalignment and the fundamental similarities of the two types of images can be exploited to achieve fast 3D registration. Here we present an intensity cross-correlation based 3D registration algorithm for registering 3D specimen images from cylindrical cores of cadaveric TB acquired by µMRI and µCT in the context of finite-element modeling to assess the bone's mechanical constants. The algorithm achieves the desired registration by first coarsely approximating the three translational and three rotational parameters required to align the µMR images to the µCT scan coordinate frame and fine-tuning the parameters in the neighborhood of the approximate solution. The algorithm described here is suitable for 3D rigid-body image registration applications where through-plane rotations are known to be relatively small. The accuracy of the technique is constrained by the image resolution and in-plane angular increments used.