Trabecular bone structure analysis in the limited spatial resolution regime of in vivo MRI

To develop a method for processing and visualization of trabecular bone networks on the basis of magnetic resonance (MR) images acquired in the limited spatial resolution regime of in vivo imaging at which trabecular thickness is comparable to voxel size.

A sequence of processing steps for analyzing the topologic structure of trabecular bone networks is presented and evaluated using three types of datasets: images of synthetic structures with various levels of superimposed Gaussian noise, micro-computed tomographic images of human trabecular bone downsampled to in vivo resolution, and in vivo micro-MR images from a prior longitudinal study investigating the structural implications of testosterone treatment of hypogonadal men. The simulated images were analyzed at a voxel size of 150 microm(3), the clinical MR image data had been acquired with 137 x 137 x 410 microm(3) voxel size. The technique is a modification to the virtual bone biopsy processing chain that involves a sinc convolution step immediately preceding binarization, and employs the Manzanera-Bernard thinning algorithm for obtaining the three-dimensional skeleton before topologic classification. The detectability of plate and rod bone elements was also analyzed theoretically.

As compared with previously published techniques, the approach produced a more accurate bone skeleton in the micro-computed tomographic and simulation experiments, with clear improvement in preservation of rod and plate elements. Simulations suggest that rods are detectable down to a diameter of approximately 50% of the MR image voxel length, whereas plates can be detected at thicknesses of 20% or more of voxel length. For in vivo studies, it was shown that the method could recover the treatment response in terms of the ensuing topologic changes in patients undergoing antiresorptive treatment.

The algorithm for processing of in vivo micro-MR images of trabecular bone is superior to prior approaches in preserving the topology of the network in the presence of noise.