Bone tissue compositional differences in women with and without osteoporotic fracture.

It is generally accepted that the hallmark of osteoporosis is a reduction in bone mass. There is significant overlap, however, in bone mineral density between osteoporotic and normal individuals. This study examined the chemical composition of bone tissue obtained from women who had sustained a fracture and women without fracture to determine if there are differences between the two groups. Nineteen fractured and eleven non-fractured proximal femurs were obtained, matched for age and bone volume fraction obtained from micro-computed tomography. Trabecular bone specimens were examined by Raman spectroscopy to determine measures of chemical composition. A subset of the specimens was utilized to compare locations at the fracture and regions at least 2 mm away from apparent tissue damage using Raman spectroscopy. In addition, fifteen iliac crest biopsies each were obtained from women who had sustained a fracture and from normal controls. Raman spectroscopy was used to determine measures of chemical composition of trabecular and cortical bone. The results demonstrated that femoral bone tissue in the region of visible damage had a trend towards differences compared to regions at least 2 mm from visible damage. Femoral trabecular bone in fractured women had a higher carbonate/amide I area ratio than in unfractured women. Iliac crest biopsies revealed a higher carbonate/phosphate ratio in cortical bone from women who had sustained a fracture. Results suggest that the chemical composition of bone tissue may be an additional risk factor for osteoporotic fracture.

Band-target entropy minimization (BTEM) applied to hyperspectral Raman image data.

Band-target entropy minimization (BTEM) has been applied to extraction of component spectra from hyperspectral Raman images. In this method singular value decomposition is used to calculate the eigenvectors of the spectroscopic image data set. Bands in non-noise eigenvectors that would normally be used for recovery of spectra are examined for localized spectral features. For a targeted (identified) band, information entropy minimization or a closely related algorithm is used to recover the spectrum containing this feature from the non-noise eigenvectors, plus the next 5-30 eigenvectors, in which noise predominates. Tests for which eigenvectors to include are described. The method is demonstrated on one synthesized Raman image data set and two bone tissue specimens. By inclusion of small amounts of signal that would be unused in other methods, BTEM enables the extraction of a larger number of component spectra than are otherwise obtainable. An improvement in signal/noise ratio of the recovered spectra is also obtained.