Raman spectroscopy is a non-invasive vibrational technique that yields the biochemical signature of bone, and this can be done transcutaneously using spatially offset Raman spectroscopy. The percentage of bone signal detected will increase with further source-detector offsets, but the overall signal will be decreased. In recent work, our work suggests that 3 mm is an optimal offset for detecting bone signal for phalanges and 5 mm for measuring metacarpals. The objective of this work is to create and validate a SORS instrument that collects offsets at 0, 3, and 6 mm offsets simultaneously. By conducting simulations with an optical design software, we were able to optimize the imaging throughput for each offset location. Preliminary data from a cadaver specimen suggests we collect good quality data from offsets 0, 3, and 6 mm from both metacarpals and phalanges. Future work will work on validating this instrument as a valid tool to perform bone quality assessment.
We investigate spatially offset Raman spectroscopy’s varying sensitivity to subsurface bone features in different regions of human cadaver hands and in subjects with different body mass index values.
Fracture toughness, a bone’s resistance to breaking, is typically measured via invasive mechanical tests. In this ex vivo study on mouse femurs, four Raman spectral features associated with bone properties were significantly correlated to fracture toughness using a partial least squares regression model. By including parameters measured from dual-energy absorptiometry and micro-computed tomography in the model, fracture toughness predictions on ovariectomized mice were significantly lower than a control cohort’s. This shows that meaningful estimates of fracture toughness can be estimated using input parameters obtained non-destructively.
Using transcutaneous spatially offset Raman spectroscopy (SORS) and partial least squares regression (PLSR), we recently predicted the areal bone mineral density (aBMD), volumetric bone mineralization density (vBMD) and maximum torque (MT) of tibiae in living mice. Despite the spatial offset geometry, the accuracy of the predictions was still affected by the signal from the overlying soft tissue that, like bone, contains large amounts of Type I collagen. Here we report a way to use SOLD (simultaneous, overconstrained, library-based decomposition) to improve the PLSR accuracy. The SOLD processing generates one bone spectrum estimate, one soft tissue spectrum estimate, and a residual. We combine the bone and residual spectra together for submission to PLSR, discarding only the soft tissue contribution. With the implementation of this soft-tissue-subtracted SOLD processing, we demonstrate that we can predict vBMD and MT more accurately than our previous transcutaneous measurements.
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