Analysis of osteoporotic tissue using combination nonlinear optical imaging

TL;DR

This paper demonstrates a combined nonlinear optical imaging approach using sum frequency generation and coherent anti-Stokes Raman scattering to analyze osteoporotic tissue, enabling non-destructive, paraffin-free imaging and classification of bone health.

Contribution

It introduces a novel imaging technique combined with a statistical and machine learning method to distinguish osteoporotic from healthy bone tissue without destructive sample preparation.

Findings

Successful imaging of collagen in mouse tibia using the combined method

Effective separation of image quality for analysis

Machine learning classification differentiates osteoporotic and healthy bone

Abstract

Currently, a large number of stored tissue samples are unavailable for spectroscopic study without the time consuming and destructive process of paraffin removal. Instead, a structurally sensitive technique, sum frequency generation, and a chemically sensitive technique, coherent anti-Stokes Raman scattering enables imaging through the paraffin. This method is demonstrated by imaging collagen in mouse tibia. We introduce a statistical method for separating images by quality and, with the aid of machine learning, distinguish osteoporotic and healthy bone. This method has the potential to verify the results of previous studies and reduce new sample production by allowing retesting results with spectroscopy.