Investigation of visible light transmissivity in human tissue phantoms of variable thickness and scattering turbidity

Post-surgical infections pose serious challenges for implant patients. The large surface areas involved in orthopedic procedures can be particularly problematic, as they can promote the formation of biofilms (bacterial colonies that can become resistant to antibiotics) in prosthetic joints, leading to chronic pain, reduced mobility, and the need for costly revision surgeries. As such, the successful monitoring and early detection of these biofilms could have significant clinical benefits. Although non-invasive optical methods are currently being developed for this purpose, these modalities are complicated by the diffusive nature of tissue. While previous work has demonstrated that tissue penetration depth at visible wavelengths increases with beam diameter, the results exhibit increased diffusivity, which can be problematic for imaging in highly scattering media. Thus, we present a series of experiments designed to optimize the transmission of identifiable information through turbid samples. This effect was investigated by positioning an Air Force target card on an automated translational stage. A 635 nm diode laser was then incident on the target through a series of agarose phantoms of various thicknesses. The turbidity of these phantoms was controlled by varying the concentration of aluminum oxide powder, to mimic the scattering and absorption coefficients of human tissue. Laser beam diameter was also varied using a series of convex lenses, to determine the effect of beam width on both penetration depth and beam diffusivity. Intensity distribution plots, collected by a CMOS-based optical beam profiler, will be presented for various phantom thicknesses and beam diameters. Finally, the results will be compared with Monte Carlo-based simulations, to verify the scattering coefficients and measure total transmissivity. These findings could assist in the development of a low-cost, non-invasive diagnostic tool for the early detection of joint-based bacterial infections, thereby extending the lifespan of orthopedic implants.