Enhancing Imaging Depth and Sensitivity in Reflectance Mode Near Infrared Optical Imaging with Scatter Reducing Agents

We investigate the role of scatter reducing agents in a continuous wave (CW) near infrared (NIR)reflectance mode imaging setting. We use food-grade dye Tartrazine as a scatter reducing agent to enhance depth sensitivity and weak-absorber detectability in CW diffuse reflectance measurements. We found that reflectance signal was enhanced when the dye was applied on chicken breast phantom. However, we saw reduced reflectance sensitivity when the dye was uniformly dissolved in intralipid phantom which is a commonly used for NIR imaging studies. This shows that the gradient of refractive index modulation created as the dye diffuses from the top layer allows increased reflectance signal sensitivity of optical photons. However, when the scatter reduction is uniform throughout the phantom (like in intralipid phantom), the improved reflectance sensitivity was not observed. Our study points to significant redistribution of photons with scatter modulation with Tartrazine dye. We show significant improvement in sensitivity to signals with reflectance imaging. To elucidate the underlying mechanism of dye induced scatter reduction in tissue, analytical diffusion models and Monte Carlo simulations were employed. Modeling results show the impact of refractive index gradient created due to dye diffusion in enhancing reflectance sensitivity. These findings demonstrate that dye induced scatter reduction provides a practical, low-complexity approach to improving depth sensitivity in CW diffuse reflectance measurements and extend the functional capabilities of CW-NIRS systems for deep-tissue sensing applications. Our preliminary studies shows up to five fold enhancement in signal sensitivity for signals between two and three cm depth.

A Novel Differential Pathlength Factor Model for Near-Infrared Diffuse Optical Imaging

Near infrared diffuse optical imaging can be performed in reflectance and transmission mode and relies on physical models along with measurements to extract information on changes in chromophore concentration. Continuous-wave near-infrared diffuse optical imaging relies on accurate differential pathlength factors (DPFs) for quantitative chromophore estimation. Existing DPF definitions inherit formulation-dependent limitations that can introduce large errors in modified Beer--Lambert law analyses. These errors are significantly higher at smaller source-detector separations in a reflectance mode of measurement. This minimizes their applicability in situations where large area detection is used and also when signal depth is varying. Using Monte Carlo simulations, we derive two distance- and property-dependent DPF models one ideal and one experimentally practical and benchmark them against standard formulations. The proposed models achieve errors below 10 percent across broad optical conditions, whereas conventional DPFs can exceed 100 percent error. The theoretical predictions are further validated using controlled phantom experiments, demonstrating improved quantitative accuracy in CW-NIR imaging.

A 3-D Full-Wave Model to Study the Impact of Soybean Components and Structure on L-Band Backscatter

Microwave remote sensing offers a powerful tool for monitoring the growth of short, dense vegetation like soybean. As the plants mature, changes in their biomass and 3-D structure impact the electromagnetic (EM) backscatter signal. This backscatter information holds valuable insights into crop health and yield, prompting the need for a comprehensive understanding of how structural and biophysical properties of soybeans as well as soil characteristics contribute to the overall backscatter signature. In this study, a full-wave model is developed for simulating L-band backscatter from soybean fields. Leveraging the ANSYS High-Frequency Structure Simulator (HFSS) framework, the model solves for the scattering of EM waves from realistic 3-D structural models of soybean, explicitly incorporating the interplant scattering effects. The model estimates of backscatter match well with the field observations from the SMAPVEX16-MicroWEX and SMAPVEX12, with average differences of 1-2 dB for co-pol and less than 4 dB for cross-pol. Furthermore, the model effectively replicates the temporal dynamics of crop backscatter throughout the growing season. The HFSS analysis revealed that the stems and pods are the primary contributors to HH-pol backscatter, while the branches contribute to VV-pol, and leaves impact the cross-pol signatures. In addition, a sensitivity study with 3-D bare soil surface resulted in an average variation of 8 dB in co- and cross-pol, even when the root mean square height and correlation length were held constant.