Deep Ensemble Model for Quantitative Optical Property and Chromophore Concentration Images of Biological Tissues
The ability to quantify widefield tissue optical properties (OPs, i.e., absorption and scattering) has major implications on the characterization of various physiological and disease processes. However, conventional image processing methods for tissue optical properties are either limited to qualitative analysis, or have tradeoffs in speed and accuracy. The key to quantification of optical properties is the extraction of amplitude maps from reflectance images under sinusoidal illumination of different spatial frequencies. Conventional three-phase demodulation (TPD) method has been demonstrated for the mapping of OPs, but it requires as many as 14 measurement images for accurate OP extraction, which leads to limited throughput and hinders practical translation. Although single-phase demodulation (SPD) method has been proposed to map OPs with a single measurement image, it is typically subject to image artifacts and decreased measurement accuracy. To tackle those challenges, here we develop a deep ensemble model (DEM) that can map tissue optical properties with high accuracy in a single snapshot, increasing the measurement speed by 14x compared to conventional TPD method. The proposed method was validated with measurements on an array of optical phantoms, ex vivo tissues, and in vivo tissues. The errors for OP extraction were 0.83 {+/-} 5.0% for absorption and 0.40 {+/-} 1.9% for reduced scattering, dramatically lower than that of the state-of-the-art SPD method (2.5 {+/-} 15% for absorption and -1.2 {+/-} 11% for reduced scattering). It was further demonstrated that while trained with data from a single wavelength, the DEM can be directly applied to other wavelengths and effectively obtain optical property and chromophore concentration images of biological tissues. Together, these results highlight the potential of DEM to enable new capabilities for quantitative monitoring of tissue physiological and disease processes.