Quantitative active super-resolution thermal imaging: The melanoma case study

Abstract

Super-resolution image acquisition has turned photo-activated far-infrared thermal imaging into a promising tool for the characterization of biological tissues. By the sub-diffraction localization of sparse temperature increments primed by the sample absorption of modulated focused laser light, the distribution of (endogenous or exogenous) photo-thermal biomarkers can be reconstructed at tunable ∼10−50 μm resolution. We focus here on the theoretical modeling of laser-primed temperature variations and provide the guidelines to convert super-resolved temperature-based images into quantitative maps of the absolute molar concentration of photo-thermal probes. We start from camera-based temperature detection via Stefan–Boltzmann’s law, and elucidate the interplay of the camera point-spread-function and pixelated sensor size with the excitation beam waist in defining the amplitude of the measured temperature variations. This can be accomplished by the numerical solution of the three-dimensional heat equation in the presence of modulated laser illumination on the sample, which is characterized in terms of thermal diffusivity, conductivity, thickness, and concentration of photo-thermal species. We apply our data-analysis protocol to murine B16 melanoma biopsies, where melanin is mapped and quantified in label-free configuration at sub-diffraction 40 µm resolution. Our results, validated by an unsupervised machine-learning analysis of hematoxylin-and-eosin images of the same sections, suggest potential impact of super-resolved thermography in complementing standard histopathological analyses of melanocytic lesions.

Keywords

• infrared thermography
• super-resolution imaging
• melanoma
• hematoxylin-and-eosin stain
• k-means clustering