Open AccessMulti-modal image sharpening in fourier transform infrared (FTIR) microscopy
Author(s) -
Rupali Mankar,
Chalapathi Charan Gajjela,
Farideh Foroozandeh Shahraki,
Saurabh Prasad,
David Mayerich,
Rohith Reddy
Publication year - 2021
Publication title -
analyst (london. 1877. online)/analyst
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.998
H-Index - 153
eISSN - 1364-5528
pISSN - 0003-2654
DOI - 10.1039/d1an00103e
Subject(s) - infrared , sharpening , hyperspectral imaging , image resolution , materials science , microscopy , optics , infrared microscopy , resolution (logic) , fourier transform , artificial intelligence , computer science , physics , quantum mechanics
Mid-infrared Spectroscopic Imaging (MIRSI) provides spatially-resolved molecular specificity by measuring wavelength-dependent mid-infrared absorbance. Infrared microscopes use large numerical aperture objectives to obtain high-resolution images of heterogeneous samples. However, the optical resolution is fundamentally diffraction-limited, and therefore wavelength-dependent. This significantly limits resolution in infrared microscopy, which relies on long wavelengths (2.5 μm to 12.5 μm) for molecular specificity. The resolution is particularly restrictive in biomedical and materials applications, where molecular information is encoded in the fingerprint region (6 μm to 12 μm), limiting the maximum resolving power to between 3 μm and 6 μm. We present an unsupervised curvelet-based image fusion method that overcomes limitations in spatial resolution by augmenting infrared images with label-free visible microscopy. We demonstrate the effectiveness of this approach by fusing images of breast and ovarian tumor biopsies acquired using both infrared and dark-field microscopy. The proposed fusion algorithm generates a hyperspectral dataset that has both high spatial resolution and good molecular contrast. We validate this technique using multiple standard approaches and through comparisons to super-resolved experimentally measured photothermal spectroscopic images. We also propose a novel comparison method based on tissue classification accuracy.