Optical phase microscopy is widely adopted for quantitative imaging of optical density in transparent cells and tissues that lack absorption contrast. Fundamentally, the phase information of the sample is contained in the wavefront of the probe beam, often detected by interferometry-based techniques. Here, a novel approach has been developed based on the phase-sensitive second harmonic signals that are generated after the sample. A deep learning algorithm is developed for efficient recovery of the original phase images. Inheriting the advantages of the second harmonic imaging, our second harmonic phase imaging is a label-free technique with a demonstrated phase sensitivity of 1/100 wavelength and high robustness against noises, facilitating applications in biological imaging and remote sensing.
Infrared (IR) spectroscopy depicts molecular structure and dynamics based on vibrational absorption of chemical bonds. Spatially resolved IR spectroscopy, i.e. IR imaging, further enabled label-free in situ chemical imaging for dynamics in complex systems. However, IR imaging suffers from low spatial resolution at a few micrometers due to diffraction limit, thus having difficulty in applications such as sub-cellular imaging. Recently, by visible light probing of the photothermal effect of vibrational absorption, mid-infrared photothermal imaging (MIP) overcomes the limitations of conventional IR microscopy and has achieved sub-micron resolution. In this work, we built an optimized MIP system to boost the spatial resolution and sensitivity, and demonstrated MIP imaging of nanometer-sized polymeric microspheres and living cells with a high spatial resolution of 200 nm.
Optical phase microscopy is widely adopted for quantitative imaging of optical density in transparent cells and tissues, yet lacks the chemical selectivity. To address this challenge, a bond-selective transient phase imaging (BTSP) technique was developed, in which a transient change in phase induced by infrared excitation of molecular vibrations was detected by a diffraction phase microscope. BTSP achieved chemically selective phase imaging of live cells. We further demonstrated an IR-pump visible-probe phase microscopy based on second harmonic generation after the sample, enabled by deep learning. The phase-sensitive information is encoded into the second harmonic signal, which is decoded using a deep learning algorithm. It presents a label-free technique featured by high phase sensitivity and high robustness against noises, which has promising applications in biological and medical imaging and remote sensing.