Application of an advanced maximum likelihood estimation restoration method for enhanced-resolution and contrast in second-harmonic generation microscopy

Second-harmonic generation (SHG) microscopy has gained popularity because of its ability to perform submicron, label-free imaging of noncentrosymmetric biological structures, such as fibrillar collagen in the extracellular matrix environment of various organs with high contrast and specificity. Because SHG is a two-photon coherent scattering process, it is difficult to define a point spread function (PSF) for this modality. Hence, compared to incoherent two-photon processes like two-photon fluorescence, it is challenging to apply the various PSF-engineering methods to improve the spatial resolution to be close to the diffraction limit. Using a synthetic PSF and application of an advanced maximum likelihood estimation (AdvMLE) deconvolution algorithm, we demonstrate restoration of the spatial resolution in SHG images to that closer to the theoretical diffraction limit. The AdvMLE algorithm adaptively and iteratively develops a PSF for the supplied image and succeeds in improving the signal to noise ratio (SNR) for images where the SHG signals are derived from various sources such as collagen in tendon and myosin in heart sarcomere. Approximately 3.5 times improvement in SNR is observed for tissue images at depths of up to ∼480 nm, which helps in revealing the underlying helical structures in collagen fibres with an ∼26% improvement in the amplitude contrast in a fibre pitch. Our approach could be adapted to noisy and low resolution modalities such as micro-nano CT and MRI, impacting precision of diagnosis and treatment of human diseases.

3D deconvolution microscopy

3D deconvolution microscopy is a combination of optical and computational techniques that are used to maximize the observed resolution and signal from a biological specimen. Mathematical models are used to predict the distribution of out-of-focus light caused by the inherent optical limitations of the instrument, which can then be compensated for using computer algorithms. This unit will review the theory of image formation and characteristics of the point spread function (PSF) based on the instrument modality and objective lens parameters. A variety of commonly used deblurring and deconvolution methods are described, and their applications to sample datasets are illustrated to show the performance of each algorithm. Steps for setting up the image acquisition to acquire data suitable for deconvolution are described, and the challenge of maximizing signal levels while minimizing light exposure addressed. Deconvolution examples from widefield epi-fluorescence and laser scanning confocal are shown, and suitability for other modalities discussed.