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J Microsc. 2017 Sep;267(3):397-408. doi: 10.1111/jmi.12579. Epub 2017 Jun 8.

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

Author information

1
Microscopy and Imaging Core Facility, Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, U.S.A.
2
Department of Electrical and Computer Engineering and Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, U.S.A.
3
Department of Mechanical and Industrial Engineering, Louisiana State University, Baton Rouge, LA, U.S.A.
4
KB Imaging Solutions LLC, Loomis, CA, U.S.A.
5
College of Liberal Arts and Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, U.S.A.
6
Micro and Nanotechnology Lab, Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, U.S.A.
7
Department of Internal Medicine, University of Cincinnati, OH, U.S.A.
8
Department of Mechanical Science and Engineering, University of Illinois at Urbana Champaign, Urbana, IL, U.S.A.

Abstract

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.

KEYWORDS:

Deconvolution; image processing; nonlinear microscopy; second-harmonic generation

PMID:
28594468
DOI:
10.1111/jmi.12579

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