Super-Sensitivity and Super-Resolution Quantitative Multiphoton Microscopy

Doctoral Dissertation
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Abstract

Multiphoton microscopy (MPM) combined with frequency-domain fluorescence lifetime imaging microscopy (FD-FLIM) has enabled three-dimensional quantitative molecular microscopy in vivo. The integrated MPM-FD-FLIM imaging system possesses both the advantages of MPM, such as reduced photodamage and deep tissue penetration, and the strengths of FLIM, including error tolerance and the ability to discriminate different fluorophores with similar emission spectra. In this work, a variety of MPM-FD-FLIM imaging systems are investigated and developed in order to handle multiple imaging tasks. All systems utilize the same MPM technique as excitation sources but different FD-FLIM methods as detection modalities.

In the first part of this work, several original techniques are developed to improve the sensitivity, i.e., the speed and signal-to-noise ratio (SNR) performance, of MPM-FD-FLIM. Starting with a thorough investigation of the SNR of MPM-FD-FLIM and their limits, a super-sensitivity technique that fundamentally improves the imaging speed by a factor of two is developed and experimentally demonstrated. Another method to improve the SNR is to increase the excitation laser power, which, however, can cause fluorophore saturation and lead to incorrect fluorescence lifetime measurements. To address this issue, a novel FD-FLIM method that permits accurate and fast lifetime measurements even with a 2.6-fold increase in excitation power is presented. Next, phase multiplexing FLIM, a technique that can generate 2D, 3D, and 4D intensity, lifetime, and phasor images simultaneously, is proposed and demonstrated in deep scattering tissues. To improve the speed of image analysis, a novel and unbiased approach to segment FLIM images automatically by K-means clustering of FLIM phasors is presented. Successful image segmentation on 2D and 3D FLIM images of fixed cells and living animals acquired with two different FLIM systems are demonstrated. Additionally, to improve the SNR of FLIM, the first Poisson-Gaussian denoising dataset that can be used to benchmark state-of-the-art denoising algorithms is presented. The benchmarked algorithms, especially those which utilize deep learning, can be used to further improve the SNR of MPM-FD-FLIM.

In the second part of this work, a super-resolution fluorescence microscopy technique that can be easily implemented and requires neither additional hardware nor complex post-processing is proposed and demonstrated. The method is based on the principle of stepwise optical saturation (SOS), where M steps of raw fluorescence images are linearly combined to generate an image with a -fold increase in resolution compared with diffraction-limited images. Additionally, an upgrade version of SOS, DeSOS, where the raw images go through a deconvolution procedure for an improved SNR before SOS processing, is demonstrated. With SOS and DeSOS, super-resolution images can be generated in intact animals. Finally, a novel super-resolution FLIM technique called generalized stepwise optical saturation (GSOS) that generalizes and extends the concept SOS is presented. SOS and GSOS both utilize the linear combination of M raw images to increase the resolution by a factor of , but SOS is a special and the simplest case of GSOS. The super-resolution FLIM capability of GSOS is experimentally demonstrated with biological samples on an MPM-FD-FLIM system based on radio frequency analog signal processing. This is the first implementation of super-resolution imaging in FD-FLIM.

Attributes

Attribute NameValues
Author Yide Zhang
Contributor Scott S. Howard, Research Director
Contributor Thomas O'Sullivan, Committee Member
Contributor Alan Seabaugh, Committee Member
Contributor Cody Smith, Committee Member
Degree Level Doctoral Dissertation
Degree Discipline Electrical Engineering
Degree Name Doctor of Philosophy
Banner Code
  • PHD-EE

Defense Date
  • 2019-08-06

Submission Date 2019-08-12
Subject
  • Multiphoton microscopy

  • Super-resolution

  • Stepwise optical saturation

  • Fluorescence lifetime imaging microscopy

  • Fluorescence microscopy

  • Deep learning

  • Signal-to-noise ratio

  • K-means clustering

  • Denoising

  • Super-sensitivity

Language
  • English

Record Visibility and Access Public
Content License
  • All rights reserved

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