Multidimensional Multicolor Image Reconstruction Techniques for Fluorescence Microscopy

• Main Author: Dilipkumar, Shilpa
• Other Authors: Mondal, Partha P
• Language: en_US
• Published: 2018
• Subjects: Image Reconstruction; Fluorescence Microscopy; Image Processing; Fluorescence Imaging; Optical Microscopy; Maximum Likelihood; Bayesian Maximum; Multi-color 3D Reconstruction; Multiple Excitation Spot Optical Microscopy; Image Reconstruction Techniques; Instrumentation
• Online Access: http://etd.iisc.ernet.in/2005/3763; http://etd.iisc.ernet.in/abstracts/4634/G26960-Abs.pdf

Fluorescence microscopy is an indispensable tool in the areas of cell biology, histology and material science as it enables non-invasive observation of specimen in their natural environment. The main advantage of fluorescence microscopy is that, it is non-invasive and capable of imaging with very high contrast and visibility. It is dynamic, sensitive and allows high selectivity. The specificity and sensitivity of antibody-conjugated probes and genetically-engineered fluorescent protein constructs allows the user to label multiple targets and the precise location of intracellular components. However, its spatial reso- lution is limited to one-quarter of the excitation wavelength (Abbe’s diffraction limit). The advent of new and sophisticated optics and availability of fluorophores has made fluorescence imaging a flourishing field. Several advanced techniques like TIRF, 4PI, STED, SIM, SPIM, PALM, fPALM, GSDIM and STORM, have enabled high resolution imaging by breaking the diffraction barrier and are a boon to medical and biological research. Invention of confocal and multi-photon microscopes have enabled observation of the specimen embedded at depth. All these advances in fluorescence microscopy have made it a much sought-after technique. The first chapter provides an overview of the fundamental concepts in fluorescence imag- ing. A brief history of emergence of the field is provided in this chapter along with the evolution of different super-resolution microscopes. An introduction to the concept of fluorophores, their broad classification and their characteristics is discussed in this chap- ter. A brief explanation of different fluorescence imaging techniques and some trending techniques are introduced. This chapter provides a thorough foundation for the research work presented in the thesis. Second chapter deals with different microscopy techniques that have changed the face of biophotonics and nanoscale imaging. The resolution of optical imaging systems are dictated by the inherent property of the system, known as impulse response or more popularly “point spread function”. A basic fluorescence imaging system is presented in this chapter and introduces the concept of point spread function and resolution. The introduction of confocal microscope and multi-photon microscope brought about improved optical sectioning. 4PI microscopy technique was invented to improve the axial resolution of the optical imaging system. Using this microscopy modality, an axial resolution of upto ≈ 100nm was made possible. The basic concepts of these techniques is provided in this chapter. The chapter concludes with a discussion on some of the optical engineering techniques that aid in improved lateral and axial resolution improvements and then we proceed to take on these engineering techniques in detail in the next chapter. Introduction of spatial masks at the back aperture of the objective lens results in gen- eration of a Bessel-like beam, which enhances our ability to see deeper inside a spec- imen with reduced aberrations and improved lateral resolution. Bessel beams have non-diffracting and self-reconstructing properties which reduces the scattering while ob- serving cells embedded deep in a thick tissue. By coupling this with the 4PI super- resolution microscopy technique, multiple excitation spots can be generated along the optical axis of the two opposing high-NA objective lenses. This technique is known as multiple excitation spot optical (MESO) microscopy technique. It provides a lateral resolution improvement upto 150nm. A detailed description of the technique and a thorough analysis of the polarization properties is discussed in chapter 3. Chapters 4 and 5 bring the focus of the thesis to the main topic of research - multi- dimensional image reconstruction for fluorescence microscopy by employing the statis- tical techniques. We begin with an introduction to filtering techniques in Chapter 4 and concentrate on an edge-preserving denoising filter: Bilateral Filter for fluorescence microscopy images. Bilateral filter is a non-linear combination of two Gaussian filters, one based on proximity of two pixels and the other based on the intensity similarity of the two. These two sub-filters result in the edge-preserving capability of the filter. This technique is very popular in the field of image processing and we demonstrate the application of the technique for fluorescence microscopy images. The chapter presents a through description of the technique along with comparisons with Poisson noise mod- eling. Chapters 4 and 5 provide a detailed introduction to statistical iterative recon- struction algorithms like expectation maximization-maximum likelihood (EM-ML) and maximum a-posteriori (MAP) techniques. The main objective of an image reconstruc- tion algorithm is to recover an object from its noisy degraded images. Deconvolution methods are generally used to denoise and recover the true object. The choice of an appropriate prior function is the crux of the MAP algorithm. The remaining of chapter 5 provides an introduction to different potential functions. We show some results of the MAP algorithm in comparison with that of ML algorithm. In chapter 6, we continue the discussion on MAP reconstruction where two new potential functions are introduced and demonstrated. The first one is based on the application of Taylor series expansion on the image. The image field is considered to be analytic and hence Taylor series produces an accurate estimation of the field being reconstructed. The second half of the chapter introduces an interpolation function to approximate the value of a pixel in its neighborhood. Cubic B-splines are widely used as a basis function during interpolation and they are popular technique in computer vision and medical imaging techniques. These novel algorithms are tested on di_erent microscopy data like, confocal and 4PI. The results are shown at the _nal part of the chapter. Tagging cell organelles with uorescent probes enable their visualization and analysis non-invasively. In recent times, it is common to tag more than one organelle of interest and simultaneously observe their structures and functions. Multicolor uorescence imaging has become a key technique to study speci_c processes like pH sensing and cell metabolism with a nanoscale precision. However, this process is hindered by various problems like optical artifacts, noise, autouorescence, photobleaching and leakage of uorescence from one channel to the other. Chapter 7 deals with an image reconstruction technique to obtain noise-free and distortion-less data from multiple channels when imaging a multicolor sample. This technique is easily adaptable with the existing imaging systems and has potential application in biological imaging and biophysics where multiple probes are used to tag the features of interest. The fact that the lateral resolution of an optical system is better than the axial resolution is well known. Conventional microscopes focus on cells that are very close to the cover-slip or a few microns into the specimen. However, for cells that are embedded deep in a thick sample (ex: tissues), it is di_cult to visualize them using a conventional microscope. A number of factors like, scattering, optical aberrations, mismatch of refractive index between the objective lens and the mounting medium and noise, cause distortion of the images of samples at large depths. The system PSF gets distorted due to di_raction and its shape changes rapidly at large depths. The aim of chapter 8 is to introduce a technique to reduce distortion of images acquired at depth by employing image reconstruction techniques. The key to this methodology is the modeling of PSF at large depths. Maximum likelihood technique is then employed to reduce the streaking e_ects of the PSF and removes noise from raw images. This technique enables the visualization of cells embedded at a depth of 150_m. Several biological processes within the cell occur at a rate faster than the rate of acquisition and hence vital information is missed during imaging. The recorded images of these dynamic events are corrupted by motion blur, noise and other optical aberrations. Chapter 9 deals with two techniques that address temporal resolution improvement of the uorescence imaging system. The _rst technique focuses on accelerating the data acquisition process. This includes employing the concept of time-multiplexing to acquire sequential images from a dynamic sample using two cameras and generating multiple sheets of light using a di_raction grating, resulting in multi-plane illumination. The second technique involves the use of parallel processing units to enable real-time image reconstruction of the acquired data. A multi-node GPU and CUDA architecture effciently reduce the computation time of the reconstruction algorithms. Faster implementation of iterative image reconstruction techniques can aid in low-light imaging and dynamic monitoring of rapidly moving samples in real time. Employing rapid acquisition and rapid image reconstruction aids in real-time visualization of cells and have immense potential in the _eld of microbiology and bio-mechanics. Finally, we conclude the thesis with a brief section on the contribution of the thesis and the future scope the work presented. Thank you for using www.freepdfconvert.com service! 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