Three-dimensional (3D) fluorescence microscopy has become one of the major tools for investigating live cells because of its non-invasive nature, high biochemical specificity, and the capability of revealing micron-scale 3D structures. However, conventional fluorescence microscopy is limited by optical diffraction, which blurs cellular structures at sizes smaller than the 3D diffraction limit. This thesis introduces a new way to break the diffraction limit using a laser scanning mechanism and non-linear post-processing algorithms.
The proposed laser scanning super-resolution microscope has a resolution limit that depends on image signal-to-noise ratios (SNRs). With reasonable SNRs, simulations show that it can achieve more than three times resolution improvement in all three dimensions. Experiments verified the simulation results and demonstrated 3D super-resolution with a variety of biological samples.
Besides SNRs, algorithms in post-processing also play an important factor in final resolution improvement. The optimal algorithm is shown to be the one that matches the noise models in image acquisition. Regularization further improves the resolution when the regularization term reflects the prior knowledge of the estimated object.
Compared with other super-resolution methods, this laser scanning microscope does not require complex optics, special fluorophores, or manipulating the fluorescing states to surpass the diffraction-limited resolution. With the simplicity in the optical design and the compatibility to a broad range of fluorophores, this method provides a practical approach for imaging biological fine structures with 3D super-resolution.
|Commitee:||Becker, Stephen, Orth, James, Monks, Colin, Piestun, Rafael|
|School:||University of Colorado at Boulder|
|School Location:||United States -- Colorado|
|Source:||DAI-B 82/3(E), Dissertation Abstracts International|
|Keywords:||3D imaging, Computational imaging, Deconvolution, Laser scanning microscopy, PSF engineering, Super-resolution microscopy|
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