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12 March 2014 Further developments in addressing depth-variant 3D fluorescence microscopy imaging
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Three-dimensional (3D) imaging with optical sectioning microscopy uses computational methods to obtain the true fluorescence distribution by ameliorating the effect of defocus, spherical aberration and noise. Inverse algorithms improve image quality at a fraction of the cost of implementing an optical system by accurate modeling of the imaging system. Good inverse imaging algorithms need to be accurate as well as fast. Better understanding of the image formation model is vital to obtain improved restoration through model-based algorithms. Forward imaging models based on a depth-varying point-spread function (DV-PSF) leads to a substantial improvement in the resulting images because it accounts for depth-induced aberrations present in the imaging system. PSFs at every layer can be represented using their principal components. Computation of the forward imaging model using a principal component analysis (PCA) representation of the DV-PSF requires fewer convolutions than a strata based approach investigated in the past. In this paper we present a new algorithm for maximum likelihood image restoration developed based on a PCA representation of the DV-PSF and an accelerated conjugate gradient (CG) iteration scheme. Results obtained with this PCA-CG algorithm from both simulated and experimental fluorescence microscope data are discussed and compared with results obtained from a CG iteration method based on the strata model and linear interpolation of the DV-PSF. The performance of the PCA-CG algorithms is shown to be promising for practical applications.
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Sreya Ghosh, Lutz Schaefer, Dietwald Schuster, and Chrysanthe Preza "Further developments in addressing depth-variant 3D fluorescence microscopy imaging", Proc. SPIE 8949, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXI, 89490Q (12 March 2014);

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