Further developments in addressing depth-variant 3D fluorescence microscopy imaging

Abstract

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.

Fluorescence microscopy point spread function model accounting for aberrations due to refractive index variability within a specimen

“…This method has been proven effective to obtain the approximate depth of the sample 24 and has also been verified using guides etched on the coverslip. 33 A schematic diagram of this system is shown in Fig.…”

“…The Haeberlé representation of the PSF model has been proven effective in restoring microscope acquired images. 24,25 The PSF model discussed in this paper was developed using an approach similar to the one presented by Haeberlé, and it represents an extension of his model, which assumes that the sample has a uniform RI. The new PSF model presented here accounts for a spatially variant sample RI by assuming that different layers within the sample can have a different RI, thereby approximating the imaging conditions more accurately than previous models while keeping the computations tractable.…”

Three-dimensional block-based restoration integrated with wide-field fluorescence microscopy for the investigation of thick specimens with spatially variant refractive index

“…Part of the research presented here was also presented elsewhere. 30,33 2 Block-Based Restoration Method…”