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

Abstract

Abstract. A three-dimensional (3-D) point spread function (PSF) model for wide-field fluorescence microscopy, suitable for imaging samples with variable refractive index (RI) in multilayered media, is presented. This PSF model is a key component for accurate 3-D image restoration of thick biological samples, such as lung tissue. Microscope-and specimen-derived parameters are combined with a rigorous vectorial formulation to obtain a new PSF model that accounts for additional aberrations due to specimen RI variability. Experimental evaluation and verification of the PSF model was accomplished using images from 175-nm fluorescent beads in a controlled test sample. Fundamental experimental validation of the advantage of using improved PSFs in depth-variant restoration was accomplished by restoring experimental data from beads (6 μm in diameter) mounted in a sample with RI variation. In the investigated study, improvement in restoration accuracy in the range of 18 to 35% was observed when PSFs from the proposed model were used over restoration using PSFs from an existing model. The new PSF model was further validated by showing that its prediction compares to an experimental PSF (determined from 175-nm beads located below a thick rat lung slice) with a 42% improved accuracy over the current PSF model prediction. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Two simple criteria to estimate an objective’s performance when imaging in non design tissue clearing solutions

“…An extensive body of work has explored, mostly at the theoretical level, the aberrations introduced by variations in RI along the microscope's optical path, such as when thin tissue slices are mounted on microscope slides [4][5][6][7]. More recently, attention has shifted to methods of correcting such aberrations [8,24] and to higher-order aberrations due to RI inhomogeneities within biological samples [25]. The current renaissance in the field of tissue clearing, motivated by an interest in viewing deep while preserving fluorescence [2,[26][27][28][29], has further aggravated the impact of aberrations due to a combination of high RI clearing solutions and great imaging depth (up to several mm).…”

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

“…The intensity in the sample can be expressed by the sum of all the nonoverlapping blocks as Eight SV-PSFs associated with each block (one for each block vertex location) are computed using the N-interface PSF model, 32 which models light propagation through N stratified layers within a block. SV-PSFs are calculated at discrete locations, ðx o ; y o ; z o Þ ¼ ðX m ; Y n ; Z k Þ, marking block vertices, using imaging conditions including thickness and RI of the sample at these unique locations.…”

“…There are four integrated goals that need to be achieved in order to obtain the solution of this challenging imaging problem. These goals are the successful development of (1) a methodology to derive the sample RI distribution; (2) an accurate but practical PSF model that accounts for specimen RI variability, which we reported elsewhere; 32 (3) a computationally tractable SV forward imaging model; 23,33 and (4) a practical restoration algorithm based on the SV model. The determination of the sample RI map is a challenging problem that has been tackled by several groups [34][35][36][37][38] including ours, 39,40 and it is beyond the scope of this paper.…”

Estimation Methods of the Point Spread Function Axial Position: A Comparative Computational Study

“…In turn, this shows that estimators obtained via the sampling theory can be sufficient to provide useful information about precision and resolution. This highlights the importance of the estimation methods and the selection of an adequate image formation model for localization and deconvolution applications [16,17,37].…”