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

• Published in: Journal of biomedical optics Vol. 21; no. 4; p. 046010
• Main Authors: Ghosh, Sreya, Preza, Chrysanthe
• Format: Journal Article
• Language: English
• Published: United States Society of Photo-Optical Instrumentation Engineers 30-04-2016
• Subjects: Algorithms; Computer Simulation; Humans; Imaging, Three-Dimensional - methods; Lung - chemistry; Microscopy, Fluorescence - methods; Models, Biological; Phantoms, Imaging; Refractometry - methods; computational optical sectioning microscopy; block-based three-dimensional restoration; three-dimensional image formation theory
• ISSN: 1083-3668; 1560-2281
• DOI: 10.1117/1.JBO.21.4.046010

Development of a block-based restoration (BBR) method that addresses spatially variant (SV) imaging in wide-field fluorescence microscopy of thick samples is presented. The BBR method is based on a block-based imaging model, which approximates SV imaging using an efficient orthonormal basis decomposition of multiple SV point-spread functions computed at block vertices. The effect of reducing the number of blocks needed to account for SV imaging on the restoration accuracy was investigated with simulations using a numerical lung tissue phantom relevant to biological studies. Results show that reducing the number of blocks by 82% and 98% resulted in a 19% and 27% reduction in restoration accuracy, respectively, thereby establishing a reasonable tradeoff between computational resources and accuracy. Comparison of the BBR method to existing methods (deconvolution) that do not account for SV imaging demonstrates a 90% improvement in restoration accuracy. BBR results from synthetic and experimental images of a controlled test sample with SV refractive index (RI) show consistency, providing a validation of the BBR approach. In this study, information from DIC and fluorescence images was combined to identify regions with changing RI within the imaging volume. The BBR method provides a first step toward computationally tractable reconstruction of images from thick samples.