3D fluorescence microscopy imaging accounting for depth-varying point-spread functions predicted by a strata interpolation method and a principal component analysis method

Abstract

In three-dimensional (3D) computational imaging for wide-field microscopy, estimation methods that solve the inverse imaging problem play an important role. The accuracy of the forward model has a significant impact on the complexity of the estimation method and consequently on the accuracy of the estimated intensity. Previous studies have shown that a forward model 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. In this depth-varying (DV) model, the depth-dependent imaging effects are handled using a stratum-based interpolation method defined on discrete, non-overlapping layers or strata along the Z axis. Recently, a new approximation method based on a principle component analysis (PCA) was developed to predict DV-PSFs1 with improved accuracy over the DV-PSFs predicted by the strata interpolation method of Ref. [11]. In this study, we implemented the PCA-based forward model for DV imaging to further compare the two approaches. DV-PSFs and forward models were computed using both the strata-based and the new PCA-based approximation schemes. Differences are quantified as a function of the approximation, i.e. the number of bases or strata used in each case respectively. A new PCA-based image estimation method was also developed based on the DV expectation maximization (DV-EM) algorithm of Ref. [11]. Preliminary evaluation of the performance of the PCA-based estimation shows promising results and consistency with previous results obtained in previous studies1. © 2011 SPIE.

Publication Title

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

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