Asymmetric Point Spread Function Estimation and Deconvolution for Serial-Sectioning Block-Face Imaging

Abstract

Serial-sectioning block-facing (SSBF) imaging is an attractive method to overcome the depth limitations of optical imaging and slice alignment challenges of traditional serial sectioning histology. Despite these advantages, SSBF modalities suffer from reduced axial resolution caused by out of focus sub-surface signals at the block face. In order to restore axial resolution, the sub-surface signal must be removed. In this work, we describe a methodology for restoring the axial resolution through a combination of sample preparation and deconvolution in post-processing. An opacifying agent used during sample preparation, decreases the subsurface signal by absorbing excitation light. From these image stacks we estimate parameters to generate a highly asymmetric point-spread function (PSF), which is then used in a Richardson-Lucy deconvolution algorithm. Whilst our methodology can be widely applied to any SSBF technique, we show its application in multi-fluorescent high resolution episcopic microscopy (MF-HREM), which is a simple and cost-effective alternative to optical sectioning techniques such as two-photon microscopy.