Cell imaging beyond the diffraction limit using sparse deconvolution spatial light interference microscopy

S Derin Babacan,Minh Do,Zhuo Wang,Gabriel Popescu

doi:10.1364/boe.2.001815

Abstract

We present an imaging method, dSLIM, that combines a novel deconvolution algorithm with spatial light interference microscopy (SLIM), to achieve 2.3x resolution enhancement with respect to the diffraction limit. By exploiting the sparsity of the phase images, which is prominent in many biological imaging applications, and modeling of the image formation via complex fields, the very fine structures can be recovered which were blurred by the optics. With experiments on SLIM images, we demonstrate that significant improvements in spatial resolution can be obtained by the proposed approach. Moreover, the resolution improvement leads to higher accuracy in monitoring dynamic activity over time. Experiments with primary brain cells, i.e. neurons and glial cells, reveal new subdiffraction structures and motions. This new information can be used for studying vesicle transport in neurons, which may shed light on dynamic cell functioning. Finally, the method is flexible to incorporate a wide range of image models for different applications and can be utilized for all imaging modalities acquiring complex field images.

Highlights

Classical light microscopy techniques cannot be used directly in imaging most biological structures, as they do not significantly absorb or scatter light [1]
Interference-based methods such as phase contrast [2] and differential interference contrast microscopy [3] allows imaging these transparent structures without the need for staining or tagging
Due to the high SNR provided by Spatial light interference microscopy (SLIM), this point spread function (PSF) closely matches the actual optical transfer function of the imager

Summary

Introduction

Classical light microscopy techniques cannot be used directly in imaging most biological structures, as they do not significantly absorb or scatter light [1]. Interference-based microscopy has tremendous advantages, it is still affected by the optical degradation and noise introduced by the instrument [6]. These degradations can be removed to a certain extent by employing post-processing methods. Deconvolution is a common postprocessing method to invert the optical transfer function of the instrument. It is widely used in intensity-based microscopy [7,8,9,10,11], not much work has been reported on deconvolution in microscopy systems collecting quantitative information through complex fields. A nonlinear deconvolution method has been developed in [14] for SLIM that estimates the unknown magnitude and phase fields via a combination of variable projection and quadratic regularization on the phase component

Objectives

Methods

Conclusion