Sparsity-based multi-height phase recovery in holographic microscopy.

Yair Rivenson,Yichen Wu,Alborz Feizi,Yibo Zhang,Hongda Wang,Aydogan Ozcan

doi:10.1038/srep37862

Abstract

High-resolution imaging of densely connected samples such as pathology slides using digital in-line holographic microscopy requires the acquisition of several holograms, e.g., at >6–8 different sample-to-sensor distances, to achieve robust phase recovery and coherent imaging of specimen. Reducing the number of these holographic measurements would normally result in reconstruction artifacts and loss of image quality, which would be detrimental especially for biomedical and diagnostics-related applications. Inspired by the fact that most natural images are sparse in some domain, here we introduce a sparsity-based phase reconstruction technique implemented in wavelet domain to achieve at least 2-fold reduction in the number of holographic measurements for coherent imaging of densely connected samples with minimal impact on the reconstructed image quality, quantified using a structural similarity index. We demonstrated the success of this approach by imaging Papanicolaou smears and breast cancer tissue slides over a large field-of-view of ~20 mm2 using 2 in-line holograms that are acquired at different sample-to-sensor distances and processed using sparsity-based multi-height phase recovery. This new phase recovery approach that makes use of sparsity can also be extended to other coherent imaging schemes, involving e.g., multiple illumination angles or wavelengths to increase the throughput and speed of coherent imaging.

Highlights

IntroductionIn imaging pathology slides using multi-height measurements, usually 6–8 holograms at different sample-to-sensor distances are required to get high quality and clinically relevant microscopic reconstructions (amplitude and phase images) of the object[10], i.e., the number of measurements is 3–4 times of the number of variables, including the amplitude and phase pixels that needed to be retrieved in a complex image of the sample
To address this phase retrieval problem[5,7,8] of in-line holography it is common to apply measurement diversity which can include, e.g. sample-to-sensor distances[9,10,11,12], illumination angles[13,14] and wavelengths[14,15]
We experimentally demonstrate that for densely connected biological samples, such as Papanicolaou smears and breast cancer tissue slides, 2 in-line holograms with different sample-to-sensor distances are sufficient for image reconstruction, when sparsity constraints are applied during the iterative reconstruction process

Summary

Introduction

In imaging pathology slides using multi-height measurements, usually 6–8 holograms at different sample-to-sensor distances are required to get high quality and clinically relevant microscopic reconstructions (amplitude and phase images) of the object[10], i.e., the number of measurements is 3–4 times of the number of variables, including the amplitude and phase pixels that needed to be retrieved in a complex image of the sample. As a result of this significant difference in the density and connectivity of the object to be imaged, the number of measurements that we need to have without losing image quality is two, rather than a single hologram This sparsity-based phase recovery approach can be extended to other coherent microscopy schemes, involving e.g., multi-angle[13] or multi-wavelength-based[26] phase retrieval. Enabled by novel algorithmic processing, this sparsity-based holographic image reconstruction technique can be regarded as another step forward in making lensfree on-chip holography more efficient, higher throughput and more appealing in microscopy-related applications

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