Abstract
High-content biological microscopy targets high-resolution imaging across large fields-of-view (FOVs). Recent works have demonstrated that computational imaging can provide efficient solutions for high-content microscopy. Here, we use speckle structured illumination microscopy (SIM) as a robust and cost-effective solution for high-content fluorescence microscopy with simultaneous high-content quantitative phase (QP). This multi-modal compatibility is essential for studies requiring cross-correlative biological analysis. Our method uses laterally-translated Scotch tape to generate high-resolution speckle illumination patterns across a large FOV. Custom optimization algorithms then jointly reconstruct the sample's super-resolution fluorescent (incoherent) and QP (coherent) distributions, while digitally correcting for system imperfections such as unknown speckle illumination patterns, system aberrations and pattern translations. Beyond previous linear SIM works, we achieve resolution gains of 4× the objective's diffraction-limited native resolution, resulting in 700 nm fluorescence and 1.2 μm QP resolution, across a FOV of , giving a space-bandwidth product (SBP) of 60 megapixels.
Highlights
The space-bandwidth product (SBP) metric characterizes information content transmitted through an optical system; it can be thought of as the number of resolvable points in an image (i.e. the system’s field-of-view (FOV) divided by the size of its point spread function (PSF) [1, 2])
Eliminating the requirement for long-distance mechanical scanning means that acquisition is faster and less expensive, while focus requirements are relaxed by the larger DOF of low-numerical apertures (NAs) objectives
The transmitted light from the sample travels through a 4f system formed by the objective lens (OBJ) and a single lens
Summary
The space-bandwidth product (SBP) metric characterizes information content transmitted through an optical system; it can be thought of as the number of resolvable points in an image (i.e. the system’s field-of-view (FOV) divided by the size of its point spread function (PSF) [1, 2]). Instead of using high-resolution optics and mechanically scanning the FOV, new approaches for high-content imaging use a low-NA objective (with a large FOV) and build up higher resolution by computationally combining a sequence of lowresolution measurements [12,13,14,15,16,17,18,19,20,21,22,23,24,25] Such approaches typically illuminate the sample with customized patterns that encode high-resolution sample information into lowresolution features, which can be measured. Eliminating the requirement for long-distance mechanical scanning means that acquisition is faster and less expensive, while focus requirements are relaxed by the larger DOF of low-NA objectives
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