Structured illumination microscopy with noise-controlled image reconstructions.

Super-resolution structured illumination microscopy (SIM) has become a widely used method for biological imaging. Standard reconstruction algorithms, however, are prone to generate noise-specific artifacts that limit their applicability for lower signal-to-noise data. Here we present a physically realistic noise model that explains the structured noise artifact, which we then use to motivate new complementary reconstruction approaches. True-Wiener-filtered SIM optimizes contrast given the available signal-to-noise ratio, and flat-noise SIM fully overcomes the structured noise artifact while maintaining resolving power. Both methods eliminate ad hoc user-adjustable reconstruction parameters in favor of physical parameters, enhancing objectivity. The new reconstructions point to a trade-off between contrast and a natural noise appearance. This trade-off can be partly overcome by further notch filtering but at the expense of a decrease in signal-to-noise ratio. The benefits of the proposed approaches are demonstrated on focal adhesion and tubulin samples in two and three dimensions, and on nanofabricated fluorescent test patterns.

Adaptive illumination reduces photobleaching in structured illumination microscopy.

Photobleaching is a major factor limiting the observation time in fluorescence microscopy. We achieve photobleaching reduction in structured illumination microscopy (SIM) by locally adjusting the illumination intensities according to the sample. Adaptive SIM is enabled by a digital micro-mirror device (DMD), which provides a projection of the grayscale illumination patterns. We demonstrate a reduction in photobleaching by a factor of three in adaptive SIM compared to the non-adaptive SIM based on a spot grid scanning approach. Our proof-of-principle experiments show great potential for DMD-based microscopes to become a more useful tool in live-cell SIM imaging.

Deconvolution methods for structured illumination microscopy.

We compare two recently developed multiple-frame deconvolution approaches for the reconstruction of structured illumination microscopy (SIM) data: the pattern-illuminated Fourier ptychography algorithm (piFP) and the joint Richardson-Lucy deconvolution (jRL). The quality of the images reconstructed by these methods is compared in terms of the achieved resolution improvement, noise enhancement, and inherent artifacts. Furthermore, we study the issue of object-dependent resolution improvement by considering the modulation transfer functions derived from different types of objects. The performance of the considered methods is tested in experiments and benchmarked with a commercial SIM microscope. We find that the piFP method resolves periodic and isolated structures equally well, whereas the jRL method provides significantly higher resolution for isolated objects compared to periodic ones. Images reconstructed by the piFP and jRL algorithms are comparable to the images reconstructed using the generalized Wiener filter applied in most commercial SIM microscopes. An advantage of the discussed algorithms is that they allow the reconstruction of SIM images acquired under different types of illumination, such as multi-spot or random illumination.

Studying different illumination patterns for resolution improvement in fluorescence microscopy.

Various types of non-uniform illumination can be used for resolution improvement in fluorescence microscopy. Here we study the differences between several types of incoherent illumination patterns, such as multi-spot, line and pseudo-random patterns. This requires an imaging setup and an image reconstruction algorithm that are flexible enough to incorporate any type of illumination pattern. We employ fluorescence microscope with structured illumination generated by a Digital Micro-mirror Device (DMD) and the pattern-illuminated Fourier Ptychography reconstruction algorithm (piFP) to this end. The piFP method is modified and improved by identifying the algorithm as steepest descent optimization of a least squares function. We find that illumination patterns with regular structure are superior to those with irregular structure in terms of resolution enhancement and noise level in the reconstructed images.