Influence of drift correction precision on super-resolution localization microscopy.

Super-resolution localization microscopy (SRLM) breaks the diffraction limit successfully and improves the resolution of optical imaging systems by nearly an order of magnitude. However, SRLM typically takes several minutes or longer to collect a sufficient number of image frames that are required for reconstructing a final super-resolution image. During this long image acquisition period, system drift should be tightly controlled to ensure the imaging quality; thus, several drift correction methods have been developed.

Real-time image resolution measurement for single molecule localization microscopy.

Recent advancements in single molecule localization microscopy (SMLM) have demonstrated outstanding potential applications in high-throughput and high-content screening imaging. One major limitation to such applications is to find a way to optimize imaging throughput without scarifying image quality, especially the homogeneity in image resolution, during the imaging of hundreds of field-of-views (FOVs) in heterogeneous samples. Here we introduce a real-time image resolution measurement method for SMLM to solve this problem.

PCIe-based FPGA-GPU heterogeneous computation for real-time multi-emitter fitting in super-resolution localization microscopy.

Real-time multi-emitter fitting is a key technology for advancing super-resolution localization microscopy (SRLM), especially when it is necessary to achieve dynamic imaging quality control and/or optimization of experimental conditions. However, with the increase of activation densities, the requirements in the computing resources would increase rapidly due to the complexity of the fitting algorithms, making it difficult to realize real-time multi-emitter fitting for emitter density more than 0.6 mol/µm in large field of view (FOV), even after acceleration with the popular Graphics Processing Unit (GPU) computation.

Two-color super-resolution localization microscopy via joint encoding of emitter location and color.

Multi-color super-resolution localization microscopy (SRLM) provides great opportunities for studying the structural and functional details of biological samples. However, current multi-color SRLM methods either suffer from medium to high crosstalk, or require a dedicated optical system and a complicated image analysis procedure. To address these problems, here we propose a completely different method to realize multi-color SRLM.

High-precision 3D drift correction with differential phase contrast images.

Single molecule localization microscopy (SMLM) usually requires long image acquisition time at the order of minutes and thus suffers from sample drift, which deteriorates image quality. A drift estimation method with high precision is typically used in SMLM, which can be further combined with a drift compensation device to enable active microscope stabilization. Among all the reported methods, the drift estimation method based on bright-field image correlation requires no extra sample preparation or complicated modification to the imaging setup.

Accelerating multi-emitter localization in super-resolution localization microscopy with FPGA-GPU cooperative computation.

The real-time multi-emitter localization method is essential for advancing high-throughput super-resolution localization microscopy (HT-SRLM). In the past decade, the graphics processing unit (GPU) computation has been dominantly used to accelerate the execution speed of the multi-emitter localization method. However, if HT-SRLM is combined with a scientific complementary metal-oxide-semiconductor (sCMOS) camera working at full frame rate, real-time image processing is still difficult to achieve using this acceleration approach, thus resulting in a massive data storage challenge and even system crash.

Computational framework for generating large panoramic super-resolution images from localization microscopy.

Combining super-resolution localization microscopy with pathology creates new opportunities for biomedical researches. This combination requires a suitable image mosaic method for generating a panoramic image from many overlapping super-resolution images. However, current image mosaic methods are not suitable for this purpose.