Summary The Nyquist-Shannon criterion has never been realized in a laser-scanning mesoscopic multiphoton microscope (MPM) with a large field-of-view (FOV)-resolution ratio, especially when employing a high-frequency resonant-raster-scanning. With a high optical resolution nature, a current mesoscopic-MPM either neglects the criterion and degrades the digital resolution to twice the pixel size, or reduces the FOV and/or the raster-scanning speed to avoid aliasing . We introduce a Nyquist figure-of-merit (NFOM) parameter to characterize a laser-scanning MPM in terms of its optical-resolution retrieving ability. Based on NFOM, we define the maximum aliasing-free FOV, and subsequently, a cross-over excitation wavelength, below which the FOV becomes NFOM-constrained irrespective of an optimized optical design. We validate our idea in a custom-built mesoscopic-MPM with millimeter-scale FOV yielding an ultra-high FOV-resolution ratio of >3,000, while securing up-to a 1.6 mm Nyquist-satisfied aliasing-free FOV, a ∼400 nm lateral resolution, and a 70 M/s effective voxel-sampling rate, all at the same time.

No abstract

Optical neuronal imaging often shows ultrafine structures, such as a nerve fiber, coexisting with ultrabright structures, such as a soma with a substantially higher fluorescence-protein concentration. Owing to experimental and environmental factors, a laser-scanning multiphoton optical microscope (MPM) often encounters a high-frequency background noise that might contaminate such weak-intensity ultrafine neuronal structures. A straightforward contrast enhancement often leads to the saturation of the brighter ones, and might further amplify the high-frequency background noise. We report a digital approach called rapid denoised contrast enhancement (DCE), which digitally mimics a hardware-based adaptive/controlled illumination technique by means of digitally optimizing the signal strengths and hence the visibility of such weak-intensity structures while mostly preventing the saturation of the brightest ones. With large field-of-view (FOV) two-photon excitation fluorescence (TPEF) neuronal imaging, we validate the effectiveness of DCE over state-ofthe-art digital image processing algorithms. With compute-unified-device-architecture (CUDA)-acceleration, a real-time DCE is further enabled with a reduced time complexity.

A digitized analog signal often encounters a high-frequency noisy background which degrades the signal-to-noise ratio (SNR) particularly in case of low signal strength. Despite quite a lot of hardware-and software-based approaches have been reported to date to deal with the noise issue, it is still a challenging task to real-time retrieve the noise-contaminated low-frequency information efficiently without degrading the original bandwidth. In this paper, we report a modified unsharp-masking (UM)-based Graphics Processing Unit (GPU)-accelerated algorithm to efficiently suppress a high-frequency noisy background in a digitized two-dimensional image. The proposed idea works effectively even if noisedensity is high and signal of interest is comparable or weaker than the maximum noise level. While suppressing the noisy background, the original resolution remains least compromised. We first explore the effectiveness of the algorithm by means of simulated images and subsequently extend our demonstration towards a real-world life-science imaging application. Securing a potential for real-time applicability, we implement the algorithm via Compute Unified Device Architecture (CUDA)-acceleration and preserve a <300 µs processing time for a 1000×1000-sized 8-bit data set.

Multicolor labeling of biological samples with large volume is required for omic-level of study such as the construction of nervous system connectome. Among the various imaging method, two photon microscope has multiple advantages over traditional single photon microscope for higher resolution and could image large 3D volumes of tissue samples with superior imaging depth. However, the growing number of fluorophores for labeling underlines the urgent need for an ultrafast laser source with the capability of providing simultaneous plural excitation wavelengths for multiple fluorophores. Here, we propose and demonstrate a single-laser-based four-wavelength excitation source for two-photon fluorescence microscopy. Using a sub-100 fs 1,070-nm Yb:fiber laser to pump an ultrashort nonlinear photonic crystal fiber in the low negative dispersion region, we introduced efficient self-phase modulation and acquired a blue-shifted spectrum dual-peaked at 812 and 960 nm with 28.5% wavelength conversion efficiency. By compressing the blue-shift near-IR spectrum to 33 fs to ensure the temporal overlap of the 812 and 960 nm peaks, the so-called sum frequency effect created the third virtual excitation wavelength effectively at 886 nm. Combined with the 1,070 nm laser source as the fourth excitation wavelength, the all-fiber-format four-wavelength excitation source enabled simultaneous four-color two-photon imaging in Brainbow AAV-labeled (TagBFP, mTFP, EYFP, and mCherry) brain samples. With an increased number of excitation wavelengths and improved excitation efficiency than typical commercial femtosecond lasers, our compact four-wavelength excitation approach can provide a versatile, efficient, and easily accessible solution for multiple-color two-photon fluorescence imaging in the field of neuroscience, biomolecular probing, and clinical applications with at least four spectrally-distinct fluorophores.