We propose HiLo based line-scanning temporal focusing microscopy to enhance contrast and axial confinement in deep imaging, and demonstrate its superiority by volumetric imaging of microglia and neurons in mouse brains in vivo.
Sensorless adaptive optics (AO) has been widely used in optical microscopy to improve imaging quality in scattering tissue without additional wavefront sensing devices. The traditional image metric-based sensorless AO method requires multiple frames to assess aberrated wavefront, which is time consuming and even inaccurate when the aberration becomes large due to distortion mode crosstalk. Here we propose a neural network based wavefront sensing method which can accurately predict wavefront distortions across different aberration scales in a single-shot. Compared to the traditional method, the neural network approach reduces the prediction time by over one thousand folds. We validate the superior performances of neural network-based approach in both accuracy and speed through numerical simulations.
Spectroscopy is an important tool having already been applied in various research fields, but still limited in observation of dynamic scenes. In this paper, we propose a video rate spectroscopy via Fourier-spectral-multiplexing (FSM-VRS) which exploits both spectral and spatial sparsity. Under the computational imaging scheme, the hyperspectral datacube is first modulated by several broadband bases, and then mapped into different regions in the Fourier domain. The encoded image compressed both in spectral and spatial are finally collected by a monochrome detector. Correspondingly, the reconstruction is essentially a Fourier domain extraction and spectral dimensional back projection with low computational load. The encoding and decoding process of the FSM-VRS is simple thus can be implemented in a low cost manner. The temporary resolution of the FSM-VRS is only limited by the camera frame rate. We demonstrate the high performance of our method by quantitative evaluation on simulation data and build a prototype system experimentally for further validation.
Multimodal microscopy offers high flexibilities for biomedical observation and diagnosis. Conventional multimodal approaches either use multiple cameras or a single camera spatially multiplexing different modes. The former needs expertise demanding alignment and the latter suffers from limited spatial resolution. Here, we report an alignment-free full-resolution simultaneous fluorescence and quantitative phase imaging approach using single-pixel detectors. By combining reference-free interferometry with single-pixel detection, we encode the phase and fluorescence of the sample in two detection arms at the same time. Then we employ structured illumination and the correlated measurements between the sample and the illuminations for reconstruction. The recovered fluorescence and phase images are inherently aligned thanks to single-pixel detection. To validate the proposed method, we built a proof-of-concept setup for first imaging the phase of etched glass with the depth of a few hundred nanometers and then imaging the fluorescence and phase of the quantum dot drop. This method holds great potential for multispectral fluorescence microscopy with additional single-pixel detectors or a spectrometer. Besides, this cost-efficient multimodal system might find broad applications in biomedical science and neuroscience.
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