Traditional fluorescence microscopes, limited in resolution, impede the precise identification of subcellular compartments. While super-resolution microscopes are frequently employed to compensate for this deficiency, their application in multicell or live tissue studies poses challenges due to inherent tradeoffs and high expenses. On the other side, understanding the functionality of living cells and exploring intracellular dynamics within their natural organismic environment demands sophisticated and costly equipment, which may not be affordable for all laboratories. In this investigation, we applied computational methods, specifically employed super-resolution radial fluctuations (SRRF), on the mouse thigh to capture sequences of tissue micrographs. By systematically exploring various numerical modifications and parameters, we identified specific factors that significantly enhanced the resolution of subcellular structures.
The subcellular imaging in the thigh muscle was conducted for this purpose. Standard imaging protocols encompassed capturing sequences of image series with varying frame counts at different rates, utilizing high numerical aperture (NA) objective lenses to explore multiple parameters for optimal results. Subsequently, following sequential numerical adjustments to minimize background noise and enhance signal intensity, the SRRF algorithm was employed on the image stacks. The result was frames of muscle fibers with significantly improved resolution. In the final images, discrete organelle structures and dynamics were discernible, overcoming the poor lateral resolution of the original microscope images that depicted indistinct schematics of organelles. Importantly, this technique is versatile, requiring neither specific systems nor components, and it does not entail additional costs.
Understanding living cells and imaging intracellular dynamics requires complex and expensive devices. Conventional fluorescence microscopes suffer from poor resolution, while super-resolution comes with its own tradeoffs, making multi-cell studies challenging. We apply computational techniques such as super-resolution radial fluctuations to the tissue micrograph sequences, comparing a variety of numerical modifications and parameter settings to enable imaging of subcellular structures with higher resolution, reduced background noise, and enhanced signal intensity. Discrete organelle structures and dynamics are distinguishable in the final images, despite the original images having poor lateral resolution. The resultant computational techniques are widely available for use in studies anywhere
Omnidirectional side-view images from miniaturized catadioptric endoscopes may be used to generate mosaics of the epithelium of tubular organs, enabling the longitudinal monitoring of surface pathologies. Here, previous results are extended to create three-dimensional sub-mm resolution 3.5 cm × 360° reconstructions of pediatric cardiac phantoms. From image stacks with a single annulus of best focus captured via parabolic mirrors, adjacent rings within the focused region may be used to infer depth via parallax while rings of best focus are used to color the inferred geometry. Potential applications include digital reconstruction of pediatric and small-animal organs for diagnostics and surgical guidance at near-cellular resolution.
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