Computational fluorescence microscopy constantly breaks through imaging performance through advanced opticalmodulation technologies;however, conventional theoretical modeling and experimental measurement approachesare...Computational fluorescence microscopy constantly breaks through imaging performance through advanced opticalmodulation technologies;however, conventional theoretical modeling and experimental measurement approachesare challenging to meet the demand for accurate system characterization of diverse modulations. To this end, wepropose a point spread function (PSF) decoupling method that is intrinsically compatible with the optimaldemodulation in computational microscopic imaging modality. The critical core lies in designing a sample prior-basedcomputational imaging strategy, in which a regular fluorescent sample instead of generally used sub-diffractionlimited particles acts as a system modulator to demodulate the system response. PSF consequently can becomputationally optimized through the strong support from the modulated sample prior, achieving accurate nonparametricsystem characterization and thereby avoiding the modeling difficulty and the low signal-to-noise ratiomeasurement errors of the system specificity. Experimental results across various biological tissues demonstrated andverified that the proposed PSF decoupling method enables excellent volumetric imaging comparable to confocalmicroscopy and multicolor, large depth-of-field imaging under aperture modulation. It provides a promisingmechanism of system characterization and computational demodulation for high-contrast and high-resolutionimaging of cellular and subcellular biological structures and life activities.展开更多
基金supported by grants from the National Natural Science Foundation of China(NSFC)(62275173,62175109,62371311)Shenzhen Fundamental Research Program(JCYJ20220531101204010)+1 种基金Shenzhen Higher Education Stable Support Program(20231122025852001)Scientific Instrument Developing Project of Shenzhen University(2023YQ009).
文摘Computational fluorescence microscopy constantly breaks through imaging performance through advanced opticalmodulation technologies;however, conventional theoretical modeling and experimental measurement approachesare challenging to meet the demand for accurate system characterization of diverse modulations. To this end, wepropose a point spread function (PSF) decoupling method that is intrinsically compatible with the optimaldemodulation in computational microscopic imaging modality. The critical core lies in designing a sample prior-basedcomputational imaging strategy, in which a regular fluorescent sample instead of generally used sub-diffractionlimited particles acts as a system modulator to demodulate the system response. PSF consequently can becomputationally optimized through the strong support from the modulated sample prior, achieving accurate nonparametricsystem characterization and thereby avoiding the modeling difficulty and the low signal-to-noise ratiomeasurement errors of the system specificity. Experimental results across various biological tissues demonstrated andverified that the proposed PSF decoupling method enables excellent volumetric imaging comparable to confocalmicroscopy and multicolor, large depth-of-field imaging under aperture modulation. It provides a promisingmechanism of system characterization and computational demodulation for high-contrast and high-resolutionimaging of cellular and subcellular biological structures and life activities.
