Journal: Nature Communications

Article Title: Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy

doi: 10.1038/s41467-024-55267-x

Figure Lengend Snippet: a Schematic. Left : Fluorescence microscopy volumes are collected and near-diffraction-limited images from the shallow side of each stack are synthetically degraded to resemble aberrated images deeper into the stack. A neural network (e.g., 3D RCAN) is trained to reverse this degradation given the ground truth on the shallow side of the stack, and the trained neural network (DeAbe model) subsequently applied to images throughout the stack, improving contrast and resolution. Right : More detailed view of synthetic degradation process. Zernike basis functions and associated coefficients (coeffs) are used to add random aberrations by modifying the ideal point spread function (iPSF) to generate an aberrated PSF (aPSF). Ground truth images (GT) are Fourier transformed (FT) and multiplied by the ratio of the Fourier transformed aberrated and ideal PSFs (essentially a modified optical transfer function, mOTF). Inverse Fourier transforming (IFT) the result and adding noise generates the synthetically aberrated images. See “ Methods ” for further details. OBJ: objective lens. b Simulated three-dimensional phantoms comparing maximum intensity projections of aberrated input image (left, random aberration with root mean square (RMS) wavefront distortion of 2 radians and Poisson noise added for an SNR of ~16, corresponding PSF in inset), network prediction (DeAbe) given aberrated input (middle), and ground truth (GT, right). Higher magnification views of dashed rectangular region are shown in ( c ) (maximum intensity projection) and ( d ) (single plane), additionally showing restoration given blind deconvolution (Blind Decon), Richardson-Lucy deconvolution with diffraction-limited PSF (RL Decon 1), Richardson-Lucy deconvolution with aberrated PSF (RL Decon 2). Yellow arrows indicate a reference structure for visual comparison. Twenty iterations were used for RL deconvolution and ten for blind deconvolution. e As in ( c , d ) but showing axial plane along dashed blue line in ( b ). f Quantitative comparisons for the restorations shown in ( b – e ) using structural similarity index (SSIM, top) and peak signal-to-noise ratio (PSNR, bottom). Means and standard deviations are shown for 100 simulations (10 independent phantom volumes, each aberrated with 10 randomly chosen aberrations). Scale bars: 5 μm ( b ) and 2.5 μm ( c–e ). See also Supplementary Figs. – .

Article Snippet: Higher magnification views of dashed rectangular region are shown in ( c ) (maximum intensity projection) and ( d ) (single plane), additionally showing restoration given blind deconvolution (Blind Decon), Richardson-Lucy deconvolution with diffraction-limited PSF (RL Decon 1), Richardson-Lucy deconvolution with aberrated PSF (RL Decon 2).

Techniques: Fluorescence, Microscopy, Transformation Assay, Modification, Comparison

Journal: Nature Communications

Article Title: Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy

doi: 10.1038/s41467-024-55267-x

Figure Lengend Snippet: a Live C. elegans embryos expressing a pan-nuclear GFP histone marker were imaged with light sheet microscopy ( i , left column) and the raw data processed with Richardson-Lucy deconvolution ( ii , 10 iterations, middle column) or with a trained DeAbe model ( iii , right column). First two rows show single planes 20.0 and 27.7 μm into the sample, third row shows axial view. Comparative line profiles through blue, yellow, and green lines are shown in insets, comparing ability to discriminate nuclei. Red arrow highlights nuclei for visual comparison. See also Supplementary Movie . b NK-92 cells stained with Alexa Fluor 555 wheat germ agglutinin and embedded in collagen matrices were fixed and imaged with instant SIM, a super-resolution imaging technique. Left: raw data, right: after application of DeAbe and deconvolution (DeAbe + , 20 iterations Richardson-Lucy). Lateral maximum intensity projections (MIP, top) or single axial planes (bottom) are shown in ( b ), and ( c , d ) show higher magnification views corresponding to green ( c ) or blue ( d ) dashed rectangular regions in ( b ). Colored arrows in ( c, d ) highlight fine features obscured in the raw data and better revealed in the DeAbe+ reconstructions. See also Supplementary Movie , Supplementary Fig. . e Live cardiac tissue containing cardiomyocytes expressing Tomm20-GFP was imaged with two photon microscopy. Raw data (left) are compared with DeAbe prediction (right) at indicated depths, with insets showing corresponding Fourier transform magnitudes. Blue circles in Fourier insets in ( e ) indicate 1/300 nm −1 spatial frequency just beyond resolution limit. See also Supplementary Movie . f Higher magnification views of white dashed rectangular region in ( e ), emphasizing recovery of mitochondrial boundaries by DeAbe model. See also Supplementary Fig. , Supplementary Movie . Scale bars: 10 μm ( a, e ); 5 μm ( b, f ); 2 μm ( c, d ); ( e ) diameter of Fourier circle: 300 nm −1 . Data shown are representative samples from N = 3 experiments.

Article Snippet: Higher magnification views of dashed rectangular region are shown in ( c ) (maximum intensity projection) and ( d ) (single plane), additionally showing restoration given blind deconvolution (Blind Decon), Richardson-Lucy deconvolution with diffraction-limited PSF (RL Decon 1), Richardson-Lucy deconvolution with aberrated PSF (RL Decon 2).

Techniques: Expressing, Marker, Microscopy, Comparison, Staining, Imaging