2022
DOI: 10.1038/s41467-022-33957-8
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Isotropic reconstruction for electron tomography with deep learning

Abstract: Cryogenic electron tomography (cryoET) allows visualization of cellular structures in situ. However, anisotropic resolution arising from the intrinsic “missing-wedge” problem has presented major challenges in visualization and interpretation of tomograms. Here, we have developed IsoNet, a deep learning-based software package that iteratively reconstructs the missing-wedge information and increases signal-to-noise ratio, using the knowledge learned from raw tomograms. Without the need for sub-tomogram averaging… Show more

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Cited by 175 publications
(121 citation statements)
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“…Tomograms from resulting image stacks were reconstructed in IMOD 54 using weighted back projection after patch track alignment. The missing wedge was corrected in IsoNet 55 . Particles were visualized in these 3D volumes in ChimeraX.…”
Section: Cryo-electron Tomography Data Collection and Processingmentioning
confidence: 99%
“…Tomograms from resulting image stacks were reconstructed in IMOD 54 using weighted back projection after patch track alignment. The missing wedge was corrected in IsoNet 55 . Particles were visualized in these 3D volumes in ChimeraX.…”
Section: Cryo-electron Tomography Data Collection and Processingmentioning
confidence: 99%
“…Tilt series were drift-corrected using alignframes in IMOD 105 and 4×-binned tomograms were reconstructed by weighted-back projection in IMOD. To enhance the contrast for visualization and particle picking, tomograms were CTF-deconvolved and filtered using isonet 106 . 2D projection images were lowpass-filtered using mtffilter in IMOD.…”
Section: Tomogram Reconstruction Data Processing and Segmentationmentioning
confidence: 99%
“…Another common attempt is to couple a reconstruction technique, e.g., backprojection reconstruction, with a deep learning denoising approach. Here, denoising is applied either on the tilt-series before reconstruction [15], [16], [17], or after the reconstruction on tomograms created with traditional techniques [18], [19]. Alternatively, reconstruction methods including regularization terms have been proposed to handle this ill-posed problem, but only for 2D synthetic limited angle data [39], [40].…”
Section: Related Workmentioning
confidence: 99%
“…This is in accordance with what we found when running more than a hundred iterations with only SART in our ablation studies. We used IMOD's tomograms (bin-4 and bin-8) for training IsoNet (3 tomograms were used for training) for 20 iterations with the recommended configuration [19]. Finally, we created reconstructions with our framework, using LADMM for 2×80 iterations with and without NLM in the final two iterations.…”
Section: Influenza Tilt-seriesmentioning
confidence: 99%
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