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Rapid image deconvolution and multiview fusion for optical microscopy


The contrast and resolution of images obtained with optical microscopes can be improved by deconvolution and computational fusion of multiple views of the same sample, but these methods are computationally expensive for large datasets. Here we describe theoretical and practical advances in algorithm and software design that result in image processing times that are tenfold to several thousand fold faster than with previous methods. First, we show that an ‘unmatched back projector’ accelerates deconvolution relative to the classic Richardson–Lucy algorithm by at least tenfold. Second, three-dimensional image-based registration with a graphics processing unit enhances processing speed 10- to 100-fold over CPU processing. Third, deep learning can provide further acceleration, particularly for deconvolution with spatially varying point spread functions. We illustrate our methods from the subcellular to millimeter spatial scale on diverse samples, including single cells, embryos and cleared tissue. Finally, we show performance enhancement on recently developed microscopes that have improved spatial resolution, including dual-view cleared-tissue light-sheet microscopes and reflective lattice light-sheet microscopes.

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Fig. 1: An unmatched back projector reduces the number of iterations required for Richardson–Lucy deconvolution.
Fig. 2: Improvements in deconvolution and registration accelerate the processing of multiview light-sheet datasets.
Fig. 3: Imaging millimeter-scale cleared-tissue volumes with isotropic micrometer-scale spatial resolution.
Fig. 4: Deep learning massively accelerates deconvolution with a spatially varying PSF.

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Data availability

The data that support the findings of this study are available from the corresponding authors upon reasonable request.

Code availability

The code used in this study is available as Supplementary Software. A code description and several test datasets are also included. Users can also download the code and updates from GitHub at;;


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We thank O. Schwartz and the Biological Imaging Section (RTB/NIAID/NIH) for supplying the confocal microscope platform and providing technical assistance with experiments, W.S. Young (NIMH) for providing early access to the V1b mouse line, J. Daniels (ASI) for advice on integrating the multi-immersion objectives into our cleared-tissue diSPIM, M. Anthony (ASI) for providing CAD drawings of our diSPIM assembly, J. Shaw (Bitplane) for help with Imaris and the neurite tracing plugin, A. Lauziere for his feedback and discussion on the neural network portion of the work, R. Christensen for testing aspects of the registration and deep learning pipelines, E. Ardiel for helping us to acquire the embryonic GCaMP3 muscle data with reflective diSPIM, N. Stuurman for advice in developing ImageJ-compatible software, R. Heintzmann for his critical evaluation of our methods and suggestions on improving the clarity of our manuscript, and H. Eden and G. Patterson for valuable feedback on the manuscript. This research was supported by the intramural research programs of the National Institute of Biomedical Imaging and Bioengineering, the National Institute of Allergy and Infectious Diseases, the National Institute of Arthritis and Musculoskeletal and Skin Diseases, the Eunice Kennedy Shriver National Institute of Child Health and Human Development, the National Institute of Mental Health and the National Cancer Institute within the National Institutes of Health, the National Natural Science Foundation of China (61525106, 61427807, U1809204) and the National Key Technology Research and Development Program of China (2017YFE0104000, 2016YFC1300302). V.P. and A.B. acknowledge support by the National Center for Advancing Translational Sciences of the National Institutes of Health through grant number UL1-TR000430, NSF award 1528911 (to V.P.) and NSF Graduate Research Fellowship Program grant number DGE-1144082 (to A.B.). A.U. acknowledges support from NSF awards PHY-1607645 and PHY-1806903. P.L.R. acknowledges support from NIH R01EB107293. H.S., D.C.-R. and P.L.R. acknowledge the Whitman and Fellows program at MBL for providing funding and space for discussions valuable to this work. D.C.-R., R.I., A.S., W.A.M. and Z.B. were supported by NIH grant number R24-OD016474, L.H.D. was supported by a Diversity Supplement to R24-OD016474 and M.W.M. was supported by F32-NS098616. Z.B. additionally acknowledges support via NIH grant number R01-GM097576 and the MSK Cancer Center Support/Core Grant (P30-CA008748). A.S. is additionally supported by grant 2019-198110 (5022) from the Chan Zuckerberg Initiative and the Silicon Valley Community Foundation. J.C.W. also acknowledges support from the Chan Zuckerberg Initiative.

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Authors and Affiliations



Conceived the project: M.G., Y.L., H.L., Y.W., H.S. Designed experiments: M.G., Y.L., Y.S., T.L., D.D.N., M.W.M., L.H.D., I.R.-S., D.G., A.B., J.C., H.V., V.P., D.C.-R., Y.W., H.S. Performed experiments: M.G., Y.L., Y.S., T.L., D.D.N., M.W.M., L.H.D., I.R.-S., D.G., A.B., J.C., H.V., S.G., T.B.U., Y.W. Prepared samples: Y.S., T.L., D.D.N., M.W.M., L.H.D., R.I., I.R.-S., A.B., J.C., H.V., T.B.U., Y.W. Built instrumentation: T.L., H.V., Y.W. Developed and tested deep learning algorithms/software: Y.L., H.L., Y.W. Developed new registration and deconvolution algorithms/software: M.G., Y.L., P.L.R., Y.W. Recognized link between medical imaging algorithms and improved deconvolution: P.L.R. Tested new registration and deconvolution algorithms/software: M.G., W.A.M., Y.W. Developed and tested big data pipeline: M.G., Y.S., Y.W. Contributed lineaging/segmentation software and expertise: D.D.N., A.S., Z.B. Contributed samples: C.M.A., M.H., A.B.C. Wrote manuscript: M.G., Y.L., Y.S., P.L.R., Y.W., H.S. with input from all authors. All authors inspected data and contributed to the drafting of the manuscript. Supervised research: V.P., J.C.W., C.M.A., M.H., W.A.M., A.B.C., A.U., T.B.U., Z.B., D.C.-R. P.L.R., H.L., Y.W., H.S. Directed research: H.S.

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Correspondence to Huafeng Liu or Yicong Wu.

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Guo, M., Li, Y., Su, Y. et al. Rapid image deconvolution and multiview fusion for optical microscopy. Nat Biotechnol 38, 1337–1346 (2020).

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