Multi-resolution analysis enables fidelity-ensured deconvolution for fluorescence microscopy

Yiwei Hou; Wenyi Wang; Yunzhe Fu; Xichuan Ge; Meiqi Li; Peng Xi

doi:10.1186/s43593-024-00073-7

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Multi-resolution analysis enables fidelity-ensured deconvolution for fluorescence microscopy

光：快讯（英文） 2024年4卷

RESEARCH ARTICLE | Updated：2024-09-25

- Multi-resolution analysis enables fidelity-ensured deconvolution for fluorescence microscopy
- Multi-resolution analysis enables fidelity-ensured deconvolution for fluorescence microscopy
- Affiliations：
  
  Department of Biomedical Engineering, College of Future Technology, Peking University, Beijing 100871, China
  National Biomedical Imaging Center, Peking University, Beijing 100871, China
  Airy Technologies Co., Ltd., Beijing 100086, China
  School of Life Sciences, Peking University, Beijing 100871, China
- Author bio：
  
  *Meiqi Lilimeiqi@pku.edu.cn
  Peng Xixipeng@pku.edu.cn
- Funds：
- DOI：10.1186/s43593-024-00073-7
  中图分类号：
- 收稿日期：2024-02-29，
  
  修回日期：2024-06-13，
  
  录用日期：2024-06-28，
  
  网络出版日期：2024-08-06，
  
  纸质出版日期：2024-12
- Accepted：
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Multi-resolution analysis enables fidelity-ensured deconvolution for fluorescence microscopy[J]. 光：快讯（英文）, 2024,4.

Yiwei Hou, Wenyi Wang, Yunzhe Fu, et al. Multi-resolution analysis enables fidelity-ensured deconvolution for fluorescence microscopy[J]. Elight, 2024, 4.
Multi-resolution analysis enables fidelity-ensured deconvolution for fluorescence microscopy[J]. 光：快讯（英文）, 2024,4. DOI： 10.1186/s43593-024-00073-7.

Yiwei Hou, Wenyi Wang, Yunzhe Fu, et al. Multi-resolution analysis enables fidelity-ensured deconvolution for fluorescence microscopy[J]. Elight, 2024, 4. DOI： 10.1186/s43593-024-00073-7.

摘要

Abstract

Fluorescence microscopic imaging is essentially a convolution process distorted by random noise

limiting critical parameters such as imaging speed

duration

and resolution. Though algorithmic compensation has shown great potential to enhance these pivotal aspects

its fidelity remains questioned. Here we develop a physics-rooted computational resolution extension and denoising method with ensured fidelity. Our approach employs a multi-resolution analysis (MRA) framework to extract the two main characteristics of fluorescence images against noise: across-edge contrast

and along-edge continuity. By constraining the two features in a model-solution framework using framelet and curvelet

we develop MRA deconvolution algorithms

which improve the signal-to-noise ratio (SNR) up to 10 dB higher than spatial derivative based penalties

and can provide up to two-fold fidelity-ensured resolution improvement rather than the artifact-prone Richardson-Lucy inference. We demonstrate our methods can improve the performance of various diffraction-limited and super-resolution microscopies with ensured fidelity

enabling accomplishments of more challenging imaging tasks.

关键词

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references

J.W. Lichtman , J.A. Conchello , Fluorescence microscopy . Nat. Methods 2 , 910 – 919 ( 2005 )

E. Betzig et al . , Imaging intracellular fluorescent proteins at nanometer resolution . Science 313 , 1642 – 1645 ( 2006 )

M.G.L. Gustafsson , Surpassing the lateral resolution limit by a factor of two using structured illumination microscopy. short communication . J. Microsc . 198 , 82 – 87 ( 2000 )

S.W. Hell , J. Wichmann , Breaking the diffraction resolution limit by stimulated emission: stimulated-emission-depletion fluorescence microscopy . Opt. Lett. ( 1994 ). https://doi.org/10.1364/OL.19.000780 https://doi.org/10.1364/OL.19.000780

M.J. Rust , M. Bates , X. Zhuang , Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM) . Nat. Methods 3 , 793 – 796 ( 2006 )

L.L. Wang et al . , Hybrid rhodamine fluorophores in the visible/NIR region for biological imaging . Angewandte Chemie-International Edition 58 , 14026 – 14043 ( 2019 )

Q.S. Zheng et al . , Ultra-stable organic fluorophores for single-molecule research . Chem. Soc. Rev . 43 , 1044 – 1056 ( 2014 )

K. Chu et al . , Image reconstruction for structured-illumination microscopy with low signal level . Opt. Express ( 2014 ). https://doi.org/10.1364/OE.22.008687 https://doi.org/10.1364/OE.22.008687

X. Huang et al . , Fast, long-term, super-resolution imaging with Hessian structured illumination microscopy . Nat. Biotechnol . 36 , 451 – 459 ( 2018 )

W. Zhao et al . , Sparse deconvolution improves the resolution of live-cell super-resolution fluorescence microscopy . Nat. Biotechnol . 40 , 606 – 617 ( 2021 )

J. Chen et al . , Three-dimensional residual channel attention networks denoise and sharpen fluorescence microscopy image volumes . Nat. Methods 18 , 678 – 687 ( 2021 )

L. Jin et al . , Deep learning enables structured illumination microscopy with low light levels and enhanced speed . Nat. Commu. ( 2020 ). https://doi.org/10.1038/s41467-020-15784-x https://doi.org/10.1038/s41467-020-15784-x

X. Li et al . , Real-time denoising enables high-sensitivity fluorescence time-lapse imaging beyond the shot-noise limit . Nat. Biotechnol. ( 2022 ). https://doi.org/10.1038/s41587-022-01450-8 https://doi.org/10.1038/s41587-022-01450-8

Y. Li et al . , Incorporating the image formation process into deep learning improves network performance . Nat. Methods 19 , 1427 – 1437 ( 2022 )

C. Qiao et al . , Evaluation and development of deep neural networks for image super-resolution in optical microscopy . Nat. Methods 18 , 194 – 202 ( 2021 )

C. Qiao et al . , Rationalized deep learning super-resolution microscopy for sustained live imaging of rapid subcellular processes . Nat. Biotechnol. ( 2022 ). https://doi.org/10.1038/s41587-022-01471-3 https://doi.org/10.1038/s41587-022-01471-3

Z. Wang et al . , Real-time volumetric reconstruction of biological dynamics with light-field microscopy and deep learning . Nat. Methods 18 , 551 – 556 ( 2021 )

D.L. Snyder , M.I. Miller , The Use of Sieves to Stabilize Images Produced with the EM Algorithm for Emission Tomography . IEEE Trans. Nucl. Sci . 32 , 3864 – 3872 ( 1985 )

White, R.L. in Instrumentation in Astronomy VIII ( 1994 ).

A. Beck , M. Teboulle , A fast iterative shrinkage-thresholding algorithm for linear inverse problems . SIAM J. Imag. Sci . 2 , 183 – 202 ( 2009 )

A. Haar , Zur theorie der orthogonalen funktionensysteme . Math. Annalen ( 1910 ). https://doi.org/10.1007/BF01456326 https://doi.org/10.1007/BF01456326

A. Grossmann , J. Morlet , Decomposition of Hardy functions into square integrable wavelets of constant shape . SIAM J. Math. Anal . 15 , 723 – 736 ( 1984 )

J.J. Koenderink , The structure of images . Biol. Cybern . 50 , 363 – 370 ( 1984 )

S.G. Mallat , Multifrequency channel decompositions of images and wavelet models . IEEE Trans. Acoust. Speech Signal Process . 37 , 2091 – 2110 ( 1989 )

S.G. Mallat , A theory for multiresolution signal decomposition: the wavelet representation . IEEE Trans. Pattern Anal. Mach. Intell . 11 , 674 – 693 ( 1989 )

I. Daubechies , Orthonormal bases of compactly supported wavelets . Commun. Pure Appl. Math . 41 , 909 – 996 ( 1988 )

I. Daubechies , The wavelet transform, time-frequency localization and signal analysis . IEEE Trans. Inf. Theory 36 , 961 – 1005 ( 1990 )

A. Aldroubi et al . , Wavelet Appl . Signal Image Proc . 3169 , 389 – 399 ( 1997 )

M.R. Banham , A.K.J.I.T.O.I.P. Katsaggelos , Spatially adaptive wavelet-based multiscale image restoration . IEEE Trans. Image Proc . 5 , 619 – 634 ( 1996 )

M.A. Figueiredo , R.D. Nowak , IEEE Int . Conf. Image Proc . 2 , 782 ( 2005 )

A.S. Lewis , G. Knowles , Image compression using the 2-D wavelet transform . IEEE Trans. Image Process . 1 , 244 – 250 ( 1992 )

J.-L. Starck , A. Bijaoui , Filtering and deconvolution by the wavelet transform . Signal Process . 35 , 195 – 211 ( 1994 )

J.D. Villasenor , B. Belzer , J. Liao , Wavelet filter evaluation for image compression . IEEE Trans. Image Process . 4 , 1053 – 1060 ( 1995 )

S.S. Chen , D.L. Donoho , M. Saunders , Atomic decomposition by basis pursuit . SIAM Rev . 43 , 129 – 159 ( 2001 )

E. Candes , L. Demanet , D. Donoho , L. Ying , simulation fast discrete curvelet transforms . Multiscale Mod. Simul . 5 , 861 – 899 ( 2006 )

E.J. Candès , D. Donoho , New tight frames of curvelets and optimal representations of objects with piecewise C2 singularities . Commun. Pure Appl. Math . 57 , 219 – 266 ( 2004 )

I. Daubechies , B. Han , A. Ron , Z. Shen , Framelets: MRA-based constructions of wavelet frames . Appl. Comput. Harmon. Anal . 14 , 1 – 46 ( 2003 )

M.N. Do , M. Vetterli , The finite ridgelet transform for image representation . IEEE Trans. Image Process . 12 , 16 – 28 ( 2003 )

S. Mallat , G. Peyré , A review of bandlet methods for geometrical image representation . Numer. Algor . 44 , 205 – 234 ( 2007 )

J.S. Bredfeldt et al . , Computational segmentation of collagen fibers from second-harmonic generation images of breast cancer . J. Biomed Optics ( 2014 ). https://doi.org/10.1117/1.JBO.19.1.016007 https://doi.org/10.1117/1.JBO.19.1.016007

B. Goyal , A. Dogra , S. Agrawal , B.S. Sohi , A. Sharma , Image denoising review: From classical to state-of-the-art approaches . Inf Fusion 55 , 220 – 244 ( 2020 )

Y. Liu , S.P. Liu , Z.F. Wang , A general framework for image fusion based on multi-scale transform and sparse representation . Inf Fusion 24 , 147 – 164 ( 2015 )

J. Cai et al . , Framelet based Blind Motion De-blurring from a Single Image . IEEE Trans. Image Process . 21 , 562 – 572 ( 2012 )

Aldroubi, A. et al . in Wavelet Applications in Signal and Image Processing VIII ( 2000 ).

M.A. Unser , I.W. Selesnick , A. Aldroubi , K.Y. Li , A.F. Laine , Wavelets Appl . Signal Image Proc. X ( 2003 ). https://doi.org/10.1117/12.504896 https://doi.org/10.1117/12.504896

J. Nixon-Abell et al . , Increased spatiotemporal resolution reveals highly dynamic dense tubular matrices in the peripheral ER . Science ( 2016 ). https://doi.org/10.1126/science.aaf3928 https://doi.org/10.1126/science.aaf3928

M.J. Phillips , G.K. Voeltz , Structure and function of ER membrane contact sites with other organelles . Nat. Rev. Mol. Cell Biol . 17 , 69 – 82 ( 2015 )

I. Arganda-Carreras et al . , Trainable Weka segmentation: a machine learning tool for microscopy pixel classification . Bioinformatics 33 , 2424 – 2426 ( 2017 )

A. Descloux , K.S. Grußmayer , A. Radenovic , Parameter-free image resolution estimation based on decorrelation analysis . Nat. Methods 16 , 918 – 924 ( 2019 )

S. Culley et al . , Quantitative mapping and minimization of super-resolution optical imaging artifacts . Nat. Methods 15 , 263 – 266 ( 2018 )

C. Fang et al . , Minutes-timescale 3D isotropic imaging of entire organs at subcellular resolution by content-aware compressed-sensing light-sheet microscopy . Nat. Commun. ( 2021 ). https://doi.org/10.1038/s41467-020-20329-3 https://doi.org/10.1038/s41467-020-20329-3

B. Chen et al . , Resolution doubling in light-sheet microscopy via oblique plane structured illumination . Nat. Methods 19 , 1419 – 1426 ( 2022 )

C.M. Hobson et al . , Practical considerations for quantitative light sheet fluorescence microscopy . Nat. Methods 19 , 1538 – 1549 ( 2022 )

W.A. Prinz , A. Toulmay , T. Balla , The functional universe of membrane contact sites . Nat. Rev. Mol. Cell Biol . 21 , 7 – 24 ( 2020 )

J. Alvelid , M. Damenti , C. Sgattoni , I. Testa , Event-triggered STED imaging . Nat. Methods 19 , 1268 – 1275 ( 2022 )

D. Mahecic et al . , Event-driven acquisition for content-enriched microscopy . Nat. Methods 19 , 1262 – 1267 ( 2022 )

P.H.C. Eilers , C. Ruckebusch , Fast and simple super-resolution with single images . Sci. Rep . 12 , 11241 ( 2022 )

S. Hugelier et al . , Sparse deconvolution of high-density super-resolution images . Sci. Rep . 6 , 21413 ( 2016 )

J. Min et al . , FALCON: fast and unbiased reconstruction of high-density super-resolution microscopy data . Sci. Rep . 4 , 4577 ( 2014 )

Y. Tian et al . , Superresolution characterization of core centriole architecture . J. Cell Biol. ( 2021 ). https://doi.org/10.1083/jcb.202005103 https://doi.org/10.1083/jcb.202005103

W. Ren et al . , Visualization of cristae and mtDNA interactions via STED nanoscopy using a low saturation power probe . Light Sci. Appl . 13 , 116 ( 2024 ). https://doi.org/10.1038/s41377-024-01463-9 https://doi.org/https://doi.org/10.1038/s41377-024-01463-9

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