Modern Statistical Challenges in High-Resolution Fluorescence Microscopy

Conventional light microscopes have been used for centuries for the study of small length scales down to approximately 250 nm. Images from such a microscope are typically blurred and noisy, and the measurement error in such images can often be well approximated by Gaussian or Poisson noise. In the past, this approximation has been the focus of a multitude of deconvolution techniques in imaging. However, conventional microscopes have an intrinsic physical limit of resolution. Although this limit remained unchallenged for a century, it was broken for the first time in the 1990s with the advent of modern superresolution fluorescence microscopy techniques. Since then, superresolution fluorescence microscopy has become an indispensable tool for studying the structure and dynamics of living organisms. Current experimental advances go to the physical limits of imaging, where discrete quantum effects are predominant. Consequently, this technique is inherently of a non-Gaussian statistical nature, and we argue tha...

[1]  Jianqing Fan,et al.  Regularization of Wavelet Approximations , 2001 .

[2]  R. Heintzmann,et al.  Saturated patterned excitation microscopy--a concept for optical resolution improvement. , 2002, Journal of the Optical Society of America. A, Optics, image science, and vision.

[3]  Axel Munk,et al.  Shape constrained regularisation by statistical multiresolution for inverse problems: asymptotic analysis. , 2010 .

[4]  Stefan W. Hell,et al.  Fundamental improvement of resolution with a 4Pi-confocal fluorescence microscope using two-photon excitation , 1992 .

[5]  Axel Munk,et al.  Drift estimation for single marker switching based imaging schemes. , 2012, Optics express.

[6]  M. Heilemann,et al.  Subdiffraction-resolution fluorescence imaging with conventional fluorescent probes. , 2008, Angewandte Chemie.

[7]  John Fricks,et al.  Likelihood inference for particle location in fluorescence microscopy , 2010, 1011.2005.

[8]  Emmanuel J. Candès,et al.  New multiscale transforms, minimum total variation synthesis: applications to edge-preserving image reconstruction , 2002, Signal Process..

[9]  Keith A. Lidke,et al.  Simultaneous multiple-emitter fitting for single molecule super-resolution imaging , 2011, Biomedical optics express.

[10]  S. Hell,et al.  Spherical nanosized focal spot unravels the interior of cells , 2008, Nature Methods.

[11]  Jianqing Fan On the Optimal Rates of Convergence for Nonparametric Deconvolution Problems , 1991 .

[12]  Yizao Wang,et al.  Limiting distribution for the maximal standardized increment of a random walk , 2012, 1211.3301.

[13]  S W Hell,et al.  Coherent use of opposing lenses for axial resolution increase. II. Power and limitation of nonlinear image restoration. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

[14]  S. Kawata,et al.  Plasmonics for near-field nano-imaging and superlensing , 2009 .

[15]  A. Munk,et al.  Jump estimation in inverse regression , 2008, 0803.2119.

[16]  G. McConnell,et al.  2.2 Confocal Microscopy , 2012 .

[17]  Thorsten Hohage,et al.  Iteratively regularized Newton-type methods for general data misfit functionals and applications to Poisson data , 2011, Numerische Mathematik.

[18]  W. Denk,et al.  Optical stethoscopy: Image recording with resolution λ/20 , 1984 .

[19]  Rainer Heintzmann,et al.  Superresolution Multidimensional Imaging with Structured Illumination Microscopy , 2013 .

[20]  S. Hell,et al.  Fluorescence microscopy with super-resolved optical sections. , 2005, Trends in cell biology.

[21]  X. Zhuang,et al.  Statistical deconvolution for superresolution fluorescence microscopy. , 2012, Biophysical journal.

[22]  François Malgouyres,et al.  Minimizing the total variation under a general convex constraint for image restoration , 2002, IEEE Trans. Image Process..

[23]  P. Davies,et al.  Local Extremes, Runs, Strings and Multiresolution , 2001 .

[24]  Michael D. Mason,et al.  Ultra-high resolution imaging by fluorescence photoactivation localization microscopy. , 2006, Biophysical journal.

[25]  T. Wilson,et al.  Image formation in structured illumination wide-field fluorescence microscopy. , 2008, Micron.

[26]  Shaoqun Zeng,et al.  High-density localization of active molecules using Structured Sparse Model and Bayesian Information Criterion. , 2011, Optics express.

[27]  A. Munk,et al.  Multiscale change point inference , 2013, 1301.7212.

[28]  L. Duembgen,et al.  Multiscale inference about a density , 2007, 0706.3968.

[29]  R Freimann,et al.  Development of a standing‐wave fluorescence microscope with high nodal plane flatness , 1997, Journal of microscopy.

[30]  Mike Heilemann,et al.  Fluorescence microscopy beyond the diffraction limit. , 2010, Journal of biotechnology.

[31]  E. Candès,et al.  Stable signal recovery from incomplete and inaccurate measurements , 2005, math/0503066.

[32]  S W Hell,et al.  Photochromic rhodamines provide nanoscopy with optical sectioning. , 2007, Angewandte Chemie.

[33]  Joerg Bewersdorf,et al.  Optical nanoscopy: from acquisition to analysis. , 2012, Annual review of biomedical engineering.

[34]  Oliver Johnson,et al.  Thinning, Entropy, and the Law of Thin Numbers , 2009, IEEE Transactions on Information Theory.

[35]  W. Webb,et al.  Precise nanometer localization analysis for individual fluorescent probes. , 2002, Biophysical journal.

[36]  V. Spokoiny,et al.  Multiscale testing of qualitative hypotheses , 2001 .

[37]  D. Eisenberg,et al.  Protein function in the post-genomic era , 2000, Nature.

[38]  E. Veklerov,et al.  Stopping Rule for the MLE Algorithm Based on Statistical Hypothesis Testing , 1987, IEEE Transactions on Medical Imaging.

[39]  Richard M. Leahy,et al.  Fast MLE for SPECT using an intermediate polar representation and a stopping criterion , 1988 .

[40]  S. Mallat,et al.  Thresholding estimators for linear inverse problems and deconvolutions , 2003 .

[41]  Stephan J. Sigrist,et al.  Bruchpilot Promotes Active Zone Assembly, Ca2+ Channel Clustering, and Vesicle Release , 2006, Science.

[42]  B. Silverman,et al.  Wavelet decomposition approaches to statistical inverse problems , 1998 .

[43]  L. Lucy An iterative technique for the rectification of observed distributions , 1974 .

[44]  Axel Munk,et al.  On the self-regularization property of the EM algorithm for Poisson inverse problems. , 2010 .

[45]  S. Hess,et al.  Precisely and accurately localizing single emitters in fluorescence microscopy , 2014, Nature Methods.

[46]  Ja-Yong Koo,et al.  Poisson intensity estimation for tomographic data using a wavelet shrinkage approach , 2002, IEEE Trans. Inf. Theory.

[47]  G. B. David,et al.  The zeiss-Nomarski differential interference equipment for transmitted-light microscopy. , 1969, Zeitschrift fur wissenschaftliche Mikroskopie und mikroskopische Technik.

[48]  Daniel L. Farkas,et al.  Enhancement of axial resolution in fluorescence microscopy by standing-wave excitation , 1993, Nature.

[49]  Ronald R. Coifman,et al.  Combining the Calculus of Variations and Wavelets for Image Enhancement , 2000 .

[50]  Mark Bates,et al.  Super-resolution fluorescence microscopy. , 2009, Annual review of biochemistry.

[51]  Zakhar Kabluchko,et al.  Extremes of the standardized Gaussian noise , 2010, 1007.0312.

[52]  A. Sakdinawat,et al.  Nanoscale X-ray imaging , 2009 .

[53]  P. Bühlmann,et al.  Boosting With the L2 Loss , 2003 .

[54]  E. Abbe Beiträge zur Theorie des Mikroskops und der mikroskopischen Wahrnehmung , 1873 .

[55]  Mohamed-Jalal Fadili,et al.  Wavelets, Ridgelets, and Curvelets for Poisson Noise Removal , 2008, IEEE Transactions on Image Processing.

[56]  E. Candès,et al.  Recovering edges in ill-posed inverse problems: optimality of curvelet frames , 2002 .

[57]  Massimo Fornasier,et al.  Low-rank Matrix Recovery via Iteratively Reweighted Least Squares Minimization , 2010, SIAM J. Optim..

[58]  J. Enderlein,et al.  Influence of interface-dipole interactions on the efficiency of fluorescence light collection near surfaces. , 2003, Optics letters.

[59]  A. Munk,et al.  Exact Convergence Rate for the Maximum of Standardized Gaussian Increments , 2008 .

[60]  S. Weiss,et al.  Fast, background-free, 3D super-resolution optical fluctuation imaging (SOFI) , 2009, Proceedings of the National Academy of Sciences.

[61]  S. Hell Toward fluorescence nanoscopy , 2003, Nature Biotechnology.

[62]  R. Hochstrasser,et al.  Wide-field subdiffraction imaging by accumulated binding of diffusing probes , 2006, Proceedings of the National Academy of Sciences.

[63]  J. Goodman Introduction to Fourier optics , 1969 .

[64]  Guenther Walther,et al.  Optimal detection of a jump in the intensity of a Poisson process or in a density with likelihood ratio statistics , 2012, 1211.2859.

[65]  Lei Zhu,et al.  Faster STORM using compressed sensing , 2012, Nature Methods.

[66]  L. Stryer Fluorescence energy transfer as a spectroscopic ruler. , 1978, Annual review of biochemistry.

[67]  F. Zernike How I discovered phase contrast. , 1955, Science.

[68]  Dylan T Burnette,et al.  Bayesian localisation microscopy reveals nanoscale podosome dynamics , 2011, Nature Methods.

[69]  S. Hell Microscopy and its focal switch , 2008, Nature Methods.

[70]  David Baddeley,et al.  Visualization of Localization Microscopy Data , 2010, Microscopy and Microanalysis.

[71]  Jean-Luc Starck,et al.  Stein Block Thresholding For Image Denoising , 2008, 0809.3486.

[72]  Axel Munk,et al.  Statistical Multiresolution Estimation for Variational Imaging: With an Application in Poisson-Biophotonics , 2012, Journal of Mathematical Imaging and Vision.

[73]  Xiaowei Zhuang,et al.  Fast compressed sensing analysis for super-resolution imaging using L1-homotopy. , 2013, Optics express.

[74]  Nicolai Bissantz,et al.  A statistical stopping rule for MLEM reconstructions in PET , 2008, 2008 IEEE Nuclear Science Symposium Conference Record.

[75]  B. Masters The Development of Fluorescence Microscopy , 2010 .

[76]  Debashis Paul,et al.  Adaptation in some linear inverse problems , 2014 .

[77]  S. Hell,et al.  Breaking the diffraction resolution limit by stimulated emission: stimulated-emission-depletion fluorescence microscopy. , 1994, Optics letters.

[78]  M. Gustafsson Surpassing the lateral resolution limit by a factor of two using structured illumination microscopy , 2000, Journal of microscopy.

[79]  Axel Munk,et al.  Statistical Multiresolution Dantzig Estimation in Imaging: Fundamental Concepts and Algorithmic Framework , 2011, ArXiv.

[80]  A. Tsybakov,et al.  Optimal change-point estimation from indirect observations , 2004, math/0407396.

[81]  Terence Tao,et al.  The Dantzig selector: Statistical estimation when P is much larger than n , 2005, math/0506081.

[82]  Antonin Chambolle,et al.  A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging , 2011, Journal of Mathematical Imaging and Vision.

[83]  E. Betzig,et al.  Live-cell photoactivated localization microscopy of nanoscale adhesion dynamics , 2008, Nature Methods.

[84]  Christian Eggeling,et al.  Fluorescence Nanoscopy in Whole Cells by Asynchronous Localization of Photoswitching Emitters , 2007, Biophysical journal.

[85]  Michael A Thompson,et al.  Super-resolution imaging in live Caulobacter crescentus cells using photoswitchable EYFP , 2008, Nature Methods.

[86]  D. Donoho Nonlinear Solution of Linear Inverse Problems by Wavelet–Vaguelette Decomposition , 1995 .

[87]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[88]  Steven P. Callahan,et al.  Sample drift correction in 3D fluorescence photoactivation localization microscopy , 2011 .

[89]  David A. Agard,et al.  Sevenfold improvement of axial resolution in 3D wide-field microscopy using two objective lenses , 1995, Electronic Imaging.

[90]  M. Gustafsson,et al.  Three-dimensional resolution doubling in wide-field fluorescence microscopy by structured illumination. , 2008, Biophysical journal.

[91]  L. Shepp,et al.  A Statistical Model for Positron Emission Tomography , 1985 .

[92]  Nicolai Bissantz,et al.  Convergence Rates of General Regularization Methods for Statistical Inverse Problems and Applications , 2007, SIAM J. Numer. Anal..

[93]  E. Stelzer,et al.  Photobleaching GFP reveals protein dynamics inside live cells. , 1999, Trends in cell biology.

[94]  Volkan Cevher,et al.  An Inexact Proximal Path-Following Algorithm for Constrained Convex Minimization , 2013, SIAM J. Optim..

[95]  S. Weiss,et al.  Achieving increased resolution and more pixels with Superresolution Optical Fluctuation Imaging (SOFI) , 2010, Optics express.

[96]  S. Hell,et al.  Fluorescence microscopy with diffraction resolution barrier broken by stimulated emission. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[97]  Axel Munk,et al.  Drift estimation in sparse sequential dynamic imaging, with application to nanoscale fluorescence microscopy , 2014, 1403.1389.

[98]  A. Antoniadis,et al.  Poisson inverse problems , 2006, math/0601099.

[99]  Nicolai Bissantz,et al.  Convergence Analysis of Generalized Iteratively Reweighted Least Squares Algorithms on Convex Function Spaces , 2008, SIAM J. Optim..

[100]  M. Heilemann,et al.  Photoswitches: Key molecules for subdiffraction‐resolution fluorescence imaging and molecular quantification , 2009 .

[101]  J. Pendry,et al.  Negative refraction makes a perfect lens , 2000, Physical review letters.

[102]  A. Munk,et al.  Fluorescence nanoscopy by polarization modulation and polarization angle narrowing , 2014, Nature Methods.

[103]  Marc Teboulle,et al.  A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems , 2009, SIAM J. Imaging Sci..

[104]  Cl'ement Marteau,et al.  Intensity estimation of non-homogeneous Poisson processes from shifted trajectories , 2011, 1105.3625.

[105]  M. Bertero,et al.  Image deblurring with Poisson data: from cells to galaxies , 2009 .

[106]  M. Gustafsson Nonlinear structured-illumination microscopy: wide-field fluorescence imaging with theoretically unlimited resolution. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[107]  J. Lippincott-Schwartz,et al.  Imaging Intracellular Fluorescent Proteins at Nanometer Resolution , 2006, Science.

[108]  T. Tony Cai,et al.  Nonparametric regression in exponential families , 2010, 1010.3836.

[109]  W. Webb,et al.  Thermodynamic Fluctuations in a Reacting System-Measurement by Fluorescence Correlation Spectroscopy , 1972 .

[110]  S. Hell,et al.  Ground-state-depletion fluorscence microscopy: A concept for breaking the diffraction resolution limit , 1995 .

[111]  Andreas Schönle,et al.  Resolution scaling in STED microscopy. , 2008, Optics express.

[112]  Extremes of independent chi-square random vectors , 2012 .

[113]  X. Zhuang,et al.  Actin, Spectrin, and Associated Proteins Form a Periodic Cytoskeletal Structure in Axons , 2013, Science.

[114]  Michael J Rust,et al.  Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM) , 2006, Nature Methods.

[115]  Axel Munk,et al.  Multiscale methods for shape constraints in deconvolution: Confidence statements for qualitative features. , 2011, 1107.1404.

[116]  S. Hell,et al.  Fluorescence nanoscopy by ground-state depletion and single-molecule return , 2008, Nature Methods.

[117]  William H. Richardson,et al.  Bayesian-Based Iterative Method of Image Restoration , 1972 .

[118]  Otmar Scherzer,et al.  The residual method for regularizing ill-posed problems , 2009, Appl. Math. Comput..

[119]  Yiqiu Dong,et al.  Automated Regularization Parameter Selection in Multi-Scale Total Variation Models for Image Restoration , 2011, Journal of Mathematical Imaging and Vision.