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Live, Video-Rate Super-Resolution Microscopy Using Structured Illumination and Rapid GPU-Based Parallel Processing

Published online by Cambridge University Press:  09 March 2011

Jonathan Lefman ,
Keana Scott  and
Stephan Stranick
Jonathan Lefman
Affiliation:
TheNational Institute of Standards and Technology, Gaithersburg, Maryland 20899, USA Oncogenomics Section, Pediatric Oncology Branch, Advanced Technology Center, National Cancer Institute, National Institutes of Health, Gaithersburg, MD, USA
Keana Scott
Affiliation:
TheNational Institute of Standards and Technology, Gaithersburg, Maryland 20899, USA
Stephan Stranick*
Affiliation:
TheNational Institute of Standards and Technology, Gaithersburg, Maryland 20899, USA
*
Corresponding author. E-mail: [email protected]
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Abstract

Structured illumination fluorescence microscopy is a powerful super-resolution method that is capable of achieving a resolution below 100 nm. Each super-resolution image is computationally constructed from a set of differentially illuminated images. However, real-time application of structured illumination microscopy (SIM) has generally been limited due to the computational overhead needed to generate super-resolution images. Here, we have developed a real-time SIM system that incorporates graphic processing unit (GPU) based in-line parallel processing of raw/differentially illuminated images. By using GPU processing, the system has achieved a 90-fold increase in processing speed compared to performing equivalent operations on a multiprocessor computer—the total throughput of the system is limited by data acquisition speed, but not by image processing. Overall, more than 350 raw images (16-bit depth, 512 × 512 pixels) can be processed per second, resulting in a maximum frame rate of 39 super-resolution images per second. This ultrafast processing capability is used to provide immediate feedback of super-resolution images for real-time display. These developments are increasing the potential for sophisticated super-resolution imaging applications.

Keywords

structured illuminationfluorescence microscopyCUDAGPUreal timesuper-resolution
Type
Biological Applications
Information
Microscopy and Microanalysis , Volume 17 , Issue 2 , April 2011 , pp. 191 - 196
Copyright
Copyright © Microscopy Society of America 2011

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Footnotes

Certain commercial equipment, instruments, or materials are identified in this document. Such identification does not imply recommendation or endorsement by the National Institute of Standards and Technology, nor does it imply that the products identified are necessarily the best available for the purpose.

References

REFERENCES

Betzig, E., Patterson, G.H., Sougrat, R., Lindwasser, O.W., Olenych, S., Bonifacino, J.S., Davidson, M.W., Lippincott-Schwartz, J. & Hess, H.F. (2006). Imaging intracellular fluorescent proteins at nanometer resolution. Science 313, 1642–1645.CrossRefGoogle ScholarPubMed
Beversluis, M., Bryant, G. & Stranick, S. (2008). Effects of inhomogeneous fields in superresolving structured-illumination microscopy. J Opt Soc Am A 25, 1371–1377.CrossRefGoogle ScholarPubMed
Castano Diez, D., Mueller, H. & Frangakis, A.S. (2007). Implementation and performance evaluation of reconstruction algorithms on graphics processors. J Struct Biol 157, 288–295.CrossRefGoogle ScholarPubMed
Dyba, M. & Hell, S.W. (2002). Focal spots of size lambda/23 open up far-field fluorescence microscopy at 33 nm axial resolution. Phys Rev Lett 88, 163901.CrossRefGoogle ScholarPubMed
Gustafsson, M.G. (2000). Surpassing the lateral resolution limit by a factor of two using structured illumination microscopy. J Microsc 198, 82–87.CrossRefGoogle ScholarPubMed
Kner, P., Chhun, B.B., Griffis, E.R., Winoto, L. & Gustafsson, M.G. (2009). Super-resolution video microscopy of live cells by structured illumination. Nat Methods 6, 339–342.CrossRefGoogle ScholarPubMed
Neil, M.A., Juskaitis, R. & Wilson, T. (1997). Method of obtaining optical sectioning by using structured light in a conventional microscope. Opt Lett 22, 1905–1907.CrossRefGoogle Scholar
Neil, M.A., Squire, A., Juskaitis, R., Bastiaens, P.I. & Wilson, T. (2000). Wide-field optically sectioning fluorescence microscopy with laser illumination. J Microsc 197, 1–4.CrossRefGoogle ScholarPubMed
Rust, M.J., Bates, M. & Zhuang, X.W. (2006). Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM). Nat Methods 3, 793–795.CrossRefGoogle ScholarPubMed
Schmeisser, M., Burkhard, C., Heisen, B.C., Luettich, M., Busche, B., Hauer, F., Koske, T., Knauber, K. & Stark, H. (2009). Parallel, distributed and GPU computing technologies in single-particle electron microscopy. Acta Crystallogr D Biol Crystallogr 65, 659–671.CrossRefGoogle ScholarPubMed
Shimobaba, T., Sato, Y., Miura, J., Takenouchi, M. & Ito, T. (2008). Real-time digital holographic microscopy using the graphic processing unit. Opt Express 16, 11776–11781.CrossRefGoogle ScholarPubMed
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