Cryo-electron microscopy single particle reconstruction of virus particles using compressed sensing theory

Min Woo Kim; Jiyoung Choi; Liu Yu; Kyung Eun Lee; Sung-Sik Han; Jong Chul Ye

doi:10.1117/12.705008

28 February 2007 Cryo-electron microscopy single particle reconstruction of virus particles using compressed sensing theory

Min Woo Kim, Jiyoung Choi, Liu Yu, Kyung Eun Lee, Sung-Sik Han, Jong Chul Ye

Author Affiliations +

Proceedings Volume 6498, Computational Imaging V; 64981G (2007) https://doi.org/10.1117/12.705008
Event: Electronic Imaging 2007, 2007, San Jose, CA, United States

Abstract

Sparse object supports are often encountered in many imaging problems. For such sparse objects, recent theory of compressed sensing tells us that accurate reconstruction of objects are possible even from highly limited number of measurements drastically smaller than the Nyquist sampling limit by solving L₁ minimization problem. This paper employs the compressed sensing theory for cryo-electron microscopy (cryo-EM) single particle reconstruction of virus particles. Cryo-EM single particle reconstruction is a nice application of the compressed sensing theory because of the following reasons: 1) in some cases, due to the difficulty in sample collection, each experiment can obtain micrographs with limited number of virus samples, providing undersampled projection data, and 2) the nucleic acid of a viron is enclosed within capsid composed of a few proteins; hence the support of capsid in 3-D real space is quite sparse. In order to minimize the L₁ cost function derived from compressed sensing, we develop a novel L₁ minimization method based on the sliding mode control theory. Experimental results using synthetic and real virus data confirm that the our algorithm provides superior reconstructions of 3-D viral structures compared to the conventional reconstruction algorithms.

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Min Woo Kim, Jiyoung Choi, Liu Yu, Kyung Eun Lee, Sung-Sik Han, and Jong Chul Ye "Cryo-electron microscopy single particle reconstruction of virus particles using compressed sensing theory", Proc. SPIE 6498, Computational Imaging V, 64981G (28 February 2007); https://doi.org/10.1117/12.705008

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