A Bayesian adaptive basis algorithm for single particle reconstruction

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• 作者：Alp Kucukelbir^a ; ^{alp.kucukelbir@yale.edu} ; Fred J. Sigworth^a ; ^b ; Hemant D. Tagare^a ; ^c
• 关键词：Single particle reconstruction ; Bayesian ; Adaptive basis ; Wavelet ; Frame ; Particle masking
• 刊名：Journal of Structural Biology
• 出版年：2012
• 期刊代码：92_10478477
• 类别：ph
• 出版时间：July, 2012
• 卷：179
• 期：1
• 页码：56-67
• 文件大小：1206 K

摘要

Traditional single particle reconstruction methods use either the Fourier or the delta function basis to represent the particle density map. This paper proposes a more flexible algorithm that adaptively chooses the basis based on the data. Because the basis adapts to the data, the reconstruction resolution and signal-to-noise ratio (SNR) is improved compared to a reconstruction with a fixed basis. Moreover, the algorithm automatically masks the particle, thereby separating it from the background. This eliminates the need for ad hoc filtering or masking in the refinement loop. The algorithm is formulated in a Bayesian maximum-a-posteriori framework and uses an efficient optimization algorithm for the maximization. Evaluations using simulated and actual cryogenic electron microscopy data show resolution and SNR improvements as well as the effective masking of particle from background.