https://3demmethods.i2pc.es/index.php?action=history&feed=atom&title=2020Moebel_MCMC 2026-05-24T22:00:58Z Revision history for this page on the wiki MediaWiki 1.44.2 https://3demmethods.i2pc.es/index.php?title=2020Moebel_MCMC&diff=3831&oldid=prev 2021-01-08T09:23:36Z

<p>Created page with &quot;== Citation == Moebel, E., Kervrann, C. A Monte Carlo framework for missing wedge restoration and noise removal in cryo-electron tomography. Journal of Structural Biology: X,...&quot;</p> <p><b>New page</b></p><div>== Citation ==<br /> <br /> Moebel, E., Kervrann, C. A Monte Carlo framework for missing wedge restoration and noise removal in cryo-electron tomography. Journal of Structural Biology: X, 2020, 4, 100013 <br /> <br /> == Abstract ==<br /> <br /> We propose a statistical method to address an important issue in cryo-electron tomography image analysis: reduction of a high amount of noise and artifacts due to the presence of a missing wedge (MW) in the spectral domain. The method takes as an input a 3D tomogram derived from limited-angle tomography, and gives as an output a 3D denoised and artifact compensated volume. The artifact compensation is achieved by filling up the MW with meaningful information. To address this inverse problem, we compute a Minimum Mean Square Error (MMSE) estimator of the uncorrupted image. The underlying high-dimensional integral is computed by applying a dedicated Markov Chain Monte-Carlo (MCMC) sampling procedure based on the Metropolis-Hasting (MH) algorithm. The proposed MWR (Missing Wedge Restoration) algorithm can be used to enhance visualization or as a pre-processing step for image analysis, including segmentation and classification of macromolecules. Results are presented for both synthetic data and real 3D cryo-electron images. <br /> <br /> == Keywords ==<br /> <br /> == Links ==<br /> <br /> https://www.sciencedirect.com/science/article/pii/S259015241930011X<br /> <br /> == Related software ==<br /> <br /> == Related methods ==<br /> <br /> == Comments ==</div>

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