https://3demmethods.i2pc.es/index.php?action=history&feed=atom&title=2026Kreymer_EM 2026-09-01T04:15:20Z Revision history for this page on the wiki MediaWiki 1.44.2 https://3demmethods.i2pc.es/index.php?title=2026Kreymer_EM&diff=5273&oldid=prev 2026-08-31T04:37:26Z

<p>Created page with &quot;== Citation == Kreymer, S., Singer, A. and Bendory, T. 2026. Expectation-maximization for structure determination directly from cryo-em micrographs. Inverse problems and imaging. 27, (2026), 110. == Abstract == A single-particle cryo-electron microscopy (cryo- EM) measurement, called a micrograph, consists of multiple two-dimensional tomographic projections of a three-dimensional (3-D) molecular structure at unknown locations, taken under unknown viewing directions. A...&quot;</p> <p><b>New page</b></p><div>== Citation ==<br /> <br /> Kreymer, S., Singer, A. and Bendory, T. 2026. Expectation-maximization for structure determination directly from cryo-em micrographs. Inverse problems and imaging. 27, (2026), 110.<br /> <br /> == Abstract ==<br /> <br /> A single-particle cryo-electron microscopy (cryo-<br /> EM) measurement, called a micrograph, consists of multiple<br /> two-dimensional tomographic projections of a three-dimensional<br /> (3-D) molecular structure at unknown locations, taken under<br /> unknown viewing directions. All existing cryo-EM algorithmic<br /> pipelines first locate and extract the projection images, and then<br /> reconstruct the structure from the extracted images. However, if<br /> the molecular structure is small, the signal-to-noise ratio (SNR)<br /> of the data is very low, making it challenging to accurately<br /> detect projection images within the micrograph. Consequently,<br /> all standard techniques fail in low-SNR regimes. To recover<br /> molecular structures from measurements of low SNR, and in<br /> particular small molecular structures, we devise an approximate<br /> expectation-maximization algorithm to estimate the 3-D structure<br /> directly from the micrograph, bypassing the need to locate the<br /> projection images. We corroborate our computational scheme<br /> with numerical experiments and present successful structure<br /> recoveries from simulated noisy measurements.<br /> <br /> == Keywords ==<br /> <br /> == Links ==<br /> <br /> https://www.aimsciences.org//article/doi/10.3934/ipi.2026033<br /> <br /> == Related software ==<br /> <br /> == Related methods ==<br /> <br /> == Comments ==</div>

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