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	<title>2017McLeod Zorro - Revision history</title>
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	<updated>2026-05-24T21:06:42Z</updated>
	<subtitle>Revision history for this page on the wiki</subtitle>
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	<entry>
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		<title>CoSS: Created page with &quot;== Citation ==  McLeod, R. A.; Kowal, J.; Ringler, P. &amp; Stahlberg, H. Robust image alignment for cryogenic transmission electron microscopy. Journal of structural biology, 201...&quot;</title>
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		<updated>2017-05-11T13:44:28Z</updated>

		<summary type="html">&lt;p&gt;Created page with &amp;quot;== Citation ==  McLeod, R. A.; Kowal, J.; Ringler, P. &amp;amp; Stahlberg, H. Robust image alignment for cryogenic transmission electron microscopy. Journal of structural biology, 201...&amp;quot;&lt;/p&gt;
&lt;p&gt;&lt;b&gt;New page&lt;/b&gt;&lt;/p&gt;&lt;div&gt;== Citation ==&lt;br /&gt;
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McLeod, R. A.; Kowal, J.; Ringler, P. &amp;amp; Stahlberg, H. Robust image alignment for cryogenic transmission electron microscopy. Journal of structural biology, 2017, 197, 279-293&lt;br /&gt;
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== Abstract ==&lt;br /&gt;
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Cryo-electron microscopy recently experienced great improvements in structure resolution due to direct electron detectors with improved contrast and fast read-out leading to single electron counting. High frames rates enabled dose fractionation, where a long exposure is broken into a movie, permitting specimen drift to be registered and corrected. The typical approach for image registration, with high shot noise and low contrast, is multi-reference (MR) cross-correlation. Here we present the software package Zorro, which provides robust drift correction for dose fractionation by use of an intensity-normalized cross-correlation and logistic noise model to weight each cross-correlation in the MR model and filter each cross-correlation optimally. Frames are reliably registered by Zorro with low dose and defocus. Methods to evaluate performance are presented, by use of independently-evaluated even- and odd-frame stacks by trajectory comparison and Fourier ring correlation. Alignment of tiled sub-frames is also introduced, and demonstrated on an example dataset. Zorro source code is available at github.com/CINA/zorro.&lt;br /&gt;
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== Keywords ==&lt;br /&gt;
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== Links ==&lt;br /&gt;
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http://www.sciencedirect.com/science/article/pii/S1047847716302520&lt;br /&gt;
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== Related software ==&lt;br /&gt;
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== Related methods ==&lt;br /&gt;
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== Comments ==&lt;/div&gt;</summary>
		<author><name>CoSS</name></author>
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