CTF Challenge: Result summary

Marabini, Roberto; Carragher, Bridget; Chen, Shao-Xia; Chen, James; Cheng, Anchi; Downing, Kenneth H.; Frank, Joachim; Grassucci, Robert A.; Heymann, J. Bernard; Jiang, Wen; Jonić, Slavica; Liao, Hstau Y.; Ludtke, Steven J.; Patwari, Shail; Piotrowski, Angela L.; Quintana, Adrián; Sorzano, Carlos Óscar S.; Stahlberg, Henning; Vargas, Javier; Voss, Neil R.; Chiu, Wah; Carazo, José Marı́a

doi:10.1016/j.jsb.2015.04.003

Cited by 35 publications

(38 citation statements)

References 6 publications

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“…5B and C although with small spread. Considering the challenges in estimating astigmatism and the large discrepancies among the different CTF fitting methods found by the CTF Challenge (Marabini et al, 2015), such small spread shown in Fig. 6B and C can be considered excellent agreement.…”

Section: Resultsmentioning

confidence: 82%

Real-time detection and single-pass minimization of TEM objective lens astigmatism

Yan

Jiang

2017

Journal of Structural Biology

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Minimization of the astigmatism of the objective lens is a critical daily instrument alignment task essential for high resolution TEM imaging. Fast and sensitive detection of astigmatism is needed to provide real-time feedback and adjust the stigmators to efficiently reduce astigmatism. Currently the method used by many microscopists is to visually examine the roundness of a diffractogram (Thon rings) and iteratively adjust the stigmators to make the Thon rings circular. This subjective method is limited by poor sensitivity and potentially biased by the astigmatism of human eyes. In this study, an s2 power spectra based method, s2stigmator, was developed to allow fast and sensitive detection of the astigmatism in TEM live images. The “radar”-style display provides real-time feedback to guide the adjustment of the objective lens stigmators. Such unique capability allowed us to discover the mapping of the two stigmators to the astigmatism amplitude and angle, which led us to develop a single-pass tuning strategy capable of significantly quicker minimization of the objective lens astigmatism.

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Section: Resultsmentioning

confidence: 82%

Real-time detection and single-pass minimization of TEM objective lens astigmatism

Yan

Jiang

2017

Journal of Structural Biology

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“…Assuming that CTFFIND3 provides accurate estimates of defocus parameters under most circumstances, we also aimed to ensure that CTFFIND4's estimates closely matched those from CTFFIND3 under a wide range of circumstances, despite the significant algorithmic changes between the two versions. To this end, we ran both versions on all 9 sets of micrographs made available by Marabini et al (2015) as part of the CTF challenge, and computed the percentage difference in estimates of defocus parameters between the two versions. We found discrepancies in defocus estimates were usually below 1%, but that discrepancies in α ast were often large, in the tens of degrees (Table 3), presumably because under experimental conditions (high noise) and with low levels of astigmatism (∆∆f /∆f 1 on the order of 2%), this parameter is poorly determined by the Thon rings.…”

Section: Accuracymentioning

confidence: 99%

CTFFIND4: Fast and accurate defocus estimation from electron micrographs

Rohou

2015

Preprint

839

937

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CTFFIND is a widely-used program for the estimation of objective lens defocus parameters from transmission electron micrographs. Defocus parameters are estimated by fitting a model of the microscope's contrast transfer function (CTF) to an image's amplitude spectrum. Here we describe modifications to the algorithm which make it significantly faster and more suitable for use with images collected using modern technologies such as dose fractionation and phase plates. We show that this new version preserves the accuracy of the original algorithm while allowing for higher throughput. We also describe a measure of the quality of the fit as a function of spatial frequency and suggest this can be used to define the highest resolution at which CTF oscillations were successfully modeled.

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“…While the estimation of the defocus in the image is a common task, estimating the CTF contributions depending on their beam placement in the reconstruction is more challenging. Several methods have been suggested for estimating the CTF on an image (Marabini et al, 2015). Here we use FASTDEF (Vargas et al, 2013), which can determine multiple CTF parameters such as defocus and astigmatism at very low dose conditions.…”

Section: Ctf Determinationmentioning

confidence: 99%