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As the prices for high-end equipment continue to fall, cryo-EM is becoming a routine tool in the life sciences, and the field is expanding rapidly. Advances in technology are speeding up the rate at which new experimental structures are determined. However, a limitation on the rate at which these structures are solved is that the images produced by the most commonly used detector devices (CCDs, conventional CCDs) are not easily amenable to atomic-scale resolution structure determination using automated approaches that are based on low-dose single particle reconstruction algorithms like single particle electron cryomicroscopy (cryo-EM) and electron tomography. While progress in automated single particle reconstruction is being made, the resolution of images produced from these detectors is limited by the extensive image-to-image variation that is intrinsic to them. These variations arise from a combination of differences in the electron beam and on-chip variations in the performance of the detectors; this variation can be as high as 10% of the average intensity value. This makes the extraction of accurate three-dimensional (3D) density maps from low-resolution images more challenging, even for non-automated approaches. One solution that could allow higher-resolution structures to be obtained from these detectors is to provide a better quality of data for computational structure determination. This chapter summarizes recent progress in developing and implementing methods to improve the image quality from DDDs by correcting microscope motion and beam-induced variation. We then compare and contrast several of the most commonly used algorithms for correcting specimen movement in DDD movies. In the latter part of the chapter, we conclude with a discussion of future developments in algorithms for processing DDD movies that would extend the capabilities of cryo-EM even further.

The chapter is organized as follows. We begin in section 1 with a review of the basic concepts behind DDD-based movies. In section 2, we discuss the mathematical models that have been used to characterize DDD-based movies. We then compare the mathematical models on several fronts: code model, physical model, and algorithm. In section 3, we discuss the performance of each algorithm, and compare their computational complexity. We conclude with an outlook on future research directions in section 4.”

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