Generalized Autocorrelation Analysis for Multi-Target Detection

Shalit, Ye'Ela; Weber, Ran; Abas, Asaf; Kreymer, Shay; Bendory, Tamir

doi:10.1109/icassp43922.2022.9747133

Cited by 1 publication

(1 citation statement)

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“…Importantly, it is possible to reconstruct the target volume only up to a 3-D rotation, a 3-D translation, and a reflection. Similar mathematical models were thoroughly studied in previous works for one-and two-dimensional setups [42], [38], [43], [44], [45], [46], [37]. Fig.…”

Section: Measurement Formation Modelmentioning

confidence: 84%

An Approximate Expectation-Maximization for Two-Dimensional Multi-Target Detection

Kreymer

Singer

Bendory

2022

IEEE Signal Process. Lett.

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A single-particle cryo-electron microscopy (cryo-EM) measurement, called a micrograph, consists of multiple two-dimensional tomographic projections of a three-dimensional molecular structure at unknown locations, taken under unknown viewing directions. All existing cryo-EM algorithmic pipelines first locate and extract the projection images, and then reconstruct the structure from the extracted images. However, if the molecular structure is small, the signal-to-noise ratio (SNR) of the data is very low, and thus accurate detection of projection images within the micrograph is challenging. Consequently, all standard techniques fail in low-SNR regimes. To recover molecular structures from measurements of low SNR, and in particular small molecular structures, we devise a stochastic approximate expectation-maximization algorithm to estimate the three-dimensional structure directly from the micrograph, bypassing locating the projection images. We corroborate our computational scheme with numerical experiments, and present successful structure recoveries from simulated noisy measurements.

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