ArticleInverse problems2019
Multi-target detection with application to cryo-electron microscopy.
Article in Inverse problems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
12 citing papers in PubMed.
- EXPECTATION-MAXIMIZATION FOR STRUCTURE DETERMINATION DIRECTLY FROM CRYO-EM MICROGRAPHS.Inverse problems and imaging (Springfield, Mo.) · 2027Article
- Methods for Cryo-EM Single Particle Reconstruction of Macromolecules Having Continuous Heterogeneity.Journal of molecular biology · 2023Review
- MULTI-TARGET DETECTION WITH ROTATIONS.Inverse problems and imaging (Springfield, Mo.) · 2023Article
- Toward Single Particle Reconstruction without Particle Picking: Breaking the Detection Limit.SIAM journal on imaging sciences · 2023Article
- Random conical tilt reconstruction without particle picking in cryo-electron microscopy.Acta crystallographica. Section A, Foundations and advances · 2022Article
- Super-resolution multi-reference alignment.Information and inference : a journal of the IMA · 2022Article
- An approximate expectation-maximization for two-dimensional multi-target detection.IEEE signal processing letters · 2022Article
- Wavelet invariants for statistically robust multi-reference alignment.Information and inference : a journal of the IMA · 2021Article
- Wilson statistics: derivation, generalization and applications to electron cryomicroscopy.Acta crystallographica. Section A, Foundations and advances · 2021Article
- Single-particle cryo-electron microscopy: Mathematical theory, computational challenges, and opportunities.IEEE signal processing magazine · 2020Article
- Multi-target Detection with an Arbitrary Spacing Distribution.IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society · 2020Article
- Heterogeneous multireference alignment for images with application to 2-D classification in single particle reconstruction.IEEE transactions on image processing : a publication of the IEEE Signal Processing Society · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
We consider the multi-target detection problem of recovering a set of signals that appear multiple times at unknown locations in a noisy measurement. In the low noise regime, one can estimate the signals by first detecting occurrences, then clustering and averaging them. In the high noise regime, however, neither detection nor clustering can be performed reliably, so that strategies along these lines are destined to fail. Notwithstanding, using autocorrelation analysis, we show that the impossibility to detect and cluster signal occurrences in the presence of high noise does not necessarily preclude signal estimation. Specifically, to estimate the signals, we derive simple relations between the autocorrelations of the observation and those of the signals. These autocorrelations can be estimated accurately at any noise level given a sufficiently long measurement. To recover the signals from the observed autocorrelations, we solve a set of polynomial equations through nonlinear least-squares. We provide analysis regarding well-posedness of the task, and demonstrate numerically the effectiveness of the method in a variety of settings. The main goal of this work is to provide theoretical and numerical support for a recently proposed framework to image 3D structures of biological macromolecules using cryo-electron microscopy in extreme noise levels.
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Registered trials
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