ArticleScientific reports2016
Compressed Sensing Electron Tomography for Determining Biological Structure.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Lamin A/C directs nucleosome-scale chromatin remodeling to define early lineage segregation in mammals.bioRxiv : the preprint server for biology · 2026Article
- Charting the molecular landscape of the cell.Structure (London, England : 1993) · 2023Review
- Automatic Differentiation for Inverse Problems in X-ray Imaging and Microscopy.Life (Basel, Switzerland) · 2023Article
- Compressed sensing for electron cryotomography and high-resolution subtomogram averaging of biological specimens.Structure (London, England : 1993) · 2022Article
- Novel nonlinear reconstruction method with grey-level quantisation units for electron tomography.Scientific reports · 2020Article
- Tomographic Collection of Block-Based Sparse STEM Images: Practical Implementation and Impact on the Quality of the 3D Reconstructed Volume.Materials (Basel, Switzerland) · 2019Article
- MBIR: A cryo-ET 3D reconstruction method that effectively minimizes missing wedge artifacts and restores missing information.Journal of structural biology · 2019Article
- Comparison of 3D cellular imaging techniques based on scanned electron probes: Serial block face SEM vs. Axial bright-field STEM tomography.Journal of structural biology · 2018Article
- A Survey of the Use of Iterative Reconstruction Algorithms in Electron Microscopy.BioMed research international · 2017Review
- Biological application of Compressed Sensing Tomography in the Scanning Electron Microscope.Scientific reports · 2016Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
There has been growing interest in applying compressed sensing (CS) theory and practice to reconstruct 3D volumes at the nanoscale from electron tomography datasets of inorganic materials, based on known sparsity in the structure of interest. Here we explore the application of CS for visualizing the 3D structure of biological specimens from tomographic tilt series acquired in the scanning transmission electron microscope (STEM). CS-ET reconstructions match or outperform commonly used alternative methods in full and undersampled tomogram recovery, but with less significant performance gains than observed for the imaging of inorganic materials. We propose that this disparity stems from the increased structural complexity of biological systems, as supported by theoretical CS sampling considerations and numerical results in simulated phantom datasets. A detailed analysis of the efficacy of CS-ET for undersampled recovery is therefore complicated by the structure of the object being imaged. The numerical nonlinear decoding process of CS shares strong connections with popular regularized least-squares methods, and the use of such numerical recovery techniques for mitigating artifacts and denoising in reconstructions of fully sampled datasets remains advantageous. This article provides a link to the software that has been developed for CS-ET reconstruction of electron tomographic data sets.
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