Compressed sensing for electron cryotomography and high-resolution subtomogram averaging of biological specimens

Böhning, Jan; Bharat, Tanmay A. M.; Collins, Sean M.

doi:10.1016/j.str.2021.12.010

Cited by 13 publications

(10 citation statements)

References 102 publications

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“…These samples are not as susceptible as biological samples which allows using higher electron doses, obtaining projections in a higher angular range (−70 • to 78 • ) and a higher number of projections (each 2 • , 75 projections) with significantly less noise. Böhning et al [45] present a cryo-ET reconstruction method based on compressed sensing. They propose using 3D second-order TV for tomographic reconstruction, casting the reconstruction problem as a regularized optimization problem, solved using a primal-dual hybrid gradient method implemented in python using AS-TRA Toolbox [30], [31] for the data term.…”

Section: Related Workmentioning

confidence: 99%

GPU Accelerated 3D Tomographic Reconstruction and Visualization From Noisy Electron Microscopy Tilt-Series

Ramirez

Rautek

Bohak

et al. 2024

IEEE Trans. Visual. Comput. Graphics

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We present a novel framework for 3D tomographic reconstruction and visualization of tomograms from noisy electron microscopy tilt-series. Our technique takes as an input aligned tilt-series from cryogenic electron microscopy and creates denoised 3D tomograms using a proximal jointly-optimized approach that iteratively performs reconstruction and denoising, relieving the users of the need to select appropriate denoising algorithms in the pre-reconstruction or post-reconstruction steps. The whole process is accelerated by exploiting parallelism on modern GPUs, and the results can be visualized immediately after the reconstruction using volume rendering tools incorporated in the framework. We show that our technique can be used with multiple combinations of reconstruction algorithms and regularizers, thanks to the flexibility provided by proximal algorithms. Additionally, the reconstruction framework is open-source and can be easily extended with additional reconstruction and denoising methods. Furthermore, our approach enables visualization of reconstruction error throughout the iterative process within the reconstructed tomogram and on projection planes of the input tilt-series. We evaluate our approach in comparison with state-of-the-art approaches and additionally show how our error visualization can be used for reconstruction evaluation.

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Section: Related Workmentioning

confidence: 99%

GPU Accelerated 3D Tomographic Reconstruction and Visualization From Noisy Electron Microscopy Tilt-Series

Ramirez

Rautek

Bohak

et al. 2024

IEEE Trans. Visual. Comput. Graphics

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“…The copyright holder for this preprint this version posted July 12, 2022. ; https://doi.org/10. 1101/2022.07.11.499538 doi: bioRxiv preprint to the above algorithms, the compressed sensing (CS)-based method has also been proven to be effective in recovering the information in electron tomograms (Böhning et al, 2022;Deng et al, 2016;Leary et al, 2013). It introduces a priori assumptions in the tomogram, e.g., density positivity and solvent flatness, to constrain the structural features and allow the high-fidelity reconstruction of signals.…”

Section: Introductionmentioning

confidence: 99%

“…By applying CS on biological samples, ICON was found to be capable of reconstructing tomograms with high contrast and successfully restoring the missing information (Deng et al, 2016). A more recently proposed method, CS-TV 2 , which uses an advanced CS algorithm, could increase the contrast while retaining high-resolution information (Böhning et al, 2022). However, CS-based methods rely heavily on sufficient signal-to-noise ratio (SNR) and thus require high-contrast tomograms.…”

Section: Introductionmentioning

confidence: 99%

“…By using these algorithms, high contrast for visualizing 3D structures can often be achieved from the tomogram in which the low-resolution information is retained while high-resolution information is discarded. In addition to the above algorithms, the compressed sensing (CS)-based method has also been proven to be effective in recovering the information in electron tomograms (Böhning et al, 2022; Deng et al, 2016; Leary et al, 2013). It introduces a priori assumptions in the tomogram, e.g., density positivity and solvent flatness, to constrain the structural features and allow the high-fidelity reconstruction of signals.…”

Section: Introductionmentioning

confidence: 99%

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REST: A method for restoring signals and revealing individual macromolecule states in cryo-ET

Zhang

Liu

et al. 2022

Preprint

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Cryo-electron tomography (cryo-ET) is widely used to explore the 3D density of biomacromolecules. However, the heavy noise and missing wedge effect prevent directly visualizing and analyzing the 3D reconstructions. Here, we introduced REST, a deep learning strategy-based method to establish the relationship between low-quality and high-quality density and transfer this knowledge to restore signals in cryo-ET. Experimental results on purified ribosome and recombinant nucleosome datasets showed that REST had outstanding performance in denoising and compensating the missing wedge. The application in dynamic nucleosome structures suggests that REST has the capability to reveal individual macromolecules which present different conformations without subtomogram averaging. Moreover, REST could greatly improve the reliability of particle picking. These advantages enable REST to be a powerful tool for the straightforward interpretation of target macromolecules by visual inspection of the density and of a broad range of other applications in cryo-ET, such as segmentation, particle picking, and subtomogram averaging.

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“…To evaluate CS-TV 2 , we first evaluated whether the reconstruction algorithm retains information to the secondary structure level in hepatitis B (HBV) triangulation number (T) = 4 capsid particles [7]. Fig.…”

mentioning

confidence: 99%

Second-order Total Variation for Compressed Sensing Cryo-ET and Subtomogram Averaging

Böhning

Bharat

Collins

2022

Microscopy and Microanalysis

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Cryo-electron tomography (cryo-ET) is a key tool for imaging macromolecules in cellular environments. Together with subtomogram averaging (STA), cryo-ET can be used for structure determination. Weak visibility of many structures in cryo-ET as well as the volume of data required for STA, however, motivate the exploration of advanced image processing for cryo-ET. Compressed sensing is a mathematically rigorous signal processing approach to sampling with far fewer measurements than traditionally required, with significant applications in limited angle, undersampled electron tomography in the physical sciences [1,2]. Compressed sensing electron tomography (CS-ET) holds that for sample features that can be described as sparse, i.e., those requiring only a few coefficients to represent the object in a particular mathematical transform domain, a set of measurements can be devised to directly identify the tomographic reconstruction that also adheres to that sparsity, in contrast to post-processing approaches. This idea is related to image compression, where an image can be represented by only a small number of coefficients to reduce the storage requirements of the fully sampled image. CS-ET enables reducing data quantities while recovering high-fidelity reconstructions, or provides improved visibility and precision of image features for a given number of samples (measurements) [1][2][3].

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Compressed sensing for electron cryotomography and high-resolution subtomogram averaging of biological specimens

Cited by 13 publications

References 102 publications

GPU Accelerated 3D Tomographic Reconstruction and Visualization From Noisy Electron Microscopy Tilt-Series

GPU Accelerated 3D Tomographic Reconstruction and Visualization From Noisy Electron Microscopy Tilt-Series

REST: A method for restoring signals and revealing individual macromolecule states in cryo-ET

Second-order Total Variation for Compressed Sensing Cryo-ET and Subtomogram Averaging

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