4.4 Article

Volume imaging by tracking sparse topological features in electron micrograph tilt series

期刊

ULTRAMICROSCOPY
卷 236, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.ultramic.2022.113475

关键词

Phase contrast; Tomography; Transmission electron microscopy; Topology; Dynamical diffraction

资金

  1. Australian Research Council (ARC) [LE0454166, LE170100118]
  2. Japan Society for the Promotion of Science (JSPS)/Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan KAKENHI [JP18H05479, JP20H02426]
  3. Japan Science and Technology Agency (JST) CREST [JPMJCR18J4]
  4. ARC [150104483]
  5. Australian Research Council [LE170100118] Funding Source: Australian Research Council

向作者/读者索取更多资源

Researchers utilize a geometric approach to track robust topological features under imaging conditions that violate the Radon projection assumption, enabling 3D reconstructions with emphasis on phase contrast. By invoking a sparsity assumption and segmenting interest points from the outset, noise reduction is achieved.
The sensitive coherent interference of electron waves arising from a specimen is useful for revealing subtle structural information in electron micrographs, which can be important for minimising dose and for rapid imaging. In general, dynamical diffraction is expected due to the useful strong interactions of electrons with matter, which can create phase contrast that violates the requisite Radon projection assumption for tomography. It is for these reasons that incoherent imaging modalities such as high angle annular dark field have been favoured to date in electron tomography of crystalline specimens, to access a monotonic relationship between specimen thickness and micrograph intensity. Here we use a geometric approach to track topological features that are robust to perturbation of the imaging conditions, to enable 3 D reconstructions from electron microscope tilt series under imaging conditions that violate the Radon projection assumption, with an emphasis on phase contrast. Invoking a sparsity assumption, we demonstrate that topological features can be reliably tracked in 3 D using a differential geometric form of stereoscopy, to circumvent departures from the projection approximation and reduce noise by effecting segmentation of interest points from the outset. We demonstrate this approach on a variety of different specimen and data types, from polyhedral nanoparticles, to steel dislocation networks, cryo-EM cellular structures and 3 D diffuse diffraction of a relaxor ferroelectric.

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