Automated discrete electron tomography – Towards routine high-fidelity reconstruction of nanomaterials

Electron tomography is an essential imaging technique for the investigation of morphology and 3D structure of nanomaterials. This method, however, suffers from well-known missing wedge artifacts due to a restricted tilt range, which limits the objectiveness, repeatability and efficiency of quantitative structural analysis. Discrete tomography represents one of the promising reconstruction techniques for materials science, potentially capable of delivering higher fidelity reconstructions by exploiting the prior knowledge of the limited number of material compositions in a specimen. However, the application of discrete tomography to practical datasets remains a difficult task due to the underlying challenging mathematical problem. In practice, it is often hard to obtain consistent reconstructions from experimental datasets. In addition, numerous parameters need to be tuned manually, which can lead to bias and non-repeatability. In this paper, we present the application of a new iterative reconstruction technique, named TVR-DART, for discrete electron tomography. The technique is capable of consistently delivering reconstructions with significantly reduced missing wedge artifacts for a variety of challenging data and imaging conditions, and can automatically estimate its key parameters. We describe the principles of the technique and apply it to datasets from three different types of samples acquired under diverse imaging modes. By further reducing the available tilt range and number of projections, we show that the proposed technique can still produce consistent reconstructions with minimized missing wedge artifacts. This new development promises to provide the electron microscopy community with an easy-to-use and robust tool for high-fidelity 3D characterization of nanomaterials

Semantic reasoning with uncertain information from unreliable sources

Intelligent software agents may significantly benefit from semantic reasoning. However, existing semantic reasoners are based on Description Logics, which cannot handle vague, incomplete, and unreliable knowledge. In this paper, we propose SDL-LiteSDL-Lite which extends DL-LiteRDL-LiteR with subjective opinions to represent uncertainty in knowledge. We directly incorporate trust into the reasoning so that the inconsistencies in the knowledge can be resolved based on trust evidence analysis. Therefore, the proposed logic can handle uncertain information from unreliable sources. We demonstrate how SDL-LiteSDL-Lite can be used for semantic fusion of uncertain information from unreliable sources and show that SDL-LiteSDL-Lite reasoner can estimate the ground truth with a minimal error.U.S. Army Research Laboratory ; U.K. Ministry of Defence ; TÜBİTA