Formation and reduction of streak artefacts in electron tomography

Automatic system for electron tomography data collection in the ultra-high voltage electron microscope

In this study, we report an automatic system for collection of tilt series for electron tomography based on the ultra-HVEM in Osaka University. By remotely controlling the microscope and reading the observation image, the system can track the field of view and do focus in each tilt angle. The automatic tracking is carried out with an image matching technique based on normalized correlation coefficient. Auto focus is realized by the optimization of image sharpness. A toolkit that can expand the field of view with technique of image stitching is also developed. The system can automatically collect the tilt series with much smaller time consumption.

The benefit of thresholding carbon layers in electron tomographic tilt series by intensity downshifting

When performing electron tomography, tilt series of images are often acquired from samples that contain unwanted carbonaceous material, such as an embedding resin, a thin carbon support film or hydrocarbon contamination. The presence of such layers can introduce artefacts in reconstructions, obscuring features of interest. Here, we illustrate the benefit of preprocessing a high-angle annular dark-field tomographic tilt series by thresholding unwanted low-density materials using a simple intensity downshifting procedure. The resulting tomograms have fewer artefacts and segmentation can be performed more accurately. We present two representative examples taken from studies of catalyst nanoparticles and amyloid plaque core material from the human brain.

Removing the effects of the "dark matter" in tomography

Electron tomography (ET) using different imaging modes has been progressively consolidating its position as a key tool in materials science. The fidelity of a tomographic reconstruction, or tomogram, is affected by several experimental factors. Most often, an unrealistic cloud of intensity that does not correspond to a real material phase of the specimen ("dark matter") blurs the tomograms and enhances artefacts arising from the missing wedge (MW). Here we show that by simple preprocessing of the background level of any tomographic tilt series, it is possible to minimise the negative effects of that "dark matter". Iterative reconstruction algorithms converge better, leading to tomograms with fewer streaking artefacts from the MW, more contrast, and increased accuracy. The conclusions are valid irrespective of the imaging mode used, and the methodology improves the segmentation and visualisation of tomograms of both crystalline and amorphous materials. We show examples of HAADF STEM and BF TEM tomography.

BSIRT: a block-iterative SIRT parallel algorithm using curvilinear projection model

Large-field high-resolution electron tomography enables visualizing detailed mechanisms under global structure. As field enlarges, the distortions of reconstruction and processing time become more critical. Using the curvilinear projection model can improve the quality of large-field ET reconstruction, but its computational complexity further exacerbates the processing time. Moreover, there is no parallel strategy on GPU for iterative reconstruction method with curvilinear projection. Here we propose a new Block-iterative SIRT parallel algorithm with the curvilinear projection model (BSIRT) for large-field ET reconstruction, to improve the quality of reconstruction and accelerate the reconstruction process. We also develop some key techniques, including block-iterative method with the curvilinear projection, a scope-based data decomposition method and a page-based data transfer scheme to implement the parallelization of BSIRT on GPU platform. Experimental results show that BSIRT can improve the reconstruction quality as well as the speed of the reconstruction process.

Local regularization of tilt projections reduces artifacts in electron tomography

Electron tomography produces very high resolution 3D image volumes useful for investigating the structure and function of cellular components. Unfortunately, unavoidable discontinuities and physical constraints in the acquisition geometry lead to a range of artifacts that can affect the reconstructed image. In particular, highly electron dense regions, such as gold nanoparticles, can hide proximal biological structures and degrade the overall quality of the reconstructed tomograms. In this work we introduce a pre-reconstruction non-conservative non-linear isotropic diffusion (NID) filter that automatically identifies and reduces local irregularities in the tilt projections. We illustrate the improvement in quality obtained using this approach for reconstructed tomograms generated from samples of malaria parasite-infected red blood cells. A quantitative and qualitative evaluation for our approach on both simulated and real data is provided.