Reconstructing porous structures from FIB-SEM image data: Optimizing sampling scheme and image processing

Video frame interpolation neural network for 3D tomography across different length scales

Three-dimensional (3D) tomography is a powerful investigative tool for many scientific domains, going from materials science, to engineering, to medicine. Many factors may limit the 3D resolution, often spatially anisotropic, compromising the precision of the information retrievable. A neural network, designed for video-frame interpolation, is employed to enhance tomographic images, achieving cubic-voxel resolution. The method is applied to distinct domains: the investigation of the morphology of printed graphene nanosheets networks, obtained via focused ion beam-scanning electron microscope (FIB-SEM), magnetic resonance imaging of the human brain, and X-ray computed tomography scans of the abdomen. The accuracy of the 3D tomographic maps can be quantified through computer-vision metrics, but most importantly with the precision on the physical quantities retrievable from the reconstructions, in the case of FIB-SEM the porosity, tortuosity, and effective diffusivity. This work showcases a versatile image-augmentation strategy for optimizing 3D tomography acquisition conditions, while preserving the information content.

Image quality evaluation for FIB-SEM images

Focused ion beam scanning electron microscopy (FIB-SEM) tomography is a serial sectioning technique where an FIB mills off slices from the material sample that is being analysed. After every slicing, an SEM image is taken showing the newly exposed layer of the sample. By combining all slices in a stack, a 3D image of the material is generated. However, specific artefacts caused by the imaging technique distort the images, hampering the morphological analysis of the structure. Typical quality problems in microscopy imaging are noise and lack of contrast or focus. Moreover, specific artefacts are caused by the FIB milling, namely, curtaining and charging artefacts. We propose quality indices for the evaluation of the quality of FIB-SEM data sets. The indices are validated on real and experimental data of different structures and materials.

Quantitative analysis of printed nanostructured networks using high-resolution 3D FIB-SEM nanotomography

Networks of solution-processed nanomaterials are becoming increasingly important across applications in electronics, sensing and energy storage/generation. Although the physical properties of these devices are often completely dominated by network morphology, the network structure itself remains difficult to interrogate. Here, we utilise focused ion beam - scanning electron microscopy nanotomography (FIB-SEM-NT) to quantitatively characterise the morphology of printed nanostructured networks and their devices using nanometre-resolution 3D images. The influence of nanosheet/nanowire size on network structure in printed films of graphene, WS2 and silver nanosheets (AgNSs), as well as networks of silver nanowires (AgNWs), is investigated. We present a comprehensive toolkit to extract morphological characteristics including network porosity, tortuosity, specific surface area, pore dimensions and nanosheet orientation, which we link to network resistivity. By extending this technique to interrogate the structure and interfaces within printed vertical heterostacks, we demonstrate the potential of this technique for device characterisation and optimisation.

Optical flow driven interpolation for isotropic FIB-SEM reconstructions

BACKGROUND AND OBJECTIVE

Focused Ion Beam - Scanning Electron Microscopy (FIB-SEM) allows three-dimensional ultrastructural analysis of cells and tissues at the nanoscale. The technique iteratively removes a section of the sample with a FIB and takes an SEM image from the exposed surface. The section thickness is usually higher than the image pixel size to reduce acquisition time, thus resulting in anisotropic resolution. In this work, we explore novel interpolation methods along the sectioning direction to produce isotropic resolution and facilitate proper interpretation of the FIB-SEM 3D volumes.

METHODS

Classical interpolation methods are usually applied in this context under the assumption that the changes through successive images are relatively smooth. However, the actual 3D arrangement of the structures in the sample may cause significant changes in the biological features between consecutive images of the FIB-SEM stacks. We have developed a novel interpolation strategy that accounts for this variation by using the Optical Flow (OF) to estimate it. As an intermediate stage, OF-compensated images are produced by aligning the spatial regions of the biological structures. Interpolated images are then generated from these OF-compensated images. The final isotropic stack is assembled by interleaving the interpolated images with the original images of the anisotropic stack.

RESULTS

OF-driven and classical interpolation methods were compared using an objective assessment based on Pearson Correlation Coefficient (PCC) and a qualitative evaluation based on visual results, using public datasets and representative anisotropy conditions. The objective assessment demonstrated that the OF-driven interpolation always yields higher PCC values, with interpolated images closer to the ground truth. The qualitative evaluation corroborated those results and confirmed that classical interpolation may blur areas with substantial changes between consecutive images whereas OF-driven interpolation provides sharpness.

CONCLUSIONS

We have developed an OF-driven interpolation approach to generating FIB-SEM stacks with isotropic resolution from experimental anisotropic data. It adapts to the rapid variation of the biological structures observed through the images of the FIB-SEM stack. Our approach outperforms classical interpolation and manages to produce sharp interpolated views in cases where there are significant changes between consecutive experimental images.