Electron Tomography

The three-dimensional (3D) atomic structure of nanomaterials, including strain, is crucial to understand their properties. Here, we investigate lattice strain in Au nanodecahedra using electron tomography. Although different electron tomography techniques enabled 3D characterizations of nanostructures at the atomic level, a reliable determination of lattice strain is not
straightforward. We therefore propose a novel model-based approach from which atomic coordinates are measured. Our findings demonstrate the importance of investigating lattice strain in 3D.

Super resolution reconstruction for quantitative imaging

We developed a super-resolution reconstruction methodology for diffusion and relaxometry MRI. It allows to improve the trade-off between acquisition time, spatial resolution and SNR. From a set of low resolution multi-slice images, each with a different (diffusion or relaxometry) contrast, an image is reconstructed with a much higher spatial resolution compared to a 3D image that is directly acquired in the same time frame.

Inline Computed Tomography

X-ray Computed Tomography (CT) has been applied in industry for quality and defect control in food products. However, conventional CT systems are neither cost effective nor flexible, making the deployment of such technology unfeasible for many industrial environments. We propose a simple and cost effective X-ray imaging setup that comprises a linear translation of the object in a conveyor belt with a fixed X-ray source and detector, with which a small number of X-ray projections can be acquired within a limited angular range.

Real-time tomographical reconstructions using Neural Networks

Tomographical algorithms can be separated into two classes: analytical “one-step” methods and iterative reconstruction algorithms. Analytical methods are fast, but require projection data of high quality and are impossible to adapt to use prior knowledge about the reconstructed object. Iterative algorithms have less strict requirements for the used data and are more flexible, but their computation time impedes real-time reconstructions. By reformulating the reconstruction problem as a classification problem, a third option becomes available: machine learning.

Tomographic segmentation

Many segmentation techniques exist in the literature. Well known is global thresholding with automated threshold selection based on the image histogram, e.g. Otsu's method. Other commonly used methods are region growing, watershed segmentation, active contours, etc. In the setting of CT, these techniques base themselves exclusively on the tomographic reconstruction or the tomogram. In practice, however, these tomograms will not be completely accurate because of reconstruction error and artefacts (e.g.

GPU-based iterative tomography

Even though iterative reconstruction algorithms give very accurate results, their long computation time inhibits practical applications. The most computationally intensive steps of the algorithms used by the ASTRA research group are very well parallelizable. Modern GPU cores consist of many processing units and are optimized for operations also required during tomographic reconstructions. With the release of NVIDIA CUDA, GPUs became fully programmable.

Subscribe to RSS - Reconstruction