Region-Based Iterative Reconstruction of Structurally Changing Objects in CT

Ab initio nonrigid X-ray nanotomography

Reaching the full potential of X-ray nanotomography, in particular for biological samples, is limited by many factors, of which one of the most serious is radiation damage. Although sample deformation caused by radiation damage can be partly mitigated by cryogenic protection, it is still present in these conditions and, as we exemplify here using a specimen extracted from scales of the Cyphochilus beetle, it will pose a limit to the achievable imaging resolution. We demonstrate a generalized tomographic model, which optimally follows the sample morphological changes and attempts to recover the original sample structure close to the ideal, damage-free reconstruction. Whereas our demonstration was performed using ptychographic X-ray tomography, the method can be adopted for any tomographic imaging modality. Our application demonstrates improved reconstruction quality of radiation-sensitive samples, which will be of increasing relevance with the higher brightness of 4th generation synchrotron sources.

Radiation induced sample deformation can be a limiting factor for X-ray imaging resolution at the nanoscale. The authors report a tomographic model that estimates and accounts for morphological changes during data acquisition and enables reconstruction of a high-resolution image ab initio.

MoVIT: a tomographic reconstruction framework for 4D-CT

4D computed tomography (4D-CT) aims to visualise the temporal dynamics of a 3D sample with a sufficiently high temporal and spatial resolution. Successive time frames are typically obtained by sequential scanning, followed by independent reconstruction of each 3D dataset. Such an approach requires a large number of projections for each scan to obtain images with sufficient quality (in terms of artefacts and SNR). Hence, there is a clear trade-off between the rotation speed of the gantry (i.e. time resolution) and the quality of the reconstructed images. In this paper, the MotionVector-based Iterative Technique (MoVIT) is introduced which reconstructs a particular time frame by including the projections of neighbouring time frames as well. It is shown that such a strategy improves the trade-off between the rotation speed and the SNR. The framework is tested on both numerical simulations and on 4D X-ray CT datasets of polyurethane foam under compression. Results show that reconstructions obtained with MoVIT have a significantly higher SNR compared to the SNR of conventional 4D reconstructions.

An Iterative CT Reconstruction Algorithm for Fast Fluid Flow Imaging

The study of fluid flow through solid matter by computed tomography (CT) imaging has many applications, ranging from petroleum and aquifer engineering to biomedical, manufacturing, and environmental research. To avoid motion artifacts, current experiments are often limited to slow fluid flow dynamics. This severely limits the applicability of the technique. In this paper, a new iterative CT reconstruction algorithm for improved a temporal/spatial resolution in the imaging of fluid flow through solid matter is introduced. The proposed algorithm exploits prior knowledge in two ways. First, the time-varying object is assumed to consist of stationary (the solid matter) and dynamic regions (the fluid flow). Second, the attenuation curve of a particular voxel in the dynamic region is modeled by a piecewise constant function over time, which is in accordance with the actual advancing fluid/air boundary. Quantitative and qualitative results on different simulation experiments and a real neutron tomography data set show that, in comparison with the state-of-the-art algorithms, the proposed algorithm allows reconstruction from substantially fewer projections per rotation without image quality loss. Therefore, the temporal resolution can be substantially increased, and thus fluid flow experiments with faster dynamics can be performed.

Employing temporal self-similarity across the entire time domain in computed tomography reconstruction

There are many cases where one needs to limit the X-ray dose, or the number of projections, or both, for high frame rate (fast) imaging. Normally, it improves temporal resolution but reduces the spatial resolution of the reconstructed data. Fortunately, the redundancy of information in the temporal domain can be employed to improve spatial resolution. In this paper, we propose a novel regularizer for iterative reconstruction of time-lapse computed tomography. The non-local penalty term is driven by the available prior information and employs all available temporal data to improve the spatial resolution of each individual time frame. A high-resolution prior image from the same or a different imaging modality is used to enhance edges which remain stationary throughout the acquisition time while dynamic features tend to be regularized spatially. Effective computational performance together with robust improvement in spatial and temporal resolution makes the proposed method a competitive tool to state-of-the-art techniques.