[en] We present a novel iterative reconstruction method applied to in situ x-ray synchrotron tomographic data of dendrite formation during the solidification of magnesium alloy. Frequently, fast dynamic imaging projection data are undersampled, noisy, of poor contrast and can contain various acquisition artifacts. Direct reconstruction methods are not suitable and iterative reconstruction techniques must be adapted to the existing data features. Normally, an accurate modelling of the objective function can guarantee a better reconstruction. In this work, we design a special cost function where the data fidelity term is based on the Group-Huber functional to minimize ring artifacts and the regularization term is a higher-order variational penalty. We show that the total variation penalty is unsuitable for some cases and higher-order regularization functionals can ensure a better fit to the expected properties of the data. Additionally, we highlight the importance of 3D regularization over 2D for the problematic data. The proposed method shows a promising performance dealing with angular undersampled noisy dynamic data with ring artifacts. (paper)

[en] In this study, we aim to reconstruct single-photon emission computed tomography images using anatomical information from magnetic resonance imaging as a priori knowledge about the activity distribution. The trade-off between anatomical and emission data is one of the main concerns for such studies. In this work, we propose an anatomically driven anisotropic diffusion filter (ADADF) as a penalized maximum likelihood expectation maximization optimization framework. The ADADF method has improved edge-preserving denoising characteristics compared to other smoothing penalty terms based on quadratic and non-quadratic functions. The proposed method has an important ability to retain information which is absent in the anatomy. To make our approach more stable to the noise-edge classification problem, robust statistics have been employed. Comparison of the ADADF method is performed with a successful anatomically driven technique, namely, the Bowsher prior (BP). Quantitative assessment using simulated and clinical neuroreceptor volumetric data show the advantage of the ADADF over the BP. For the modelled data, the overall image resolution, the contrast, the signal-to-noise ratio and the ability to preserve important features in the data are all improved by using the proposed method. For clinical data, the contrast in the region of interest is significantly improved using the ADADF compared to the BP, while successfully eliminating noise. (paper)

[en] Rapid developments in photon-counting and energy-discriminating detectors have the potential to provide an additional spectral dimension to conventional x-ray grayscale imaging. Reconstructed spectroscopic tomographic data can be used to distinguish individual materials by characteristic absorption peaks. The acquired energy-binned data, however, suffer from low signal-to-noise ratio, acquisition artifacts, and frequently angular undersampled conditions. New regularized iterative reconstruction methods have the potential to produce higher quality images and since energy channels are mutually correlated it can be advantageous to exploit this additional knowledge. In this paper, we propose a novel method which jointly reconstructs all energy channels while imposing a strong structural correlation. The core of the proposed algorithm is to employ a variational framework of parallel level sets to encourage joint smoothing directions. In particular, the method selects reference channels from which to propagate structure in an adaptive and stochastic way while preferring channels with a high data signal-to-noise ratio. The method is compared with current state-of-the-art multi-channel reconstruction techniques including channel-wise total variation and correlative total nuclear variation regularization. Realistic simulation experiments demonstrate the performance improvements achievable by using correlative regularization methods. (paper)

[en] There has been a rapid expansion of multi-modal imaging techniques in tomography. In biomedical imaging, patients are now regularly imaged using both single photon emission computed tomography (SPECT) and x-ray computed tomography (CT), or using both positron emission tomography and magnetic resonance imaging (MRI). In non-destructive testing of materials both neutron CT (NCT) and x-ray CT are widely applied to investigate the inner structure of material or track the dynamics of physical processes. The potential benefits from combining modalities has led to increased interest in iterative reconstruction algorithms that can utilize the data from more than one imaging mode simultaneously. We present a new regularization term in iterative reconstruction that enables information from one imaging modality to be used as a structural prior to improve resolution of the second modality. The regularization term is based on a modified anisotropic tensor diffusion filter, that has shape-adapted smoothing properties. By considering the underlying orientations of normal and tangential vector fields for two co-registered images, the diffusion flux is rotated and scaled adaptively to image features. The images can have different greyscale values and different spatial resolutions. The proposed approach is particularly good at isolating oriented features in images which are important for medical and materials science applications. By enhancing the edges it enables both easy identification and volume fraction measurements aiding segmentation algorithms used for quantification. The approach is tested on a standard denoising and deblurring image recovery problem, and then applied to 2D and 3D reconstruction problems; thereby highlighting the capabilities of the algorithm. Using synthetic data from SPECT co-registered with MRI, and real NCT data co-registered with x-ray CT, we show how the method can be used across a range of imaging modalities. (paper)

[en] Melt processing offers a cost effective method for producing metal matrix nanocomposite (MMNC) components; however, the influence of nanoparticles on the evolving microstructure during solidification is still not well understood. In this study, the effect of SiC nanoparticles on α-Mg dendrite evolution in a Mg-25Zn-7Al (wt.%) alloy was investigated through 4D (three dimensions plus time) synchrotron tomographic quantification of solidification experiments conducted at different cooling rates with and without nanoparticles. Key features of the solidifying primary α-Mg dendritic grains were quantified, including grain morphology, size distribution, and dendrite tip velocity. To obtain the high-contrast tomography dataset necessary for structure quantification, a new image reconstruction and processing methodology was implemented. The results reveal that the addition of nanoparticles increases grain nucleation whilst restricting dendritic growth and altering the dendritic grain growth morphology. Using LGK model calculations, it is shown that these changes in solidification microstructure occur as a result of nanoparticle-induced restriction in Zn's effective diffusivity ahead of the dendrite tips, reducing tip velocity. The results both suggest the key phenomena required to be simulated when numerically modelling solidifying Mg-based MMNC and provide the data required to validate those models.

[en] The scale of solidification microstructures directly impacts micro-segregation, grain size, and other factors which control strength. Using in situ high speed synchrotron X-ray tomography we have directly quantified the evolution of dendritic microstructure length scales during the coarsening of Mg-Zn hcp alloys in three spatial dimensions plus time (4D). The influence of two key parameters, solute composition and cooling rate, was investigated. Key responses, including specific surface area, dendrite mean and Gauss curvatures, were quantified as a function of time and compared to existing analytic models. The 3D observations suggest that the coarsening of these hcp dendrites is dominated by both the re-melting of small branches and the coalescence of the neighbouring branches. The results show that solute concentration has a great impact on the resulting microstructural morphologies, leading to both dendritic and seaweed-type grains. It was found that the specific solid/liquid surface and its evolution can be reasonably scaled to time with a relationship of ∼ t"−"1"/"3. This term is path independent for the Mg-25 wt%Zn; that is, the initial cooling rate during solidification does not greatly influence the coarsening rate. However, path independence was not observed for the Mg-38 wt%Zn samples because of the seaweed microstructure. This led to large differences in the specific surface area (S_s) and its evolution both between the two alloy compositions and within the Mg-38 wt%Zn for the different cooling rates. These findings allow for microstructure models to be informed and validated to improve predictions of solidification microstructural length scales and hence strength.