Model-based iterative reconstruction using higher-order regularization of dynamic synchrotron data

Abstract

Abstract. We present a novel iterative reconstruction method applied to in situ Xray 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.

Towards routine 3D characterization of intact mesoscale samples by multi-scale and multimodal scanning X-ray tomography

“…It is the fastest method, exploiting fast Fourier transforms. However, FBP suffers from a lack of robustness when the measurements are sparse, of low contrast, or noisy 55 , 56 , which is often the case in XRF tomography. MLEM is an iterative algorithm that explicitly accounts for the noise affecting the data and imposes positivity on the estimated pixels.…”

In Situ Tomographic Observation of Dendritic Growth in Mg/Al Matrix Composites

“…The data of magnesium alloys were preprocessed following the method described in ref. [18] to minimize ring artefacts and improve phase contrast, while filtered back projection reconstruction was used for the Al-10Cu datasets. After data reconstruction and pre-processing, the primary dendrites were segmented using a global threshold value.…”

Versatile regularisation toolkit for iterative image reconstruction with proximal splitting algorithms

“…Additionally, we reconstructed dynamic alloy solidification data also obtained at DLS. 15 We use the ADMM algorithm (see Alg. 4) with the SB-TV 16 regulariser.…”