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

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.