Speaker
Dr
Daniil Kazantsev
(The University of Manchester)
Description
Technological advances in tomographic acquisition speeds allow multiple 3D data to be acquired in a short period of time, meaning that the structural changes of an object can be interpreted as a function of time. Dynamic experiments are crucial since they can shed a light on structural changes under realistic conditions. The critical limitation of dynamic imaging is a number of projections required per scan. While reducing the angular density of projections increases scans frequency (with less incorporated motion per time-frame), reconstruction from undersampled data poses major difficulties related to mathematical ill-posedness of inversion. Iterative image reconstruction (IIR) methods are much better adapted to deal with ill-posed inversion from undersampled measurements than direct methods as they use error-correcting refinements in iterations and allow the use of a priori information.
In order to demonstrate the possibilities of IIR for time-lapse data we will show a challenging case of reconstructing undersampled low-contrast dynamic data of ice-cream. The temperature of a sample changes during the experiment which leads to various structural deformations and interesting physical phenomena to occur. Although IIR reconstruction significantly improves contrast and SNR of images, segmentation of three-phase material (air, ice-crystals, ice-matrix) remains a challenge. We will present a novel post-processing approach to tackle the problem of varying intensities within one-phase region which impedes successful segmentation.
Primary author
Dr
Daniil Kazantsev
(The University of Manchester)
Co-authors
Dr
Anders Kaestner
(Paul Scherrer Institut, NIAG)
Dr
Enyu Guo
(The University of Manchester)
Prof.
Peter Lee
(The University of Manchester)
