Dynamic tomography has become an important technique to study fluid flow processes in porous media. The use of laboratory X-ray tomography instruments is, however, limited by their low X-ray brilliance. The prolonged exposure times, in turn, greatly limit temporal resolution. We have developed a tomographic reconstruction algorithm that maintains high image quality, despite reducing the exposure time and the number of projections significantly. Our approach, based on the Simultaneous Iterative Reconstruction Technique, mitigates the problem of few and noisy exposures by utilising a high-quality scan of the system before the dynamic process is started. We use the high-quality scan to initialise the first time step of the dynamic reconstruction. We further constrain regions of the dynamic reconstruction with a segmentation of the static system. We test the performance of the algorithm by reconstructing the dynamics of fluid separation in a multiphase system. The algorithm is compared quantitatively and qualitatively with several other reconstruction algorithms and we show that it can maintain high image quality using only a fraction of the normally required number of projections and with a substantially larger noise level. By robustly allowing fewer projections and shorter exposure, our algorithm enables the study of faster flow processes using laboratory tomography instrumentation but it can also be used to improve the reconstruction quality of dynamic synchrotron experiments.
Bibliographical noteFunding Information:
We would like to thank the Danish Hydrocarbon Research and Technology Centre for financial support and the Danish Agency for Science, Technology, and Innovation for funding the instrument center DanScatt. We thank the Japan Synchrotron Radiation Research Institute for the allotment of beam time on beamline BL47XU of SPring-8 (Proposal 2016A1459) and N. Bovet and D. Müter for assisting at the experiment. We are grateful to P. C. Hansen for discussions related to the NCP stopping rule. We also thank the Center for Quantification of Imaging Data from MAX IV (QIM) funded by the Capital Region of Denmark.
© 2021, The Author(s).