Abstract
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.
Highlights
IntroductionThe most commonly used reconstruction techniques, filtered back projection (FBP) and its cone beam counterpart the Feldkamp, Davis, and Kress algorithm (FDK) are unsuited for data with the previously mentioned deficiencies[24,25,26]
We present a method that is developed with the aim to reconstruct dynamic data from two-phase fluid flow experiments, but it can be used for any dynamic experiment, where it is possible to obtain a highquality static data set of the initial system before initiating the dynamic experiment
We have investigated the performance of our proposed approach by comparison to other SIRT based algorithms as well as the commonly used filtered-back projection (FBP) algorithm
Summary
The most commonly used reconstruction techniques, filtered back projection (FBP) and its cone beam counterpart the Feldkamp, Davis, and Kress algorithm (FDK) are unsuited for data with the previously mentioned deficiencies[24,25,26]. This is because a good reconstruction using this type of algorithm requires a rather large number of projections Nproj , preferably Nproj Npixπ/2 where Npix is the number of detector pixels[26]. A solution can be encouraged to have a noise-free appearance by penalising the norm of the derivative of the reconstruction, which is the case in e.g. total variation r egularisation[28]
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