New software protocols for enabling laboratory based temporal CT.

Temporal micro-computed tomography (CT) allows the non-destructive quantification of processes that are evolving over time in 3D. Despite the increasing popularity of temporal CT, the practical implementation and optimisation can be difficult. Here, we present new software protocols that enable temporal CT using commercial laboratory CT systems. The first protocol drastically reduces the need for periodic intervention when making time-lapse experiments, allowing a large number of tomograms to be collected automatically. The automated scanning at regular intervals needed for uninterrupted time-lapse CT is demonstrated by analysing the germination of a mung bean (vigna radiata), whilst the synchronisation with an in situ rig required for interrupted time-lapse CT is highlighted using a shear cell to observe granular segregation. The second protocol uses golden-ratio angular sampling with an iterative reconstruction scheme and allows the number of projections in a reconstruction to be changed as sample evolution occurs. This overcomes the limitation of the need to know a priori what the best time window for each scan is. The protocol is evaluated by studying barite precipitation within a porous column, allowing a comparison of spatial and temporal resolution of reconstructions with different numbers of projections. Both of the protocols presented here have great potential for wider application, including, but not limited to, in situ mechanical testing, following battery degradation and chemical reactions.

[1]  Philip J. Withers,et al.  Metamorphosis revealed: time-lapse three-dimensional imaging inside a living chrysalis , 2013, Journal of The Royal Society Interface.

[2]  R. Ritchie,et al.  Real-time Quantitative Imaging of Failure Events in Materials under Load at Temperatures above 1,600 , 2012 .

[4]  A. Kingston,et al.  Dynamic tomography with a priori information. , 2011, Applied optics.

[5]  E. Maire,et al.  In-situ X-ray tomographic monitoring of gypsum plaster setting , 2016 .

[6]  Anton du Plessis,et al.  Application of microCT to the non-destructive testing of an additive manufactured titanium component , 2015 .

[7]  Clinton S. Willson,et al.  Imaging tissue structures: assessment of absorption and phase-contrast x-ray tomography imaging at 2nd and 3rd generation synchrotrons , 2006, SPIE Optics + Photonics.

[8]  F. Zimmermann,et al.  High resolution gamma ray tomography scanner for flow measurement and non-destructive testing applications. , 2007, The Review of scientific instruments.

[9]  Nigel P. Brandon,et al.  Lithiation‐Induced Dilation Mapping in a Lithium‐Ion Battery Electrode by 3D X‐Ray Microscopy and Digital Volume Correlation , 2014 .

[10]  Ryuta Mizutani,et al.  X-ray microtomography in biology. , 2012, Micron.

[11]  P. Withers,et al.  Characterization of the three‐dimensional structure of a metallic foam during compressive deformation , 2006, Journal of microscopy.

[12]  P. Withers,et al.  X-ray computed tomography of polymer composites , 2017 .

[13]  Søren Holdt Jensen,et al.  Implementation of an optimal first-order method for strongly convex total variation regularization , 2011, ArXiv.

[14]  M. Nordborg,et al.  A Coastal Cline in Sodium Accumulation in Arabidopsis thaliana Is Driven by Natural Variation of the Sodium Transporter AtHKT1;1 , 2010, PLoS genetics.

[15]  Paul J. Williams,et al.  X-ray micro-computed tomography (μCT) for non-destructive characterisation of food microstructure , 2016 .

[16]  Stéphane Roux,et al.  Self-calibration for lab-μCT using space-time regularized projection-based DVC and model reduction , 2018 .

[17]  V. Cnudde,et al.  A pore-scale study of fracture dynamics in rock using X-ray micro-CT under ambient freeze-thaw cycling. , 2015, Environmental science & technology.

[18]  S. Mooney,et al.  Quantification of differences in germination behaviour of pelleted and coated sugar beet seeds using x-ray computed tomography (x-ray CT) , 2017 .

[19]  C. Ancey,et al.  Underlying Asymmetry within Particle Size Segregation. , 2015, Physical review letters.

[20]  A. Thornton,et al.  Asymmetric breaking size-segregation waves in dense granular free-surface flows , 2016, Journal of Fluid Mechanics.

[21]  S. González-Morales,et al.  Engineering food crops to grow in harsh environments , 2015, F1000Research.

[22]  Stefan Sawall,et al.  Iterative 4D cardiac micro-CT image reconstruction using an adaptive spatio-temporal sparsity prior , 2012, Physics in medicine and biology.

[23]  M. De Graef,et al.  The Three-Dimensional Morphology of Growing Dendrites , 2015, Scientific Reports.

[24]  Veerle Cnudde,et al.  High-resolution X-ray computed tomography in geosciences: A review of the current technology and applications , 2013 .

[25]  Andreas Mortensen,et al.  20 Hz X-ray tomography during an in situ tensile test , 2016, International Journal of Fracture.

[26]  Charles A. Bouman,et al.  TIMBIR: A Method for Time-Space Reconstruction From Interlaced Views , 2015, IEEE Transactions on Computational Imaging.

[27]  E. Maire,et al.  In Situ Experiments with X ray Tomography: an Attractive Tool for Experimental Mechanics , 2010 .

[28]  Jan Sijbers,et al.  Fast and flexible X-ray tomography using the ASTRA toolbox. , 2016, Optics express.

[29]  Veerle Cnudde,et al.  Fast laboratory-based micro-computed tomography for pore-scale research: Illustrative experiments and perspectives on the future , 2016 .

[30]  William R B Lionheart,et al.  SparseBeads data: benchmarking sparsity-regularized computed tomography , 2017 .

[31]  John Banhart,et al.  Fast Synchrotron X‐Ray Tomography of Dynamic Processes in Liquid Aluminium Alloy Foam   , 2017 .

[32]  L. Feldkamp,et al.  Practical cone-beam algorithm , 1984 .

[33]  Andrew G. Stack,et al.  The dynamic nature of crystal growth in pores , 2016, Scientific Reports.

[34]  S. A. McDonald,et al.  Employing temporal self-similarity across the entire time domain in computed tomography reconstruction , 2015, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[35]  P. Withers,et al.  Time-lapse 3D imaging of calcite precipitation in a microporous column , 2018 .

[36]  Olaf Dössel,et al.  An Optimal Radial Profile Order Based on the Golden Ratio for Time-Resolved MRI , 2007, IEEE Transactions on Medical Imaging.

[37]  Per Christian Hansen,et al.  Regularization Tools version 4.0 for Matlab 7.3 , 2007, Numerical Algorithms.

[38]  M. Hestenes,et al.  Methods of conjugate gradients for solving linear systems , 1952 .

[39]  Oluwadamilola O. Taiwo,et al.  Investigating the evolving microstructure of lithium metal electrodes in 3D using X-ray computed tomography. , 2017, Physical chemistry chemical physics : PCCP.

[40]  V. Deshpande,et al.  Deformation mechanisms of idealised cermets under multi-axial loading , 2017, Journal of the Mechanics and Physics of Solids.

[41]  Michal Snehota,et al.  Recent developments in neutron imaging with applications for porous media research , 2015 .

[42]  E. Y. Sidky,et al.  How little data is enough? Phase-diagram analysis of sparsity-regularized X-ray computed tomography , 2014, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[43]  William R B Lionheart,et al.  4D-CT reconstruction with unified spatial-temporal patch-based regularization , 2015 .

[44]  I. Sinclair,et al.  Imaging the interaction of roots and phosphate fertiliser granules using 4D X-ray tomography , 2016, Plant and Soil.

[45]  Philip J. Withers,et al.  Imaging in Four Dimensions , 2012 .

[46]  T. El-Adawy,et al.  Nutritional potential and functional properties of germinated mung bean, pea and lentil seeds , 2003 .

[47]  T. Lowe Time dependent variations in X-ray Computed Tomography data during repeated scanning , 2001 .

[48]  Philip J. Withers,et al.  Combining X-ray microtomography and three-dimensional digital volume correlation to track microstructure evolution during sintering of copper powder , 2014 .

[49]  William R B Lionheart,et al.  High speed imaging of dynamic processes with a switched source x-ray CT system , 2015 .

[50]  Anders Kaestner,et al.  Spatiotemporal computed tomography of dynamic processes , 2011 .

[51]  Johann Kastner,et al.  Special issue on the 6th conference on industrial computed tomography 2016 (iCT2016) , 2016 .

[52]  N. Ishida,et al.  Water uptake by dry beans observed by micro-magnetic resonance imaging. , 2006, Annals of botany.

[53]  Veerle Cnudde,et al.  X-ray computed micro-tomography to study the porous structure and degradation processes of a building stone from Sabucina (Sicily) , 2015 .

[54]  Hyoung-Koo Lee,et al.  Sparse-view neutron CT reconstruction of irradiated fuel assembly using total variation minimization with Poisson statistics , 2016, Journal of Radioanalytical and Nuclear Chemistry.

[55]  P. Withers,et al.  Quantitative X-ray tomography , 2014 .

[56]  Jan Sijbers,et al.  The ASTRA Toolbox: A platform for advanced algorithm development in electron tomography. , 2015, Ultramicroscopy.

[57]  Stéphane Roux,et al.  Digital Volume Correlation Applied to X‐ray Tomography Images from Spherical Indentation Tests on Lightweight Gypsum , 2014 .

[58]  P. Withers,et al.  Comparison of the Mechanical Behaviour of Standard and Auxetic Foams by X‐ray Computed Tomography and Digital Volume Correlation , 2013 .

[59]  S. Stock Recent advances in X-ray microtomography applied to materials , 2008 .

[60]  J. Godinho,et al.  Growth Kinetics and Morphology of Barite Crystals Derived from Face-Specific Growth Rates , 2015 .

[61]  P. Withers,et al.  Time-lapse imaging of particle invasion and deposition in porous media using in situ X-ray radiography , 2019, Journal of Petroleum Science and Engineering.

[62]  E. Sidky,et al.  Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization , 2008, Physics in medicine and biology.