Iterative Algorithm for 4D Tomography Reconstruction Using a Single Projection per Time Step

In this paper, a new method for the mathematical tomographic reconstruction of time-varying objects (4D tomography) is presented. The method is applicable under the conditions that the amount of substance at each spatial point does not decrease, and only one projection image is available per time point during the process of the object changing. The temporal resolution of the proposed method is obtained by using an iterative approach to reconstruction. Prior knowledge about object morphology is exploited to improve the convergence of the algorithm by reducing the number of processed spatial points. Verification of the algorithm operation was carried out using model examples. The presented method was experimentally tested on the tomographic reconstruction of the dynamics of the capillary rise of a liquid. Our experiments demonstrated that monitoring fast processes, when the speed of the process does not allow to measure more than one tomographic projection, is potentially possible.

[1]  W. Han,et al.  Characterizing locality- and scale-dependent heterogeneity in conglomerate core and associated fluid flow using X-ray CT imaging , 2021 .

[2]  Jiang Hsieh,et al.  Computed tomography recent history and future perspectives , 2021, Journal of medical imaging.

[3]  A. Bera,et al.  A review on pore-scale modeling and CT scan technique to characterize the trapped carbon dioxide in impermeable reservoir rocks during sequestration , 2021, Renewable and Sustainable Energy Reviews.

[4]  P. Maass,et al.  LoDoPaB-CT, a benchmark dataset for low-dose computed tomography reconstruction , 2021, Scientific data.

[5]  Zhangxiang Hao,et al.  A Multiscale X-Ray Tomography Study of the Cycled-Induced Degradation in Magnesium-Sulfur Batteries. , 2021, Small methods.

[6]  M.V. Chukalina,et al.  Algorithm for post-processing of tomography images to calculate the dimension-geometric features of porous structures , 2021, Computer Optics.

[7]  Jonathan Sanctorum,et al.  FleXCT: a flexible X-ray CT scanner with 10 degrees of freedom. , 2020, Optics express.

[8]  A. Sadate,et al.  Ultra-low-dose CT versus radiographs for minor spine and pelvis trauma: a Bayesian analysis of accuracy , 2020, European Radiology.

[9]  Maxim Grigoriev,et al.  Robust technique for representative volume element identification in noisy microtomography images of porous materials based on pores morphology and their spatial distribution , 2020, International Conference on Machine Vision.

[10]  Orestis Malaspinas,et al.  Palabos: Parallel Lattice Boltzmann Solver , 2020, Comput. Math. Appl..

[11]  L. Berlouis,et al.  Multiscale pore structure analysis of nano titanium dioxide cement mortar composite , 2020, Materials Today Communications.

[12]  Willem Jan Palenstijn,et al.  Real-time reconstruction and visualisation towards dynamic feedback control during time-resolved tomography experiments at TOMCAT , 2019, Scientific Reports.

[13]  Marina V. Chukalina,et al.  Polychromatic CT Data Improvement with One-Parameter Power Correction , 2019, Mathematical Problems in Engineering.

[14]  Matthias Balzer,et al.  Laboratory Microtomographs: Design and Data Processing Algorithms , 2018, Crystallography Reports.

[15]  Jelle Vlassenbroeck,et al.  MoVIT: a tomographic reconstruction framework for 4D-CT. , 2017, Optics express.

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

[17]  Philip J. Withers,et al.  4-D imaging of sub-second dynamics in pore-scale processes using real-time synchrotron X-ray tomography , 2016 .

[18]  Kees Joost Batenburg,et al.  An Iterative CT Reconstruction Algorithm for Fast Fluid Flow Imaging , 2015, IEEE Transactions on Image Processing.

[19]  Dariush Hampai,et al.  3D structure of liquid sprays: X-ray μ-radiography and tomography by polycapillary based technique , 2015 .

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

[21]  Tomy dos Santos Rolo,et al.  In vivo X-ray cine-tomography for tracking morphological dynamics , 2014, Proceedings of the National Academy of Sciences.

[22]  Xiao-dong Wang,et al.  Study on initial stage of capillary rise dynamics , 2013 .

[23]  A. Orekhov,et al.  Comparison of the data of X-ray microtomography and fluorescence analysis in the study of bone-tissue structure , 2012 .

[24]  Martin Szegedi,et al.  4D CT image reconstruction with diffeomorphic motion model , 2012, Medical Image Anal..

[25]  Guang-Hong Chen,et al.  Time-Resolved Interventional Cardiac C-arm Cone-Beam CT: An Application of the PICCS Algorithm , 2012, IEEE Transactions on Medical Imaging.

[26]  N. Fries,et al.  Dimensionless scaling methods for capillary rise. , 2009, Journal of colloid and interface science.

[27]  Alexander Sasov,et al.  Four-dimensional time-resolved micro-CT imaging for small animals , 2008, Optical Engineering + Applications.

[28]  Weiguo Lu,et al.  A simple fixed-point approach to invert a deformation field. , 2007, Medical physics.

[29]  W. Kalender.  X-ray computed tomography , 2006, Physics in medicine and biology.

[30]  J. France,et al.  Helical computed tomography alone compared with plain radiographs with adjunct computed tomography to evaluate the cervical spine after high-energy trauma. , 2005, The Journal of bone and joint surgery. American volume.

[31]  Michael C. Sukop,et al.  Lattice Boltzmann Modeling: An Introduction for Geoscientists and Engineers , 2005 .

[32]  M. V. van Herk,et al.  Respiratory correlated cone beam CT. , 2005, Medical physics.

[33]  T. Kohler.  A projection access scheme for iterative reconstruction based on the golden section , 2004, IEEE Symposium Conference Record Nuclear Science 2004..

[34]  Manuel Dierick,et al.  Octopus, a fast and user-friendly tomographic reconstruction package developed in LabView® , 2004 .

[35]  Dirk Bucher,et al.  Interjoint coordination in the stick insect leg-control system: the role of positional signaling. , 2003, Journal of neurophysiology.

[36]  Anatoly Snigirev,et al.  Tomography with high resolution , 2002, Optics + Photonics.

[37]  Hiroshi Aradate,et al.  Large-area two-dimensional detector for real-time three-dimensional CT (4D CT) , 2001, SPIE Medical Imaging.

[38]  D. Quéré,et al.  Rebounds in a capillary tube , 1999 .

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

[40]  L D Harris,et al.  Identification of the Optimal Orientation of Oblique Sections Through Multiple Parallel CT Images , 1981, Journal of computer assisted tomography.

[41]  E. W. Washburn.  The Dynamics of Capillary Flow , 1921 .

[42]  Jian Fu,et al.  In-line phase contrast micro-CT reconstruction for biomedical specimens. , 2014, Bio-medical materials and engineering.

[43]  Demetri Terzopoulos,et al.  A dynamic finite element surface model for segmentation and tracking in multidimensional medical images with application to cardiac 4D image analysis. , 1995, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[44]  I. I. Rushakov,et al.  Computed Tomography , 2019, Compendium of Biomedical Instrumentation.