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High-resolution computed microtomography for the characterization of a diffusion tensor imaging phantom

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Języki publikacji
EN
Abstrakty
EN
This paper addresses the issue of the quantitative characterization of the structure of the calibration model (phantom) for b-matrix spatial distribution diffusion tensor imaging (BSD-DTI) scanners. The aim of this study was to verify manufacturing assumptions of the structure of materials, since phantoms are used for BSD-DTI calibration directly after manufacturing. Visualization of the phantoms’ structure was achieved through optical microscopy and high-resolution computed microtomography (µCT). Using µCT images, a numerical model of the materials structure was developed for further quantitative analysis. 3D image characterization was performed to determine crucial structural parameters of the phantom: porosity, uniformity and distribution of equivalent diameter of capillary bundles. Additionally calculations of hypothetical flow streamlines were also performed based on the numerical model that was developed. The results obtained in this study can be used in the calibration of DTI-BST measurements. However, it was found that the structure of the phantom exhibits flaws and discrepancies from the assumed geometry which might affect BSD-DTI calibration.
Czasopismo
Rocznik
Strony
259--268
Opis fizyczny
Bibliogr. 24 poz.
Twórcy
  • Faculty of Geology, University of Warsaw, Warsaw, Poland
  • Faculty of Materials Science and Engineering, Warsaw University of Technology, Warsaw, Poland
  • Faculty of Materials Science and Engineering, Warsaw University of Technology, Warsaw, Poland
  • Faculty of Materials Science and Engineering, Warsaw University of Technology, Warsaw, Poland
autor
  • Faculty of Geology, Geophysics and Environmental Protection, University of Science and Technology in Kraków, Kraków, Poland
Bibliografia
  • 1. Appoloni C, Fernandes C, Rodrigues C (2007) X-ray microtomography study of a sandstone reservoir rock. Nucl Instrum Methods Phys Res Sect A 580:629–632. doi:10.1016/j.nima.2007.05.027CrossRefGoogle Scholar
  • 2. Aurenhammer F (1991) Voronoi diagrams—a survey of a fundamental geometric data structure. ACM Comput Surv 23:345–405. doi:10.1145/116873.116880CrossRefGoogle Scholar
  • 3. Baker D, Mancini L, Polacci M, Higgins M, Gualda G, Hill R, Rivers M (2012) An introduction to the application of X-ray microtomography to the three-dimensional study of igneous rocks. Lithos 148:262–276. doi:10.1016/j.lithos.2012.06.008CrossRefGoogle Scholar
  • 4. Bielecki J, Jarzyna J, Bożek S, Lekki J, Stachura Z, Kwiatek W (2013) Computed microtomography and numerical study of porous rock samples. Radiat Phys Chem 93:59–66. doi:10.1016/j.radphyschem.2013.03.050CrossRefGoogle Scholar
  • 5. Callaghan P (1994) Principles of nuclear magnetic resonance microscopy. Oxford University Press, OxfordGoogle Scholar
  • 6. Coates GR, Xiao L, Prammer MG (1999) NMR logging principles and applications. Halliburton Energy Services Publication, HoustonGoogle Scholar
  • 7. Das S (2004) Nuclear magnetic resonance spectroscopy. Resonance 9:34–49CrossRefGoogle Scholar
  • 8. Dvorkin J, Derzhi N, Fang Q, Nur A, Nur B, Grader A, Baldwin C, Tono H, Diaz E (2009) From micro to reservoir scale: permeability from digital experiments. Lead Edge 28:1446–1452. doi:10.1190/1.3272699CrossRefGoogle Scholar
  • 9. Gnudde V, Boone M (2013) High-resolution X-ray computed tomography in geosciences: a review of the current technology and applications. Earth Sci Rev 123:1–17. doi:10.1016/j.earscirev.2013.04.003CrossRefGoogle Scholar
  • 10. Kaczmarek Ł, Maksimczuk M, Wejrzanowski T, Krzyżak A (2015) High-resolution X-ray microtomography and nuclear magnetic resonance study of a carbonate reservoir rock. In: Proc. 15th International Multidisciplinary Scientific GeoConference SGEM 2015, June 18–24, 2015, Albena. doi:10.5593/SGEM2015/B11/S6.099
  • 11. Ketcham R, Carlson W (2001) Acquisition, optimization and interpretation of X-ray computed tomographic imagery: applications to the geosciences. Comput Geosci 27:381–400. doi:10.1016/S0098-3004(00)00116-3CrossRefGoogle Scholar
  • 12. Komlosh ME, Özarslan E, Lizak MJ, Horkay F, Schram V, Shemesh N, Cohen Y, Basser PJ (2011) Pore diameter mapping using double pulsed-field gradient MRI and its validation using a novel glass capillary array phantom. J Magn Reson 208:128–135. doi:10.1016/j.jmr.2010.10.014CrossRefGoogle Scholar
  • 13. Krzyżak AT, Olejniczak Z (2014) Improving the accuracy of PGSE DTI experiments using the spatial distribution of b matrix. Magn Reson Imaging 33:286–295. doi:10.1016/j.mri.2014.10.007CrossRefGoogle Scholar
  • 14. Krzyżak AT, Jasiński A, Adamek D (2005) Qualification of the most statistically “sensitive” diffusion tensor imaging parameters for detection of spinal cord injury. Acta Phys Pol Ser A 108:207–210CrossRefGoogle Scholar
  • 15. Krzyżak AT, Jasiński A, Kwieciński S, Kozłowski P, Adamek D (2008) Quantitative assessment of injury in rats spinal cord in vivo using MRI of water diffusion tensor. Appl Magn Reson 34:1–24. doi:10.1007/s00723-008-0095-7CrossRefGoogle Scholar
  • 16. Krzyżak AT, Kłodowski K, Raszewski Z (2015) Anisotropic phantoms in magnetic resonance imaging. In: Proc. 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, August 25–29, 2015, MiCo, MilanoGoogle Scholar
  • 17. Petchsingto T, Karpyn T (2009) Deterministic modeling of fluid flow through a CT-scanned fracture using computational fluid dynamic. Energy Sources Part A Recovery Util Environ Effects 31:897–905. doi:10.1080/15567030701752842CrossRefGoogle Scholar
  • 18. Straley C, Rossini D, Vinegar H, Tutunjian P, Morriss C (1995) Core analysis by low field NMR. SCA-9404, pp 43–56Google Scholar
  • 19. Sun Q, Xue L, Zhu S (2015) Permeability evolution and rock brittle failure. Acta Geophys 63(4):978–999. doi:10.1515/acgeo-2015-0017CrossRefGoogle Scholar
  • 20. Tomanek B, Jasiñski A, Sulek Z, Muszyñska J, Kulinowski P, Kwieciński S, Krzyżak A, Skórka T, Kibiński J (1996) Magnetic resonance microscopy of internal structure drone and queen honey bees. J Apic Res 35:3–9. doi:10.1080/00218839.1996.11100907CrossRefGoogle Scholar
  • 21. Twarduś E, Nowicka A (2014) Dokumentacja wynikowa otworu badawczego Opalino. PGNIG, PiłaGoogle Scholar
  • 22. Wei D-F, Liu X-P, Hu X-X, Xu R, Zhu L-L (2015) Estimation of permeability from NMR logs based on formation classification method in tight gas sands. Acta Geophys 63(5):1316–1338. doi:10.1515/acgeo-2015-0042CrossRefGoogle Scholar
  • 23. Wejrzanowski T, Spychalski WL, Rożniatowski K, Kurzydłowski KJ (2008) Image based analysis of complex microstructures of engineering materials. Int J Appl Math Comput Sci 18:33–39. doi:10.2478/v10006-008-0003-1CrossRefGoogle Scholar
  • 24. Xiao L, Zou C, Mao Z, Shi Y, Liu XP, Jin Y, Guo H, Hu X (2013) Estimation of water saturation from nuclear magnetic resonance (NMR) and conventional logs in low permeability sandstone reservoirs. J Petrol Sci Eng 108:40–51. doi:10.1016/j.petrol.2013.05.009
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f821ad78-e12a-4131-9424-7d327c049af5
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