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Journal articles on the topic 'X-Ray Computed'

Author: Grafiati
Published: 4 June 2021
Last updated: 31 July 2024

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1

Kalender, Willi A. "X-ray computed tomography." Physics in Medicine and Biology 51, no. 13 (June 20, 2006): R29—R43. http://dx.doi.org/10.1088/0031-9155/51/13/r03.

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2

Phillips,, D. H., and J. J. Lannutti. "X-ray computed tomography." NDT & E International 27, no. 2 (April 1994): 101. http://dx.doi.org/10.1016/0963-8695(94)90323-9.

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3

Michael, Greg. "X-ray computed tomography." Physics Education 36, no. 6 (October 19, 2001): 442–51. http://dx.doi.org/10.1088/0031-9120/36/6/301.

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4

Bonnet, S., A. Koenig, S. Roux, P. Hugonnard, R. Guillemaud, and P. Grangeat. "Dynamic X-ray computed tomography." Proceedings of the IEEE 91, no. 10 (October 2003): 1574–87. http://dx.doi.org/10.1109/jproc.2003.817868.

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5

Baird, Emily, and Gavin Taylor. "X-ray micro computed-tomography." Current Biology 27, no. 8 (April 2017): R289—R291. http://dx.doi.org/10.1016/j.cub.2017.01.066.

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6

Dubsky, S., R. A. Jamison, S. C. Irvine, K. K. W. Siu, K. Hourigan, and A. Fouras. "Computed tomographic x-ray velocimetry." Applied Physics Letters 96, no. 2 (January 11, 2010): 023702. http://dx.doi.org/10.1063/1.3285173.

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7

Harding, G., J. Kosanetzky, and U. Neitzel. "X-ray diffraction computed tomography." Medical Physics 14, no. 4 (July 1987): 515–25. http://dx.doi.org/10.1118/1.596063.

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8

Buynak, C. F., and R. H. Bossi. "Applied X-ray computed tomography." Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 99, no. 1-4 (May 1995): 772–74. http://dx.doi.org/10.1016/0168-583x(94)00615-6.

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9

Zhang, Wei, Dianwen Zhu, Michael Lun, and Changqing Li. "Collimated superfine x-ray beam based x-ray luminescence computed tomography." Journal of X-Ray Science and Technology 25, no. 6 (November 28, 2017): 945–57. http://dx.doi.org/10.3233/xst-17265.

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10

Zhang, Wei, Michael C. Lun, Alex Anh-Tu Nguyen, and Changqing Li. "X-ray luminescence computed tomography using a focused x-ray beam." Journal of Biomedical Optics 22, no. 11 (November 10, 2017): 1. http://dx.doi.org/10.1117/1.jbo.22.11.116004.

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11

Jago, Karine Le Gorju. "X-RAY COMPUTED MICROTOMOGRAPHY OF RUBBER." Rubber Chemistry and Technology 85, no. 3 (September 1, 2012): 387–407. http://dx.doi.org/10.5254/rct.12.87985.

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ABSTRACT In rubber science, X-ray computed microtomography (micro CT) is becoming an increasingly used technique to characterize 3D microstructures. As a first step, experimental methods, limitations, and data analysis are described. A review of published micro CT studies for rubber is reported. Examples of our recent works are presented, including investigations on samples or complex structures, for compact or foam rubbers. Micro CT is used to describe the evolution of microstructures relative to different processing steps, to environmental interaction, and to adaptation to a mechanical deformation. New insights and better understanding of damage mechanisms due to quasistatic, creep, and fatigue solicitations are presented from in situ micro CT experiments. Perspective studies are outlined.
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12

HAYASHI, KAZUO. "High Resolution X-Ray Computed Tomography." RADIOISOTOPES 42, no. 12 (1993): 721–22. http://dx.doi.org/10.3769/radioisotopes.42.12_721.

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13

Duarte, J., R. Cassin, J. Huijts, B. Iwan, F. Fortuna, L. Delbecq, H. Chapman, et al. "Computed stereo lensless X-ray imaging." Nature Photonics 13, no. 7 (April 15, 2019): 449–53. http://dx.doi.org/10.1038/s41566-019-0419-1.

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14

Qu, L. Z., O. Nalcioglu, W. W. Roeck, B. Rabbani, and M. Colman. "Video Based X-Ray Computed Tomography." IEEE Transactions on Nuclear Science 33, no. 1 (February 1986): 527–30. http://dx.doi.org/10.1109/tns.1986.4337158.

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15

Vamvakeros, Antonios, Simon D. M. Jacques, Marco Di Michiel, Pierre Senecal, Vesna Middelkoop, Robert J. Cernik, and Andrew M. Beale. "Interlaced X-ray diffraction computed tomography." Journal of Applied Crystallography 49, no. 2 (March 1, 2016): 485–96. http://dx.doi.org/10.1107/s160057671600131x.

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An X-ray diffraction computed tomography data-collection strategy that allows, post experiment, a choice between temporal and spatial resolution is reported. This strategy enables time-resolved studies on comparatively short timescales, or alternatively allows for improved spatial resolution if the system under study, or components within it, appear to be unchanging. The application of the method for studying an Mn–Na–W/SiO2 fixed-bed reactor in situ is demonstrated. Additionally, the opportunities to improve the data-collection strategy further, enabling post-collection tuning between statistical, temporal and spatial resolutions, are discussed. In principle, the interlaced scanning approach can also be applied to other pencil-beam tomographic techniques, like X-ray fluorescence computed tomography, X-ray absorption fine structure computed tomography, pair distribution function computed tomography and tomographic scanning transmission X-ray microscopy.
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16

Fu, Jian, Zhenzhong Liu, and Jingzheng Wang. "Multi-Mounted X-Ray Computed Tomography." PLOS ONE 11, no. 4 (April 13, 2016): e0153406. http://dx.doi.org/10.1371/journal.pone.0153406.

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17

Zhu, Zheyuan, and Shuo Pang. "Few-photon computed x-ray imaging." Applied Physics Letters 113, no. 23 (December 3, 2018): 231109. http://dx.doi.org/10.1063/1.5050890.

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18

Anthony Seibert, J., D. K. Shelton, and E. H. Moore. "Computed radiography x-ray exposure trends." Academic Radiology 2, no. 12 (December 1995): 1167. http://dx.doi.org/10.1016/s1076-6332(05)80655-5.

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19

Anthony Seibert, J., David K. Shelton, and Elizabeth H. Moore. "Computed radiography X-ray exposure trends." Academic Radiology 3, no. 4 (April 1996): 313–18. http://dx.doi.org/10.1016/s1076-6332(96)80247-9.

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20

Lusic, Hrvoje, and Mark W. Grinstaff. "X-ray-Computed Tomography Contrast Agents." Chemical Reviews 113, no. 3 (December 5, 2012): 1641–66. http://dx.doi.org/10.1021/cr200358s.

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21

Moroni, Giovanni, and Stefano Petrò. "Design for X-Ray Computed Tomography." Procedia CIRP 84 (2019): 173–78. http://dx.doi.org/10.1016/j.procir.2019.04.342.

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22

Samant, P., L. Trevisi, X. Ji, and L. Xiang. "X-ray induced acoustic computed tomography." Photoacoustics 19 (September 2020): 100177. http://dx.doi.org/10.1016/j.pacs.2020.100177.

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23

Kalender, Willi A. "Dose in x-ray computed tomography." Physics in Medicine and Biology 59, no. 3 (January 17, 2014): R129—R150. http://dx.doi.org/10.1088/0031-9155/59/3/r129.

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24

Lwin, Thet-Thet, Akio Yoneyama, Hiroko Maruyama, and Tohoru Takeda. "Visualization Ability of Phase-Contrast Synchrotron-Based X-Ray Imaging Using an X-Ray Interferometer in Soft Tissue Tumors." Technology in Cancer Research & Treatment 20 (January 1, 2021): 153303382110101. http://dx.doi.org/10.1177/15330338211010121.

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Phase-contrast synchrotron-based X-ray imaging using an X-ray interferometer provides high sensitivity and high spatial resolution, and it has the ability to depict the fine morphological structures of biological soft tissues, including tumors. In this study, we quantitatively compared phase-contrast synchrotron-based X-ray computed tomography images and images of histopathological hematoxylin-eosin-stained sections of spontaneously occurring rat testicular tumors that contained different types of cells. The absolute densities measured on the phase-contrast synchrotron-based X-ray computed tomography images correlated well with the densities of the nuclear chromatin in the histological images, thereby demonstrating the ability of phase-contrast synchrotron-based X-ray imaging using an X-ray interferometer to reliably identify the characteristics of cancer cells within solid soft tissue tumors. In addition, 3-dimensional synchrotron-based phase-contrast X-ray computed tomography enables screening for different structures within tumors, such as solid, cystic, and fibrous tissues, and blood clots, from any direction and with a spatial resolution down to 26 μm. Thus, phase-contrast synchrotron-based X-ray imaging using an X-ray interferometer shows potential for being useful in preclinical cancer research by providing the ability to depict the characteristics of tumor cells and by offering 3-dimensional information capabilities.
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25

Chen, Dongmei, Shouping Zhu, Xueli Chen, Tiantian Chao, Xu Cao, Fengjun Zhao, Liyu Huang, and Jimin Liang. "Quantitative cone beam X-ray luminescence tomography/X-ray computed tomography imaging." Applied Physics Letters 105, no. 19 (November 10, 2014): 191104. http://dx.doi.org/10.1063/1.4901436.

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26

Sala, Simone, Darren J. Batey, Anupama Prakash, Sharif Ahmed, Christoph Rau, and Pierre Thibault. "Ptychographic X-ray computed tomography at a high-brilliance X-ray source." Optics Express 27, no. 2 (January 8, 2019): 533. http://dx.doi.org/10.1364/oe.27.000533.

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27

Habrat, Magdalena, Paulina Krakowska, Edyta Puskarczyk, Mariusz Jędrychowski, and Paweł Madejski. "Technical Note. The Concept of a Computer System for Interpretation of Tight Rocks Using X-Ray Computed Tomography Results." Studia Geotechnica et Mechanica 39, no. 1 (March 28, 2017): 101–7. http://dx.doi.org/10.1515/sgem-2017-0010.

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Abstract The article presents the concept of a computer system for interpreting unconventional oil and gas deposits with the use of X-ray computed tomography results. The functional principles of the solution proposed are presented in the article. The main goal is to design a product which is a complex and useful tool in a form of a specialist computer software for qualitative and quantitative interpretation of images obtained from X-ray computed tomography. It is devoted to the issues of prospecting and identification of unconventional hydrocarbon deposits. The article focuses on the idea of X-ray computed tomography use as a basis for the analysis of tight rocks, considering especially functional principles of the system, which will be developed by the authors. The functional principles include the issues of graphical visualization of rock structure, qualitative and quantitative interpretation of model for visualizing rock samples, interpretation and a description of the parameters within realizing the module of quantitative interpretation.
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28

Onischenko, G. G., A. Yu Popova, I. K. Romanovich, A. V. Vodovatov, N. S. Bashketova, O. A. Istorik, L. A. Chipiga, I. G. Shatsky, L. V. Repin, and A. M. Biblin. "Modern principles of the radiation protection from sources of ionizing radiation in medicine. Part 1: Trends, structure of x-ray diagnostics and doses from medical exposure." Radiatsionnaya Gygiena = Radiation Hygiene 12, no. 1 (March 27, 2019): 6–24. http://dx.doi.org/10.21514/1998-426x-2019-12-1-6-24.

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Implementation of modern highly informative methods of X-ray diagnostics (computed tomography, interventional examinations, nuclear medicine), associated with the increase of doses to the public and patients, requires the development and improvement of the existing system of the radiation protection from medical exposure. Despite the prevalence of the traditional imaging modalities in the structure of X-ray diagnostics in the Russian Federation (radiography and fluorography compose up to 95% out of 280 mln. X-ray examinations performed in 2017), the major contribution into the collective dose from medical exposure is due to the computed tomography (50,5%). Comparison of the structure of X-ray diagnostics in the Russian Federation with European Union indicates the absence of fluorography examinations and significantly (up to a factor of 5) higher contribution of computed tomography in European countries. An average collective dose from medical exposure in European countries is composed of 80% of computed tomography and of 10% of nuclear medicine; a mean effective dose per X-ray examination are higher up to a factor of 3 compared to Russia. The analysis of the trends of the development of the X-ray diagnostic in the Russian Federation allows predicting a further increase of the number of computer tomography, interventional and nuclear medicine examinations as well as an increase of the collective dose from medical exposure up to a factor of two in the next decade. This will be associated with changes in the structure of the X-ray diagnostics and an increase of the mean effective doses from X-ray examinations.
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29

SCHNEIDER, G., E. ANDERSON, S. VOGT, C. KNÖCHEL, D. WEISS, M. LEGROS, and C. LARABELL. "COMPUTED TOMOGRAPHY OF CRYOGENIC CELLS." Surface Review and Letters 09, no. 01 (February 2002): 177–83. http://dx.doi.org/10.1142/s0218625x02001914.

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Soft X-ray microscopy has resolved 30 nm structures in biological cells. To protect the cells from radiation damage caused by X-rays, imaging of the samples has to be performed at cryogenic temperatures, which makes it possible to take multiple images of a single cell. Due to the small numerical aperture of zone plates, X-ray objectives have a depth of focus on the order of several microns. By treating the X-ray microscopic images as projections of the sample absorption, computed tomography (CT) can be performed. Since cryogenic biological samples are resistant to radiation damage, it is possible to reconstruct frozen-hydrated cells imaged with a full-field X-ray microscope. This approach is used to obtain three-dimensional information about the location of specific proteins in cells. To localize proteins in cells, immunolabeling with strongly X-ray absorbing nanoparticles was performed. With the new tomography setup developed for the X-ray microscope XM-1 installed at the ALS, we have performed tomography of immunolabeled frozen-hydrated cells to detect protein distributions inside of cells. As a first example, the distribution of the nuclear protein male-specific lethal 1 (MSL-1) in the Drosophila melanogaster cell was studied.
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30

Karahara, Ichirou, Daisuke Yamauchi, Kentaro Uesugi, and Yoshinobu Mineyuki. "Three-dimensional imaging of plant tissues using X-ray micro-computed tomography." PLANT MORPHOLOGY 27, no. 1 (2015): 21–26. http://dx.doi.org/10.5685/plmorphol.27.21.

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31

Foereid, Bente. "X-Ray Computed Tomography for Root Quantification." Open Journal of Soil Science 05, no. 07 (2015): 145–48. http://dx.doi.org/10.4236/ojss.2015.57014.

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32

MONKAWA, Akira, Hiroyuki CHIBA, Shinichi TOMIYAMA, and Syouhei TANIGUCHI. "Application Example of X-ray Computed Tomography." Journal of the Japan Society for Precision Engineering 82, no. 6 (2016): 518–22. http://dx.doi.org/10.2493/jjspe.82.518.

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33

Koizumi, Makoto. "X-ray computed tomography for laboratory animals." Drug Delivery System 30, no. 1 (2015): 55–58. http://dx.doi.org/10.2745/dds.30.55.

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34

Dabravolski, Andrei, Kees Joost Batenburg, and Jan Sijbers. "Adaptive zooming in X-ray computed tomography." Journal of X-Ray Science and Technology 22, no. 1 (2014): 77–89. http://dx.doi.org/10.3233/xst-130410.

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35

Morigi, Maria Pia, and Fauzia Albertin. "X-ray Digital Radiography and Computed Tomography." Journal of Imaging 8, no. 5 (April 21, 2022): 119. http://dx.doi.org/10.3390/jimaging8050119.

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36

Nagata, Yasuaki. "X-ray Computed Tomography Using Synchrotron Radiation." Materia Japan 35, no. 3 (1996): 250–54. http://dx.doi.org/10.2320/materia.35.250.

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37

Spanne, P. "X-ray energy optimisation in computed microtomography." Physics in Medicine and Biology 34, no. 6 (June 1, 1989): 679–90. http://dx.doi.org/10.1088/0031-9155/34/6/004.

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38

Hiriyannaiah, H. P. "X-ray computed tomography for medical imaging." IEEE Signal Processing Magazine 14, no. 2 (March 1997): 42–59. http://dx.doi.org/10.1109/79.581370.

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39

Bieberle, Martina, Frank Barthel, Hans-Jürgen Menz, Hans-Georg Mayer, and Uwe Hampel. "Ultrafast three-dimensional x-ray computed tomography." Applied Physics Letters 98, no. 3 (January 17, 2011): 034101. http://dx.doi.org/10.1063/1.3534806.

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40

Xin Liu, Qimei Liao, and Hongkai Wang. "Fast X-Ray Luminescence Computed Tomography Imaging." IEEE Transactions on Biomedical Engineering 61, no. 6 (June 2014): 1621–27. http://dx.doi.org/10.1109/tbme.2013.2294633.

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41

Hunt, P. K., P. Engler, and W. D. Friedman. "Industrial Applications of X-Ray Computed Tomography." Advances in X-ray Analysis 31 (1987): 99–105. http://dx.doi.org/10.1154/s0376030800021893.

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Computed tomography (CT), commonly known as CAT scanning (computerized axial tomography), is a technology that produces an image of the internaI structure of a cross sectional slice through an object via the reconstruction of a matrix of X-ray attenuation coefficients. This non-destructive method is fast (50 ms to 7 min per image depending on the technological generation of the instrument) and requires minimal sample preparation. Images are generated from digital computations, and instruments essentially have a linear response. This allows quantitative estimations of density variations, dimensions and areas directly from console displays.
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42

Wijesekera, N. T., M. K. Duncan, and S. P. G. Padley. "X-ray computed tomography of the heart." British Medical Bulletin 93, no. 1 (November 17, 2009): 49–67. http://dx.doi.org/10.1093/bmb/ldp043.

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43

Ijiri, Takashi, Shin Yoshizawa, Hideo Yokota, and Takeo Igarashi. "Flower modeling via X-ray computed tomography." ACM Transactions on Graphics 33, no. 4 (July 27, 2014): 1–10. http://dx.doi.org/10.1145/2601097.2601124.

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44

de Jonge, Martin D., Andrew M. Kingston, Nader Afshar, Jan Garrevoet, Robin Kirkham, Gary Ruben, Glenn R. Myers, et al. "Spiral scanning X-ray fluorescence computed tomography." Optics Express 25, no. 19 (September 15, 2017): 23424. http://dx.doi.org/10.1364/oe.25.023424.

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45

Winter, J. M., R. E. Green, A. M. Waters, and W. H. Green. "X-Ray Computed Tomography of Ultralightweight Metals." Research in Nondestructive Evaluation 11, no. 1 (1999): 199–211. http://dx.doi.org/10.1080/09349849908968156.

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46

Winter, J. M., R. E. Green, A. M. Waters, and W. H. Green. "X-Ray Computed Tomography of Ultralightweight Metals." Research in Nondestructive Evaluation 11, no. 4 (January 1999): 199–211. http://dx.doi.org/10.1080/09349849909409642.

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47

Garcea, S. C., Y. Wang, and P. J. Withers. "X-ray computed tomography of polymer composites." Composites Science and Technology 156 (March 2018): 305–19. http://dx.doi.org/10.1016/j.compscitech.2017.10.023.

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48

Georgeson, G. E., R. H. Bossi, and R. D. Rempt. "X-ray computed tomography for casting demonstration." NDT & E International 27, no. 2 (April 1994): 101. http://dx.doi.org/10.1016/0963-8695(94)90318-2.

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49

Bossi, R., A. Crews, G. Georgeson, J. Nelson, and J. Shrader. "X-ray computed tomography for geometry acquisition." NDT & E International 27, no. 2 (April 1994): 102. http://dx.doi.org/10.1016/0963-8695(94)90326-3.

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50

Georgeson, G. E., A. R. Crews, and R. H. Bossi. "X-ray computed tomography for casting development." NDT & E International 27, no. 2 (April 1994): 102. http://dx.doi.org/10.1016/0963-8695(94)90331-x.

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