Skip to main content
CT Lab GX
Features
  • Sample stationary geometry
  • Ultra-high-speed CT scan (max. 3.9 seconds/scan) and image reconstruction (15 seconds/scan)
  • Ex-vivo & in-vivo compatible
  • Easy-to-use software

Stationary sample, high-speed X-ray CT scanner

Ex-vivo, in-vivo, ultrafast in-situ compatible

Rigaku CT Lab GX series provides ultra-high-speed 3D X-ray micro-CT imaging. Samples are easy to mount, and live mode and in-situ imaging are available.

Sample stationary fast scan: The sample stationary geometry eliminates problems due to sample holding and sample drift. Samples can simply be placed on the sample bed with no glue or tape and do not move during scans. This geometry, combined with high-speed gantry rotation, enables high-speed data collection at 3.9 seconds per scan.

Live mode and in-situ imaging: Observe real-time structural changes in-situ with 2D live mode image collection or high-speed CT scans. These features are effective for observing flow and diffusion of liquid or structural change caused by environmental changes.

Specifications
Product name CT Lab GX130
Benefit Sample stationary high-speed X-ray CT scanner
Technology X-ray computed tomography
X-ray generator 39 W sealed micro-source
Tube voltage 30 to 130 kV
Tube current up to 300 μA
Target W
Detector Flat-panel
Field of view Maximum 72 x 120 mm
Resolution Maximum 4.5 μm
 
Product name CT Lab GX90
Benefit Sample stationary high-speed X-ray CT scanner
Technology X-ray computed tomography
X-ray generator 8 W sealed micro-source
Tube voltage 30 to 90 kV
Tube current up to 200 μA
Target W
Detector Flat-panel
Field of view Maximum 72 x 120 mm
Resolution Maximum 4.5 μm

Video for CTLab GX

Application Bytes


Capsule

Corn

Tablets in package 1

Tablets in package 2

Wart remover


Learn more about our products at these events

Booth number Date Location Event website
Ceramics Expo 2021 - Cleveland, OH Website

Webinars - X-ray Computed Tomography for Materials Science

Aya Takase
An introduction to the X-ray computed tomography (CT) technique. Designed to show how X-ray CT works and how it can be applied to scientific research. It will include an introduction to the technique, instrumentation and application examples.
Aya Takase
An overview of X-ray CT data analysis techniques starting with basic image processing and leading to traditional segmentation, machine learning based segmentation and quantitative analysis. A number of commonly used data analysis and visualization programs will be discussed and demonstrated to help beginners to get an idea of where to start and select the right analysis tool for their needs
Aya Takase
A number of X-ray CT application examples in the food and pharmaceutical industries will be discussed. Examples to include the analyses of cracks and aggregation inside tablets, tablet and drug particle coating thicknesses, air pocket size distributions in food and sugar coating thicknesses of candies.
Aya Takase
A number of X-ray CT application examples of foams and composite materials will be discussed. Examples to include the analysis of foam porosity, cell size, and cell wall thicknesses as well as void distribution and fiber orientation in composite materials.
Angela Criswell
A number of X-ray CT application examples for plants and seeds will be discussed. Examples to include the non-destructive characterization of plant traits for ripening fruit, seeds and root systems. Additionally, analysis of cell wall thicknesses and void distribution among different seed varieties will be presented.
Aya Takase
A number of X-ray CT application examples of geological samples will be discussed. Examples include the analysis of cracks, pores, inclusions, and phase quantification of rocks and drill cores. We will introduce available resources for pore network analysis that can be applied to rock CT scans.
Angela Criswell
A 3D look at the structures of a reptile, insects, and a mouse, including their stained organs. In the newest episode of the webinar series “X-ray Computed tomography for Materials & Life Science,” we will discuss how to deal with unique challenges in life science sample preparation and introduce some quantitative analyses.
Aya Takase
Basics of metrology analysis and a number of X-ray CT application examples will be discussed. Examples include size and shape measurements of metal and plastic parts, tolerancing evaluation, comparison of nominal (CAD) and actual (CT) or a golden standard and a test subject. We will also introduce available resources to learn more about X-ray CT metrology.
Angela Criswell

This webinar will discuss basics of X-ray computed tomography and how to apply X-ray CT methods to soft materials. Additionally, we will show 3D imaging results for pharmaceuticals, foams, composites and other soft material.

We invite you to view this short video about the Rigaku nano3DX X-ray microscope for microtomography of large samples at high resolution in advance of the webinar.

X-ray CT Publications


Our first CT project

Morio ONOE, Jing Wen TSAO and Hiroaki YAMADA, Hiroshi NAKAMURA, Jin KOGURE and Hiromi KAWAMURA, M. Y. (1984). COMPUTED TOMOGRAPHY FOR MEASURING THE ANNUAL RINGS OF A LIVE TREE. Nuclear Instruments and Methods in Physics Research, 221, 213-220. https://www.sciencedirect.com/science/article/pii/0167508784902023 

  1. Joseph P. Neilly, Leilei Yin, Sarah-Ellen Leonard, Paul J.A. Kenis, Gerald D. Danzer, Ashtamurthy S. Pawate (2019) Quantitative Measures of Crystalline Fenofibrate in Amorphous Solid Dispersion Formulations by X-Ray Microscopy, Journal of Pharmaceutical Sciences, 109(10), 3078-3085 https://www.sciencedirect.com/science/article/pii/S0022354920303683
  2. Carolina Oliver-Urrutia, Raúl Rosales Ibañez, Miriam V. Flores-Merino, Lucy Vojtova, Jakub Salplachta, Ladislav Čelko, Jozef Kaiser, and Edgar B. Montufar (2021). Lyophilized Polyvinylpyrrolidone Hydrogel for Culture of Human Oral Mucosa Stem Cells. Materials 14, 227. https://www.mdpi.com/1996-1944/14/1/227/htm
  3. Tomáš Sedlačík, Takayuki Nonoyama, Honglei Guo, Ryuji Kiyama, Tasuku Nakajima, Yoshihiro Takeda, Takayuki Kurokawa, and Jian Ping Gong (2020). Preparation of Tough Double- and Triple-Network Supermacroporous Hydrogels through Repeated Cryogelation. Chem. Mater. Published online18 September 2020. https://pubs.acs.org/doi/abs/10.1021/acs.chemmater.0c02911
  4. Nanako Sakata, Yoshihiro Takeda, Masaru Kotera, Yasuhito Suzuki, and Akikazu Matsumoto. (2020). Interfacial Structure Control and Three-Dimensional X-ray Imaging of an Epoxy Monolith Bonding System with Surface Modification. Langmuir, 36, 37, 10923–10932. https://pubs.acs.org/doi/10.1021/acs.langmuir.0c01481
  5. Kenji Ohta , Tatsuya Wakamatsu, Manabu Kodama , Katsuyuki Kawamura, and Shuichiro Hirai. Laboratory-based x-ray computed tomography for 3D imaging of samples in a diamond anvil cell in situ at high pressures. Rev. Sci. 91, 091101. https://aip.scitation.org/doi/pdf/10.1063/5.0014486
  6. Fukami, T., Koide, T., Hisada, H., Inoue, M., Yamamoto, Y., Suzuki, T., & Tomono, K. (2016). Pharmaceutical evaluation of atorvastatin calcium tablets available on the Internet: A preliminary investigation of substandard medicines in Japan. Journal of Drug Delivery Science and Technology, 31, 35-40. https://doi.org/10.1016/j.jddst.2015.11.006 
  7. Kalasova, D., Zikmund, T., Pina, L., Takeda, Y., Horvath, M., Omote, K., & Kaiser, J. (2019). Characterization of a laboratory-based X-ray computed nanotomography system for propagation-based method of phase contrast imaging. IEEE Transactions on Instrumentation and Measurement, PP(c), 1-1. https://doi.org/10.1109/tim.2019.2910338 
  8. Kunishima, N., Takeda, Y., Hirose, R., Kalasová, D., Šalplachta, J., & Omote, K. (2020). Visualization of internal 3D structure of small live seed on germination by laboratory-based X-ray microscopy with phase contrast computed tomography. Plant Methods, 16(1), 1-10. https://doi.org/10.1186/s13007-020-0557-y 
  9. Zhang, S., Byrnes, A. P., Jankovic, J., & Neilly, J. (2019). Management, Analysis, and Simulation of Micrographs with Cloud Computing. Microscopy Today, 27(2), 26-33. https://doi.org/10.1017/s1551929519000026 
  10. Kalasová, D., Zikmund, T., Pína, L., Horváth, M., & Kaiser, J. (2016). Phase contrast tomographic imaging of polymer composites. 2020, 2020. http://ctlab.ceitec.cz/files/252/165.pdf 
  11. Kalasova, D., Pavlinakova, V., Zikmund, T., Vojtova, L., & Kaiser, J. (2018). Correlation of X-ray Computed Nanotomography and Scanning Electron Microscopy Imaging of Collagen Scaffolds. Microscopy and Microanalysis, 24(S2), 104-105. https://doi.org/10.1017/s1431927618012904 
  12. Hisada, K., Matsuoka, M., Tabata, I., Hirogaki, K., & Hori, T. (2013). Two-step radical grafting onto polypropylene fiber initiated by active species prepared through the irradiation of electron beam. Journal of Photopolymer Science and Technology, 26(2), 277-282. https://doi.org/10.2494/photopolymer.26.277 
  13. Sekita, A., Matsugaki, A., & Nakano, T. (2017). Disruption of collagen/apatite alignment impairs bone mechanical function in osteoblastic metastasis induced by prostate cancer. Bone, 97, 83-93. https://doi.org/10.1016/j.bone.2017.01.004 
  14. Nanako Sakata, Yoshihiro Takeda, Masaru Kotera, Yasuhito Suzuki, and A. M. (2020). Non‐destructive 3D X‐ray Imaging of Internal and Interfacial Structure of Epoxy Monolith and Strength Control by Surface Modification for the Monolith Bonding System. Langmuir. https://pubs.acs.org/doi/abs/10.1021/acs.langmuir.0c01481 
  15. Tomáš Sedlačík, Takayuki Nonoyama, Honglei Guo, Tasuku Nakajima, Yoshihiro Takeda, Takayuki Kurokawa, J. P. G. (2020). Tough Double- and Triple- Network Supermacroporous Hydrogels through Repeated Cryogelation. ACS Publications. https://pubs.acs.org/doi/abs/10.1021/acs.chemmater.0c02911 
  16. Kakio, T., Yoshida, N., Macha, S., Moriguchi, K., Hiroshima, T., Ikeda, Y., Kimura, K. (2017). Classification and visualization of physical and chemical properties of falsified medicines with handheld Raman spectroscopy and X-Ray computed tomography. American Journal of Tropical Medicine and Hygiene, 97(3), 684-689. https://doi.org/10.4269/ajtmh.16-0971 
  17. Takase, A., McNulty, T., & Fitzgibbons, T. (2018). Foam Porosity Calculation by X-Ray Computed Tomography and Errors Caused by Insufficient Resolution. Microscopy and Microanalysis, 24(S2), 546-547. https://doi.org/10.1017/s1431927618014927 
  18. Omote, K., Iwata, T., Takeda, Y., & Ferrara, J. D. (2017). Investigation for fuel-cell structures with multi-scale X-ray analysis. Rigaku Journal, 33(2), 8-13. https://www.semanticscholar.org/paper/Investigation-for-fuel-cell-structures-with-X-ray-Omote-Iwata/78e57af8fa13252533eabb7d5e5a32ceadac9eaa?p2df 
  19. Watanabe, M., Takeda, Y., Maruyama, T., Ikeda, J., Kawai, M., & Mitsumata, T. (2019). Chain structure in a cross-linked polyurethane magnetic elastomer under a magnetic field. International Journal of Molecular Sciences, 20(12). https://doi.org/10.3390/ijms20122879 
  20. Kunishima, N., Takeda, Y., Hirose, R., Kalasová, D., Šalplachta, J., & Omote, K. (2020). Visualization of internal 3D structure of small live seed on germination by laboratory-based X-ray microscopy with phase contrast computed tomography. Plant Methods, 16(1), 1-10. https://doi.org/10.1186/s13007-020-0557-y 
  21. Kalasova, D., Zikmund, T., Pina, L., Takeda, Y., Horvath, M., Omote, K., & Kaiser, J. (2020). Characterization of a laboratory-based x-ray computed nanotomography system for propagation-based method of phase contrast imaging. IEEE Transactions on Instrumentation and Measurement, 69(4), 1170-1178. https://doi.org/10.1109/TIM.2019.2910338 
  22. Akitomo, F., Sasabe, T., Yoshida, T., Naito, H., Kawamura, K., & Hirai, S. (2019). Investigation of effects of high temperature and pressure on a polymer electrolyte fuel cell with polarization analysis and X-ray imaging of liquid water. Journal of Power Sources, 431(February), 205-209. https://doi.org/10.1016/j.jpowsour.2019.04.115 
  23. Watanabe, M., Takeda, Y., Maruyama, T., Ikeda, J., Kawai, M., & Mitsumata, T. (2019). Chain structure in a cross-linked polyurethane magnetic elastomer under a magnetic field. International Journal of Molecular Sciences, 20(12). https://doi.org/10.3390/ijms20122879 
  24. Kalasová, D., Zikmund, T., Mancini, L., Jaroš, J., Tesařová, M., Kaucká, M., Kaiser, J. (2016). Industrial Tomography System for Answering Biological Issues: Development of the Mouse Embryo Face. 6th Conference on Industrial Computed Tomography (ICT 2016), (iCT), 1-9. https://www.ndt.net/article/ctc2016/papers/ICT2016_paper_id37.pdf 
  25. Tanaka, K., Yamada, T., Moriito, K., & Katayama, T. (2016). The effect of molding pressure on the mechanical properties of CFRTP using paper-type intermediate material. High Performance and Optimum Design of Structures and Materials II, 1(Hpsm), 307-315. https://doi.org/10.2495/hpsm160281 
  26. Watanabe, M., Ikeda, J., Takeda, Y., Kawai, M., & Mitsumata, T. (2018). Effect of Sonication Time on Magnetorheological Effect for Monomodal Magnetic Elastomers. Gels, 4(2), 49. https://doi.org/10.3390/gels4020049 
  27. Fukami, T., Koide, T., Hisada, H., Inoue, M., Yamamoto, Y., Suzuki, T., & Tomono, K. (2016). Pharmaceutical evaluation of atorvastatin calcium tablets available on the Internet: A preliminary investigation of substandard medicines in Japan. Journal of Drug Delivery Science and Technology, 31, 35-40. https://doi.org/10.1016/j.jddst.2015.11.006 
  28. Eiichi Yamamoto, Yoshihiro Takeda, Daisuke Ando, Tatsuo Koide, Yuta Amano, Shingo Miyazaki, Tamaki Miyazaki, Ken-ichi Izutsu, Hideko Kanazawa, and Yukihiro Goda. (2021). Discrimination of ranitidine hydrochloride crystals using X-ray micro-computed tomography for the evaluation of three-dimensional spatial distribution in solid dosage forms. Int. J. Pharm. Available online 28 June 2021, 120834 https://doi.org/10.1016/j.ijpharm.2021.120834

  1. Aung, W., Jin, Z. H., Furukawa, T., Claron, M., Boturyn, D., Sogawa, C., … Saga, T. (2013). Micro-positron emission tomography/contrast-enhanced computed tomography imaging of orthotopic pancreatic tumor-bearing mice using the αvβ3 integrin tracer 64Cu-labeled cyclam-RAFT-c(-RGDfK-)4. Molecular Imaging, 12(6), 376-387. https://doi.org/10.2310/7290.2013.00054 
  2. Arai, Y., Yamada, A., Ninomiya, T., Kato, T., & Masuda, Y. (2005). Micro-computed tomography newly developed for in vivo small animal imaging. Oral Radiology, 21(1), 14-18. https://doi.org/10.1007/s11282-005-0024-5 
  3. Reedy, C. L. (2020). 3D Documentation and Analysis of Porosity in Deteriorated Historic Brick. Studies in Conservation, 0(0), 1-4. https://doi.org/10.1080/00393630.2020.1752426 
  4. Iikubo, M., Nishioka, T., Okura, S., Kobayashi, K., Sano, T., Katsumata, A., … Sasano, T. (2016). Influence of voxel size and scan field of view on fracture-like artifacts from gutta-percha obturated endodontically treated teeth on cone-beam computed tomography images. Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology, 122(5), 631-637. https://doi.org/10.1016/j.oooo.2016.07.014 
  5. Benjamin M. Davis, Glen F. Rall, M. J. S. (2017). HHS Public Access. Physiology & Behavior, 176(1), 139-148. https://doi.org/10.1016/j.physbeh.2017.03.040 
  6. Bolmin, O., Wei, L., Hazel, A. M., Dunn, A. C., Wissa, A., & Alleyne, M. (2019). Latching of the click beetle (Coleoptera: Elateridae) thoracic hinge enabled by the morphology and mechanics of conformal structures. Journal of Experimental Biology, 222(12). https://doi.org/10.1242/jeb.196683 
  7. Kameoka, S., Matsumoto, K., Kai, Y., Yonehara, Y., Arai, Y., & Honda, K. (2010). Establishment of temporomandibular joint puncture technique in rats using in vivo micro-computed tomography (R-mCT®). Dentomaxillofacial Radiology, 39(7), 441-445. https://doi.org/10.1259/dmfr/37174063 

  1. Hagen, C. K., Vittoria, F. A., Morgó, O. R. I., Endrizzi, M., & Olivo, A. (2020). Cycloidal Computed Tomography. Physical Review Applied, 14(1), 1. https://doi.org/10.1103/PhysRevApplied.14.014069 
  2. Diemoz, P. C., Hagen, C. K., Endrizzi, M., Minuti, M., Bellazzini, R., Urbani, L., … Olivo, A. (2017). Single-Shot X-Ray Phase-Contrast Computed Tomography with Nonmicrofocal Laboratory Sources. Physical Review Applied, 7(4), 1-6. https://doi.org/10.1103/PhysRevApplied.7.044029 
  3. Hagen, C. K., Endrizzi, M., Diemoz, P. C., & Olivo, A. (2016). Reverse projection retrieval in edge illumination x-ray phase contrast computed tomography. Journal of Physics D: Applied Physics, 49(25). https://doi.org/10.1088/0022-3727/49/25/255501 
  4. Zamir, A., Endrizzi, M., Hagen, C. K., Vittoria, F. A., Urbani, L., De Coppi, P., & Olivo, A. (2016). Robust phase retrieval for high resolution edge illumination x-ray phase-contrast computed tomography in non-ideal environments. Scientific Reports, 6(August), 1-9. https://doi.org/10.1038/srep31197