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Volume 5 Issue 4
Dec.  2021
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Abstract
References

Monte Carlo simulation of a cone-beam CT system for lightweight casts

  • 1. Department of Nuclear Engineering and Environmental Physics, School of Engineering Physics, Hanoi University of Science and Technology, 01 Dai Co Viet, Hai Ba Trung, Hanoi, Vietnam;

  • 2. Vietnam Atomic Energy Institute, 59 Ly Thuong Kiet, Hoan Kiem, Hanoi, Vietnam

More Information
  • Received Date: April 29, 2021
  • Revised Date: August 02, 2021
  • Accepted Date: August 28, 2021
  • Available Online: October 16, 2022
  • Published Date: October 03, 2021
    Abstract
  • Cone-beam computed tomography (CBCT), a modern technique with many applications, is becoming increasingly important and essential in many areas of economics and life. CBCT equipment has been commercialized in both hardware and software. However, having a device system suitable for a specific group of objects will be the best. This work used Monte Carlo simulation to optimize the design of a Cone-beam Computed Tomography (CBCT) for lightweight and small-size casts. Factors affecting image quality such as radiation-source, flat-panel detector, and geometrical distance were investigated. Simulation results indicate that an X-ray generator with a focal spot size of 4 × 4 μm2, high voltage of 240 kV, and a radiation intensity of 1013 photon/s with a flat panel CsI (Tl) detector of 0.3 mm thickness and a pixel size of 0.1-0.2 mm, is suitable for a CBCT to inspect small objects in industry. The Monte Carlo model was validated against experiments and to evaluate some characteristics of the existing system.
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    • References (13)
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    • Related Articles
  • Thuy Duong Tran, Ngoc Ha Bui, Kim Tuan Tran, et al. Monte Carlo simulation of a cone-beam CT system for lightweight casts[J]. Radiation Detection Technology and Methods, 2021, 5(4): 504-512. DOI: 10.1007/s41605-021-00279-0
    Citation: Thuy Duong Tran, Ngoc Ha Bui, Kim Tuan Tran, et al. Monte Carlo simulation of a cone-beam CT system for lightweight casts[J]. Radiation Detection Technology and Methods, 2021, 5(4): 504-512. DOI: 10.1007/s41605-021-00279-0
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