Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

A CT and MRI scan to MCNP input conversion program.

Kenneth A Van Riper1

  • 1White Rock Science, P.O. Box 4729, Los Alamos, NM 87544, USA. kvr@rt66.com

Radiation Protection Dosimetry
|December 31, 2005
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Ford Motor Company NDE facility shielding design.

Radiation protection dosimetry·2006
Same author

Application of a sitting MIRD phantom for effective dose calculations.

Radiation protection dosimetry·2006
Same journal

LIST OF REVIEWERS FOR 2025.

Radiation protection dosimetry·2026
Same journal

Development of CaSO4: Dy-based ring badge for extremity dose monitoring of radiation workers in India.

Radiation protection dosimetry·2026
Same journal

A proposal for a differentiated radiation protection program for the decommissioning of nuclear power plants compared to the operation of nuclear power plants.

Radiation protection dosimetry·2026
Same journal

A three-dimensional neutron localization method based on double-scattering imaging and reconstruction algorithm.

Radiation protection dosimetry·2026
Same journal

Effect of 131I biodistribution on measurements using a scanning whole-body counter.

Radiation protection dosimetry·2026
Same journal

Activity concentration of 137Cs and natural radionuclides in soil around the Belarusian nuclear power plant in the pre-commissioning period.

Radiation protection dosimetry·2026
See all related articles

This program automates MCNP input file generation from tomographic scans. It processes images to define geometry and materials, simplifying complex simulations.

Area of Science:

  • Computational physics and engineering
  • Medical imaging and simulation
  • Nuclear engineering and radiation transport

Background:

  • Generating MCNP (Monte Carlo N-Particle) input files from medical imaging data is a complex and time-consuming process.
  • Manual conversion of tomographic scans into detailed geometric and material descriptions for MCNP requires significant expertise and effort.
  • Existing methods often lack efficient image processing capabilities for accurate material assignment and voxelization.

Purpose of the Study:

  • To develop and present a novel software program for automating the creation of MCNP input files from tomographic scan sequences.
  • To integrate advanced image processing techniques for enhanced material identification and assignment within the MCNP simulation environment.
  • To streamline the workflow for converting medical imaging data into radiation transport simulations.

Related Experiment Videos

Main Methods:

  • The program reads tomographic scan data and employs image processing techniques, including contrast adjustment and grayscale-to-color mapping.
  • A user interface allows association of image intensity ranges with MCNP materials from a library, with special handling for boundary pixels.
  • Material fractions are computed within a user-defined voxel grid, enabling the creation of new mixed materials and defining geometry as MCNP lattices or cells.

Main Results:

  • Successful generation of MCNP geometry and material sections directly from tomographic scan data.
  • Automated material assignment based on pixel intensity, including boundary-specific assignments.
  • Computation of material fractions and creation of new mixed materials for detailed simulation.
  • Flexible output options for MCNP geometry (lattice or individual cells) with an algorithm to merge adjacent cells of the same material.

Conclusions:

  • The developed program significantly simplifies and automates the process of preparing MCNP input files from tomographic scans.
  • The integration of image processing and material assignment tools enhances the accuracy and efficiency of creating simulation models.
  • This approach facilitates more accessible and robust use of MCNP for radiation transport simulations based on medical imaging data.