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Neutron Radiography and Computed Tomography of Biological Systems at the Oak Ridge National Laboratory's High Flux Isotope Reactor

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Statistical reconstruction for cosmic ray muon tomography.

Larry J Schultz¹, Gary S Blanpied, Konstantin N Borozdin

¹Los Alamos National Laboratory, Los Alamos, NM 87545, USA. schultz@lanl.gov

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|August 11, 2007

Summary

Cosmic ray muons enable non-invasive inspection by exploiting particle scattering. A new tomographic reconstruction algorithm improves image quality for this radiation-free technique.

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Area of Science:

Particle Physics
Applied Physics
Imaging Science

Background:

Cosmic ray muons are ubiquitous, penetrating particles.
Existing methods for muon-based imaging use heuristic algorithms.
Nondestructive inspection often relies on artificial radiation sources.

Purpose of the Study:

To develop a novel tomographic reconstruction algorithm for muon-based imaging.
To adapt medical imaging techniques for cosmic ray muon scattering data.
To improve the accuracy and robustness of muon tomography.

Main Methods:

Developed a maximum likelihood/expectation maximization (MLEM) algorithm.
Modeled the statistics of multiple Coulomb scattering for muons.
Utilized simulated data to test and validate the algorithm.

Main Results:

The MLEM algorithm provides a robust framework for muon tomography.
Simulated examples demonstrate the potential for high-resolution imaging.
The algorithm accounts for the unique statistical properties of muon scattering.

Conclusions:

The proposed MLEM algorithm advances nondestructive inspection using cosmic ray muons.
This radiation-free technique offers a promising alternative to traditional methods.
Further work will focus on improving algorithm robustness against experimental noise.