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Accelerating Neutron Tomography experiments through Artificial Neural Network based reconstruction.

Davide Micieli1,2, Triestino Minniti3, Llion Marc Evans4,5

  • 1Università della Calabria, Dipartimento di Fisica, Arcavacata di Rende (Cosenza), 87036, Italy. davide.micieli@unical.it.

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|February 23, 2019
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Summary
This summary is machine-generated.

Neural Network Filtered Back-Projection (NN-FBP) offers a faster solution for Neutron Tomography (NT) image reconstruction. This machine learning approach significantly reduces scanning and processing times while maintaining high image quality, even with fewer projections.

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Area of Science:

  • Materials Science and Engineering
  • Non-destructive Testing
  • Computational Imaging

Background:

  • Neutron Tomography (NT) is a powerful non-destructive imaging technique for internal structure analysis.
  • Current NT methods are time-consuming, limiting their use for quality control of multiple similar objects.
  • Reducing projection numbers speeds up acquisition but degrades image quality with conventional methods.

Purpose of the Study:

  • To introduce and evaluate the Neural Network Filtered Back-Projection (NN-FBP) method for optimizing Neutron Tomography.
  • To assess the performance of NN-FBP in reducing acquisition and reconstruction times for NT.
  • To demonstrate the efficacy of a machine learning algorithm in NT image reconstruction.

Main Methods:

  • Utilized simulated and real neutron data for method assessment.
  • Evaluated the NN-FBP method across varying numbers of projections.
  • Compared NN-FBP performance against conventional NT reconstruction algorithms.

Main Results:

  • NN-FBP successfully reduced both acquisition and reconstruction times in Neutron Tomography.
  • The method demonstrated superior performance compared to traditional reconstruction techniques.
  • High image quality was achieved even with a limited number of projections.

Conclusions:

  • NN-FBP is a viable machine learning-based solution for efficient NT image reconstruction.
  • This approach significantly improves time efficiency in NT experiments.
  • NN-FBP enables high-quality imaging for large-scale quality checking studies in NT.