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Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
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Transmission reconstruction algorithm by combining maximum-likelihood expectation maximization and a convolutional

Hui Yang1, Bing Dong1, Weiguo Gu1

  • 1School of Nuclear Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.

Applied Radiation and Isotopes : Including Data, Instrumentation and Methods for Use in Agriculture, Industry and Medicine
|March 27, 2022
PubMed
Summary

A new algorithm combining MLEM and deep learning improves radioactive drum transmission reconstruction. This method significantly reduces artifacts and noise, enhancing accuracy and decreasing measurement time for radioactive waste analysis.

Keywords:
Convolutional neural networkRadioactive waste drumTomographic gamma scanningTransmission reconstruction

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Area of Science:

  • Nuclear Engineering
  • Image Processing
  • Machine Learning

Background:

  • Accurate reconstruction of radioactive drum activity relies on precise tomographic gamma scanning transmission.
  • Traditional algorithms like MLEM often produce grid artifacts and high noise, degrading image detail and increasing reconstruction errors.

Purpose of the Study:

  • To develop a novel algorithm for improved transmission reconstruction in radioactive waste drum analysis.
  • To enhance the accuracy and reduce artifacts in density map and activity reconstructions.

Main Methods:

  • A hybrid algorithm combining Maximum-Likelihood Expectation Maximization (MLEM) with a deep Convolutional Neural Network (CNN).
  • Supervised learning approach for CNN training using ground truth and MLEM-generated image pairs.
  • Evaluation of the algorithm's performance in terms of spatial resolution, artifact removal, and noise robustness.

Main Results:

  • The proposed algorithm significantly improves spatial resolution and effectively removes grid artifacts.
  • Demonstrated robustness with noisy input images; a 20% noise level resulted in a 78.51% decrease in mean squared error.
  • Achieved substantial improvements in peak signal-to-noise ratio (81.19%) and structural similarity index measure (71.74%).

Conclusions:

  • The novel MLEM-CNN algorithm offers a significant advancement in radioactive drum transmission image reconstruction.
  • The method enhances accuracy, reduces noise, and decreases required measurement time.
  • Presents an effective new approach for radioactive waste characterization and monitoring.