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Area of Science:

  • Engineering
  • Computer Science
  • Medical Imaging

Background:

  • Industrial tomography applications require robust image reconstruction algorithms.
  • Existing methods for electrical impedance tomography (EIT) and ultrasound transmission tomography (UST) face challenges with complex object configurations.

Purpose of the Study:

  • To develop and evaluate a refined machine learning algorithm for industrial tomography.
  • To improve image reconstruction accuracy in EIT and UST using logistic regression.

Main Methods:

  • A novel pixel-by-pixel logistic regression approach was implemented for EIT and UST.
  • The elastic net method was employed to reduce predictor variables in logistic regression.
  • Image reconstruction quality was assessed using compatibility ratio (CR) and relative error (RE).

Main Results:

  • The developed algorithm demonstrated insensitivity to object shape, number, and position.
  • The pixel-by-pixel reconstruction transformed an under-determined problem into an over-determined one.
  • The approach showed promise for accurate image reconstruction in EIT and UST.

Conclusions:

  • Logistic regression, enhanced by the elastic net method, is a viable tool for industrial tomography image reconstruction.
  • The pixel-by-pixel approach offers a robust solution for EIT and UST, adaptable to various scenarios.
  • This research contributes to advancing machine learning applications in tomographic imaging.