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Positron Emission Tomography (PET) is a medical imaging technique that provides crucial insights into the body's physiological functions at a molecular level. It is an indispensable resource for diagnosing, staging, and monitoring various illnesses, notably cancer, neurological disorders, and cardiovascular conditions.
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TextureWGAN: texture preserving WGAN with multitask regularizer for computed tomography inverse problems.

Masaki Ikuta1,2, Jun Zhang1

  • 1University of Wisconsin - Milwaukee, Department of Electrical Engineering and Computer Science, Milwaukee, Wisconsin, United States.

Journal of Medical Imaging (Bellingham, Wash.)
|March 10, 2023
PubMed
Summary

TextureWGAN, a deep learning method, preserves image texture and pixel fidelity in medical imaging. It addresses over-smoothing issues without compromising image quality, outperforming conventional techniques.

Keywords:
computed tomography image reconstructionconvolutional neural networkgenerative adversarial networksinverse image processing problemsmean squared errorstatistical texture analysiswasserstein distance

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Area of Science:

  • Medical Imaging
  • Deep Learning
  • Image Reconstruction

Background:

  • Medical imaging often suffers from over-smoothing in postprocessing, degrading image quality.
  • Preserving image texture while maintaining high pixel fidelity is a significant challenge.

Purpose of the Study:

  • To introduce TextureWGAN, a deep learning method for computed tomography (CT) inverse problems.
  • To address the over-smoothing issue in medical image postprocessing.
  • To preserve image texture without compromising pixel fidelity.

Main Methods:

  • TextureWGAN extends Wasserstein GAN (WGAN) by incorporating a multitask regularizer (MTR).
  • MTR uses mean squared error (MSE) loss and perception loss for pixel fidelity and image quality.
  • Regularization parameters are trained with generator weights to optimize performance.

Main Results:

  • TextureWGAN demonstrated superior texture preservation compared to CNN and NLM.
  • Achieved competitive pixel fidelity performance against CNN and NLM.
  • Evaluated in CT reconstruction, super-resolution, and denoising applications.

Conclusions:

  • TextureWGAN effectively preserves image texture and pixel fidelity.
  • MTR stabilizes training and enhances generator performance.
  • The method offers a solution to over-smoothing in medical imaging.