A convolutional neural network for ultra-low-dose CT denoising and emphysema screening.

Journal: Medical physics
Published Date: Jul 17, 2019

Abstract

PURPOSE: Reducing dose level to achieve ALARA is an important task in diagnostic and therapeutic applications of computed tomography (CT) imaging. Effective image quality enhancement strategies are crucial to compensate for the degradation caused by dose reduction. In the past few years, deep learning approaches have demonstrated promising denoising performance on natural/synthetic images. This study tailors a neural network model for (ultra-)low-dose CT denoising, and assesses its performance in enhancing CT image quality and emphysema quantification.

Authors

  • Tingting Zhao
    School of Software Engineering, Beihang University, Beijing, China.
  • Michael McNitt-Gray
    Departments of Biomedical Physics and Radiology, University of California, Los Angeles, CA, 90095, USA.
  • Dan Ruan
    Departments of Radiation Oncology, Biomedical Physics and Bioengineering, UCLA, Los Angeles, CA, 90095, USA.

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