Deep Learning-Based Image Noise Quantification Framework for Computed Tomography.

Journal: Journal of computer assisted tomography
Published Date: Jun 30, 2023

Abstract

OBJECTIVE: Noise quantification is fundamental to computed tomography (CT) image quality assessment and protocol optimization. This study proposes a deep learning-based framework, Single-scan Image Local Variance EstimatoR (SILVER), for estimating the local noise level within each region of a CT image. The local noise level will be referred to as a pixel-wise noise map.

Authors

  • Nathan R Huber
    From the Department of Radiology, Mayo Clinic, Rochester, MN.
  • Jiwoo Kim
    Department of Chemical and Biological Engineering, Korea University, Seoul, 02841, South Korea. bong98@korea.ac.kr.
  • Shuai Leng
    Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA.
  • Cynthia H McCollough
    Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA.
  • Lifeng Yu
    Hithink RoyalFlush Information Network Co., Ltd., Hangzhou 310023, China. yulifeng@myhexin.com.

Keywords

External Resources

Popular Topics
Recent Journals