A deep learning- and partial least square regression-based model observer for a low-contrast lesion detection task in CT.

Journal: Medical physics
Published Date: Apr 1, 2019

Abstract

PURPOSE: This work aims to develop a new framework of image quality assessment using deep learning-based model observer (DL-MO) and to validate it in a low-contrast lesion detection task that involves CT images with patient anatomical background.

Authors

  • Hao Gong
    Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA.
  • Lifeng Yu
    Hithink RoyalFlush Information Network Co., Ltd., Hangzhou 310023, China. yulifeng@myhexin.com.
  • Shuai Leng
    Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA.
  • Samantha K Dilger
    Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA.
  • Liqiang Ren
    Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA.
  • Wei Zhou
    Department of Eye Function Laboratory, Eye Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
  • Joel G Fletcher
    Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA.
  • Cynthia H McCollough
    Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA.

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