Using neural networks to extend cropped medical images for deformable registration among images with differing scan extents.

Journal: Medical physics
Published Date: Jul 11, 2021

Abstract

PURPOSE: Missing or discrepant imaging volume is a common challenge in deformable image registration (DIR). To minimize the adverse impact, we train a neural network to synthesize cropped portions of head and neck CT's and then test its use in DIR.

Authors

  • Elizabeth M McKenzie
    Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90024, USA.
  • Nuo Tong
    Key Lab of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi'an, Shaanxi, 710071, China.
  • Dan Ruan
    Departments of Radiation Oncology, Biomedical Physics and Bioengineering, UCLA, Los Angeles, CA, 90095, USA.
  • Minsong Cao
    Department of Radiation Oncology, University of California-Los Angeles, Los Angeles, CA, 90095, USA.
  • Robert K Chin
    Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
  • Ke Sheng
    Department of Radiation Oncology, University of California-Los Angeles, Los Angeles, CA, 90095, USA.

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