Multimodality image registration in the head-and-neck using a deep learning-derived synthetic CT as a bridge.

Journal: Medical physics
Published Date: Jan 2, 2020

Abstract

PURPOSE: To develop and demonstrate the efficacy of a novel head-and-neck multimodality image registration technique using deep-learning-based cross-modality synthesis.

Authors

  • Elizabeth M McKenzie
    Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90024, USA.
  • Anand Santhanam
    Department of Radiation Oncology, UCLA, 200 Medical Plaza, Suite B265, Los Angeles, CA, 90095, USA.
  • Dan Ruan
    Departments of Radiation Oncology, Biomedical Physics and Bioengineering, UCLA, Los Angeles, CA, 90095, USA.
  • Daniel O'Connor
    Department of Radiation Oncology, University of California, Los Angeles, CA, 90095, USA.
  • Minsong Cao
    Department of Radiation Oncology, University of California-Los Angeles, Los Angeles, CA, 90095, USA.
  • Ke Sheng
    Department of Radiation Oncology, University of California-Los Angeles, Los Angeles, CA, 90095, USA.

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