Seq2Morph: A deep learning deformable image registration algorithm for longitudinal imaging studies and adaptive radiotherapy.

Journal: Medical physics
Published Date: Nov 10, 2022

Abstract

PURPOSE: To simultaneously register all the longitudinal images acquired in a radiotherapy course for analyzing patients' anatomy changes for adaptive radiotherapy (ART).

Authors

  • Donghoon Lee
    Department of Radiation Convergence Engineering, Research Institute of Health Science, Yonsei Univeristy, 1 Yonseidae-gil, Wonju, Gangwon, 26493, Korea.
  • Sadegh Alam
    Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, USA.
  • Jue Jiang
    Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA.
  • Laura Cervino
    Center for Advanced Radiotherapy Technologies and Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California 92037.
  • Yu-Chi Hu
    Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, USA; Department of Radiation Oncology, Memorial Sloan-Kettering Cancer Center, New York, USA.
  • Pengpeng Zhang
    Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, USA; Department of Radiation Oncology, Memorial Sloan-Kettering Cancer Center, New York, USA. Electronic address: zhangp@mskcc.org.

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