Performance of deep learning synthetic CTs for MR-only brain radiation therapy.

Journal: Journal of applied clinical medical physics
Published Date: Jan 7, 2021

Abstract

PURPOSE: To evaluate the dosimetric and image-guided radiation therapy (IGRT) performance of a novel generative adversarial network (GAN) generated synthetic CT (synCT) in the brain and compare its performance for clinical use including conventional brain radiotherapy, cranial stereotactic radiosurgery (SRS), planar, and volumetric IGRT.

Authors

  • Xiaoning Liu
    Department of Medical Physics, Memorial Sloan Kettering Cancer Center, Middletown, NJ, USA.
  • Hajar Emami
    Department of Computer Science, Wayne State University, Detroit, MI, USA.
  • Siamak P Nejad-Davarani
    Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.
  • Eric Morris
    Department of Radiation Oncology, University of California-Los Angeles, Los Angeles, CA, USA.
  • Lonni Schultz
    Department of Public Health Sciences, Henry Ford Health System, Detroit, MI, USA.
  • Ming Dong
    Department of Computer Science, Wayne State University.
  • Carri K Glide-Hurst
    Department of Human Oncology, School of Medicine and Public Heath, University of Wisconsin - Madison, Madison, WI, USA.

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