Deep Learning-Based Synthetic Computed Tomography for Low-Field Brain Magnetic Resonance-Guided Radiation Therapy.

Journal: International journal of radiation oncology, biology, physics
PMID: 39357787

Abstract

PURPOSE: Magnetic resonance (MR)-guided radiation therapy enables online adaptation to address intra- and interfractional changes. To address the need of high-fidelity synthetic computed tomography (synCT) required for dose calculation, we developed a conditional generative adversarial network for synCT generation from low-field MR imaging in the brain.

Authors

  • Yuhao Yan
    Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China.
  • Joshua P Kim
    Department of Radiation Oncology, Henry Ford Health, Detroit, Michigan.
  • Siamak P Nejad-Davarani
    Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.
  • Ming Dong
    Department of Computer Science, Wayne State University.
  • Newton J Hurst
    Department of Human Oncology, University of Wisconsin-Madison, Madison, Wisconsin.
  • Jiwei Zhao
    Water Conservancy College, North China University of Water Resources and Electric Power, Zhengzhou 450046, China E-mail: 1173434259@qq.com.
  • Carri K Glide-Hurst
    Department of Human Oncology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53792, USA.

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