Deep learning-based motion compensation for four-dimensional cone-beam computed tomography (4D-CBCT) reconstruction.

Journal: Medical physics
PMID: 36412165

Abstract

BACKGROUND: Motion-compensated (MoCo) reconstruction shows great promise in improving four-dimensional cone-beam computed tomography (4D-CBCT) image quality. MoCo reconstruction for a 4D-CBCT could be more accurate using motion information at the CBCT imaging time than that obtained from previous 4D-CT scans. However, such data-driven approaches are hampered by the quality of initial 4D-CBCT images used for motion modeling.

Authors

  • Zhehao Zhang
    Department of Radiation Oncology, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA.
  • Jiaming Liu
    Department of Electrical and Systems Engineering, University in St. Louis, St. Louis, MO, USA.
  • Deshan Yang
    Department of Radiation Oncology, Washington University School of Medicine, 4921 Parkview Place, Campus Box 8224, St. Louis, MO, 63110, USA.
  • Ulugbek S Kamilov
    Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, MO, USA.
  • Geoffrey D Hugo
    Department of Radiation Oncology, Washington University School of Medicine, St. Louis, 63110, MO, USA.

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