Projection-domain scatter correction for cone beam computed tomography using a residual convolutional neural network.

Journal: Medical physics
Published Date: Jun 5, 2019

Abstract

PURPOSE: Scatter is a major factor degrading the image quality of cone beam computed tomography (CBCT). Conventional scatter correction strategies require handcrafted analytical models with ad hoc assumptions, which often leads to less accurate scatter removal. This study aims to develop an effective scatter correction method using a residual convolutional neural network (CNN).

Authors

  • Yusuke Nomura
    Department of Radiation Oncology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, 060-8638, Japan.
  • Qiong Xu
    Global Station for Quantum Medical Science and Engineering, Global Institution for Collaborative Research and Education (GI-CoRE), Hokkaido University, Sapporo, 060-8648, Japan.
  • Hiroki Shirato
    Department of Radiation Medicine, Hokkaido University Graduate School of Medicine, North 15 West 7 Kita-ku, Sapporo, Hokkaido, 060-8638, Japan.
  • Shinichi Shimizu
    Department of Carbon Ion Radiotherapy, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.
  • Lei Xing
    Department of Radiation Oncology, Stanford University, CA, USA.

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