Attention-aware fully convolutional neural network with convolutional long short-term memory network for ultrasound-based motion tracking.

Journal: Medical physics
Published Date: Apr 22, 2019

Abstract

PURPOSE: One of the promising options for motion management in radiation therapy (RT) is the use of LINAC-compatible robotic-arm-mounted ultrasound imaging system due to its high soft tissue contrast, real-time capability, absence of ionizing radiation, and low cost. The purpose of this work is to develop a novel deep learning-based real-time motion tracking strategy for ultrasound image-guided RT.

Authors

  • Pu Huang
    Department of Obstetrics & Gynecology, the First Affiliated Hospital of Xi'an Jiaotong University, Xian, Shaanxi, China.
  • Gang Yu
    The Children's Hospital, Zhejiang University School of Medicine, Hangzhou, China.
  • Hua Lu
    Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan, Shandong, 250358, China.
  • Danhua Liu
    Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan, Shandong, 250358, China.
  • Ligang Xing
    Department of Radiation Oncology, Shandong Cancer Hospital, Jinan, Shandong, 250117, China.
  • Yong Yin
    Department of Radiation Oncology, Shandong Cancer Hospital, Jinan, Shandong, 250117, China.
  • Nataliya Kovalchuk
    Department of Radiation Oncology, Stanford University, 875 Blake Wilbur Drive, Stanford, CA, 94305-5847, USA.
  • Lei Xing
    Department of Radiation Oncology, Stanford University, CA, USA.
  • Dengwang Li
    Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan, China.

Keywords

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