Improved Tumor Image Estimation in X-Ray Fluoroscopic Images by Augmenting 4DCT Data for Radiotherapy

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  • Shinohara Takumi
    Graduate School of Biomedical Engineering, Tohoku University
  • Ichiji Kei
    Tohoku University Graduate School of Medicine
  • Wang Jiaoyang
    Graduate School of Biomedical Engineering, Tohoku University
  • Homma Noriyasu
    Graduate School of Biomedical Engineering, Tohoku University Tohoku University Graduate School of Medicine
  • Zhang Xiaoyong
    Tohoku University Graduate School of Medicine National Institute of Technology, Sendai College
  • Sugita Norihiro
    Graduate School of Engineering, Tohoku University
  • Yoshizawa Makoto
    Center for Promotion of Innovation Strategy, Tohoku University

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<p>Measurement of tumor position is important for the radiotherapy of lung tumors with respiratory motion. Although tumors can be observed using X-ray fluoroscopy during radiotherapy, it is often difficult to measure tumor position from X-ray image sequences accurately because of overlapping organs. To measure tumor position accurately, a method for extracting tumor intensities from X-ray image sequences using a hidden Markov model (HMM) has been proposed. However, the performance of tumor intensity extraction depends on limited knowledge regarding the tumor motion observed in the four-dimensional computed tomography (4DCT) data used to construct the HMM. In this study, we attempted to improve the performance of tumor intensity extraction by augmenting 4DCT data. The proposed method was tested using simulated datasets of X-ray image sequences. The experimental results indicated that the HMM using the augmentation method could improve tumor-tracking performance when the range of tumor movement during treatment differed from that in the 4DCT data.</p>

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