Reduction of Unsharpness Caused by Patient Motion on Radiography using Deep Learning

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  • Eiichiro Okumura
    Department of Radiological Technology, Faculty of Health Sciences, Tsukuba International University
  • Suzuki Nobutada
    Department of Radiology, Eastern Chiba Medical Center
  • Okumura Erika
    Department of Radiological Technology, Faculty of Health Sciences, Tsukuba International University
  • Kitamura Shigemi
    Department of Radiological Technology, Faculty of Health Sciences, Tsukuba International University
  • Muranaka Hiroyuki
    Department of Health Sciences, School of Health Sciences, Nippon Bunri University
  • Ishida Takayuki
    Division of Health Sciences, Graduate School of Medicine, Osaka University

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  • 深層学習を用いた単純X線撮影における患者体動による不鋭の低減

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<p>In radiographic examination, in case of the patient’s body movement or incomplete breath-holding, radiography was retaken again. Therefore, we investigated in U-net, Cycle-GAN, and UNIT, and developed a method for remove blur on radiographs with higher accuracy. The database used in this study consists of 39 cases of pair of radiography (blurred image and reference image) at the Eastern Chiba Medical Center, in which the radiography was retaken due to body movement or incomplete breath-holding. Next, for data augmentation, the number of training images was increased by rotation and inversion, the blurred image was input to U-net, Cycle-GAN and UNIT to generate no-blurred image. Comparing with the reference image, four radiological technologists performed visual evaluation of the test images on 5-point scale. Blurred score, retention of anatomy, contrast change on visual evaluation in UNIT was higher than this in U-net and Cycle-GAN, and there was a statistically significant difference (P<0.01). In the future, it will be necessary to increase the number of cases and further improve the accuracy.</p>

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