Journal of Radiation Protection and Research

  • 제44권4호
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  • Pages.149-155
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  • 2019
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  • 2508-1888(pISSN)
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  • 2466-2461(eISSN)
대한방사선방어학회 (The Korean Association for Radiation Protection)
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Preliminary Application of Synthetic Computed Tomography Image Generation from Magnetic Resonance Image Using Deep-Learning in Breast Cancer Patients

  • Jeon, Wan (Department of Radiation Oncology, Dongnam Institute of Radiological and Medical Sciences) ;
  • An, Hyun Joon (Department of Radiation Oncology, Seoul National University Hospital) ;
  • Kim, Jung-in (Department of Radiation Oncology, Seoul National University Hospital) ;
  • Park, Jong Min (Department of Radiation Oncology, Seoul National University Hospital) ;
  • Kim, Hyoungnyoun (GenAI Inc.) ;
  • Shin, Kyung Hwan (Department of Radiation Oncology, Seoul National University Hospital) ;
  • Chie, Eui Kyu (Department of Radiation Oncology, Seoul National University Hospital)
  • 투고 : 2019.05.27
  • 심사 : 2019.12.13
  • 발행 : 2019.12.31
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초록

Background: Magnetic resonance (MR) image guided radiation therapy system, enables real time MR guided radiotherapy (RT) without additional radiation exposure to patients during treatment. However, MR image lacks electron density information required for dose calculation. Image fusion algorithm with deformable registration between MR and computed tomography (CT) was developed to solve this issue. However, delivered dose may be different due to volumetric changes during image registration process. In this respect, synthetic CT generated from the MR image would provide more accurate information required for the real time RT. Materials and Methods: We analyzed 1,209 MR images from 16 patients who underwent MR guided RT. Structures were divided into five tissue types, air, lung, fat, soft tissue and bone, according to the Hounsfield unit of deformed CT. Using the deep learning model (U-NET model), synthetic CT images were generated from the MR images acquired during RT. This synthetic CT images were compared to deformed CT generated using the deformable registration. Pixel-to-pixel match was conducted to compare the synthetic and deformed CT images. Results and Discussion: In two test image sets, average pixel match rate per section was more than 70% (67.9 to 80.3% and 60.1 to 79%; synthetic CT pixel/deformed planning CT pixel) and the average pixel match rate in the entire patient image set was 69.8%. Conclusion: The synthetic CT generated from the MR images were comparable to deformed CT, suggesting possible use for real time RT. Deep learning model may further improve match rate of synthetic CT with larger MR imaging data.

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피인용 문헌

  1. Deep learning methods to generate synthetic CT from MRI in radiotherapy: A literature review vol.89, 2019, https://doi.org/10.1016/j.ejmp.2021.07.027
  2. Deep learning based synthetic‐CT generation in radiotherapy and PET: A review vol.48, pp.11, 2019, https://doi.org/10.1002/mp.15150