Earticle

초록

영어

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. Pixelto- 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.

목차

ABSTRACT
Introduction
Materials and Methods
Patient’s Characteristics
Deep learning based on U-net model
Results and Discussion
Conclusion
Acknowledgements
References

키워드

저자

• Wan Jeon [ Department of Radiation Oncology, Dongnam Institute of Radiological and Medical Sciences, Busan, Korea ]
• Hyun Joon An [ Department of Radiation Oncology, Seoul National University Hospital, Seoul, Korea ]
• Jung-in Kim [ Department of Radiation Oncology, Seoul National University Hospital, Seoul, Korea; Institute of Radiation Medicine, Medical Research Center, Seoul National University, Seoul, Korea ]
• Jong Min Park [ Department of Radiation Oncology, Seoul National University Hospital, Seoul, Korea; Institute of Radiation Medicine, Medical Research Center, Seoul National University, Seoul, Korea ]
• Hyoungnyoun Kim [ 4GenAI Inc., Seoul, Korea ]
• Kyung Hwan Shin [ Department of Radiation Oncology, Seoul National University Hospital, Seoul, Korea; Institute of Radiation Medicine, Medical Research Center, Seoul National University, Seoul, Korea; Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, Korea. ]
• Eui Kyu Chie [ Department of Radiation Oncology, Seoul National University Hospital, Seoul, Korea; Institute of Radiation Medicine, Medical Research Center, Seoul National University, Seoul, Korea; Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, Korea. ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  방사선방어학회지 [Journal of Radiation Protection and Research]

• 간기

  계간

• pISSN

  2508-1888

• 수록기간

  1976~2026

• 등재여부

  KCI 등재,SCOPUS

• 십진분류

  KDC 559 DDC 629