Journal of radiological science and technology (대한방사선기술학회지:방사선기술과학)

Korean Society of Radiological Science (대한방사선과학회)
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A Study on the Application of Deep Learning Model by Using ACR Phantom in CT Quality Control

CT 정도관리에서 ACR 팬텀을 이용한 딥러닝 모델 적용에 관한 연구

  • Eun-Been Choi (Department of Radiological Science, Eulji University) ;
  • Si-On Kim (Department of Radiological Science, Eulji University) ;
  • Seung-Won Choi (Department of Radiological Science, Eulji University) ;
  • Jae-Hee Kim (Department of Radiological Science, Eulji University) ;
  • Young-Kyun Kim (Department of Radiology, Samsung Medical Center) ;
  • Dong-Kyun Han (Department of Radiological Science, Eulji University)
  • 최은빈 (을지대학교 방사선학과) ;
  • 김시온 (을지대학교 방사선학과) ;
  • 최승원 (을지대학교 방사선학과) ;
  • 김재희 (을지대학교 방사선학과) ;
  • 김영균 (삼성서울병원 영상의학과 ) ;
  • 한동균 (을지대학교 방사선학과)
  • Received : 2023.11.17
  • Accepted : 2023.12.05
  • Published : 2023.12.31
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Abstract

This study aimed to implement a deep learning model that can perform quantitative quality control through ACTS software used for quantitative evaluation of ACR phantom in CT quality control and evaluate its usefulness. By changing the scanning conditions, images of three modules of the ACR phantom's slice thickness (ST), low contrast resolution (LC), and high contrast resolution (HC) were obtained and classified as ACTS software. The deep learning model used ResNet18, implementing three models in which ST, HC, and LC were learned with epoch 50 and an integrated model in which three modules were learned with Epoch 10, 30, and 50 at once. The performance of each model was evaluated through Accuracy and Loss. When comparing and evaluating the accuracy and loss function values of the deep learning models by ST, LC, and HC modules, the Accuracy and Loss of the HC model were the best with 100% and 0.0081, and in the integrated model according to the Epoch value, Accuracy and Loss with epoch 50 were the best with 96.29% and 0.1856. This paper showed that quantitative quality control is possible through a deep learning model, and it can be used as a basis and evidence for applying deep learning to the CT quality control.

Keywords

Acknowledgement

This research was supported by 2023 Eulji University Innovation Support Project grant funded.

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