CT 정도관리에서 ACR 팬텀을 이용한 딥러닝 모델 적용에 관한 연구

• 영문명: A Study on the Application of Deep Learning Model by Using ACR Phantom in CT Quality Control
• 발행기관: 대한방사선과학회(구 대한방사선기술학회)
• 저자명: 최은빈(Eun-Been Choi) 김시온(Si-On Kim) 최승원(Seung-Won Choi) 김재희(Jae-Hee Kim) 김영균(Young-Kyun Kim) 한동균(Dong-Kyun Han)
• 간행물 정보: 『방사선기술과학』제46권 제6호, 535~542쪽, 전체 8쪽
• 주제분류: 의약학 > 방사선과학
• 파일형태: PDF
• 발행일자: 2023.12.31

영문 초록

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.

목차

Ⅰ. 서 론

Ⅱ. 대상 및 방법

Ⅲ. 결 과

Ⅳ. 고 찰

Ⅴ. 결 론

REFERENCES

키워드

딥러닝 컴퓨터단층촬영 ACR 팬텀 정도관리 Deep learning ResNet18 Computed tomography ACR phantom Quality control