Journal of the Korean Society of Radiology (대한영상의학회지)

The Korean Society of Radiology (대한영상의학회)
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Radiation Dose Reduction in Digital Mammography by Deep-Learning Algorithm Image Reconstruction: A Preliminary Study

딥러닝 알고리즘을 이용한 저선량 디지털 유방 촬영 영상의 복원: 예비 연구

  • Su Min Ha (Department of Radiology, Research Institute of Radiology, Chung-Ang University Hospital, Chung-Ang University College of Medicine) ;
  • Hak Hee Kim (Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Eunhee Kang (Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST)) ;
  • Bo Kyoung Seo (Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine) ;
  • Nami Choi (Department of Radiology, Konkuk University Medical Center, Konkuk University School of Medicine) ;
  • Tae Hee Kim (Department of Radiology, Ajou University Hospital, Ajou University School of Medicine) ;
  • You Jin Ku (Department of Radiology, Catholic Kwangdong University International St. Mary's Hospital, Catholic Kwandong University) ;
  • Jong Chul Ye (Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST))
  • 하수민 (중앙대학교 의과대학 중앙대학교병원 영상의학과) ;
  • 김학희 (울산대학교 의과대학 서울아산병원 영상의학과) ;
  • 강은희 (한국과학기술원 바이오 및 뇌공학과) ;
  • 서보경 (고려대학교 의과대학 고려대학교 안산병원 영상의학과) ;
  • 최나미 (건국대학교 의과대학 건국대학교병원 영상의학과) ;
  • 김태희 (아주대학교 의과대학 아주대학교병원 영상의학과) ;
  • 구유진 (가톨릭관동대학교 국제성모병원 영상의학과) ;
  • 예종철 (한국과학기술원 바이오 및 뇌공학과)
  • Received : 2020.08.19
  • Accepted : 2021.07.23
  • Published : 2022.03.01
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Abstract

Purpose To develop a denoising convolutional neural network-based image processing technique and investigate its efficacy in diagnosing breast cancer using low-dose mammography imaging. Materials and Methods A total of 6 breast radiologists were included in this prospective study. All radiologists independently evaluated low-dose images for lesion detection and rated them for diagnostic quality using a qualitative scale. After application of the denoising network, the same radiologists evaluated lesion detectability and image quality. For clinical application, a consensus on lesion type and localization on preoperative mammographic examinations of breast cancer patients was reached after discussion. Thereafter, coded low-dose, reconstructed full-dose, and full-dose images were presented and assessed in a random order. Results Lesions on 40% reconstructed full-dose images were better perceived when compared with low-dose images of mastectomy specimens as a reference. In clinical application, as compared to 40% reconstructed images, higher values were given on full-dose images for resolution (p < 0.001); diagnostic quality for calcifications (p < 0.001); and for masses, asymmetry, or architectural distortion (p = 0.037). The 40% reconstructed images showed comparable values to 100% full-dose images for overall quality (p = 0.547), lesion visibility (p = 0.120), and contrast (p = 0.083), without significant differences. Conclusion Effective denoising and image reconstruction processing techniques can enable breast cancer diagnosis with substantial radiation dose reduction.

목적 깊은 컨볼루션 신경망 기법을 결합한 영상 잡음 제거 알고리즘을 개발하고 이를 응용하여 저선량 유방 촬영 영상으로 유방암을 진단하는 데 그 효능을 조사하고자 한다. 대상과 방법 6명의 유방 영상 전문의가 전향적 연구에 참여하였다. 모든 영상 전문의는 병변 감지를 위해 저선량 영상을 독립적으로 평가하고 정성적 척도를 사용하여 진단 품질을 평가하였다. 영상 잡음 제거 알고리즘을 적용한 후, 동일한 영상 전문의가 병변 감지 가능성과 영상 품질에 대한 평가를 하였다. 임상 적용을 위해 동일한 영상 전문의가 병변 유형과 위치에 대한 합의 결정 후, 저선량 영상, 재구성된 영상, 기존 선량 영상을 무작위 순서로 제시하여 평가하였다. 결과 전 절제 표본의 저선량 영상을 참조로 40% 재구성된 영상에서 병변이 더 잘 인식되었다. 임상 적용단계에서 40% 재구성된 영상과 비교하여, 기존 선량 영상이 해상도(p < 0.001), 석회에 대한 진단 품질(p < 0.001), 유방 종괴, 비대칭, 구조왜곡의 진단 품질(p = 0.037)에 대해 더 높은 평균값을 보였다. 40% 재구성된 영상은 100% 영상과 비교 시 전반적 화질(p = 0.547), 병변의 가시성(p = 0.120), 대조도(p = 0.083)에서 비슷한 성적을 보였으며 유의미한 차이도 보이지 않았다. 결론 깊은 컨볼루션 신경망 기법을 결합한 효과적인 잡음 제거 및 영상 재구성 처리 알고리즘은 유방 촬영의 상당한 선량 감소를 위한 길을 열어 유방암 진단을 가능하게 할 것이다.

Keywords

Acknowledgement

This research was supported by Korean Society of Breast Imaging & Korean Society for Breast Screening (KSBI&KSFBS-2017-03).

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