Journal of the Korean Society of Radiology (대한영상의학회지)

The Korean Society of Radiology (대한영상의학회)
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Adaptation of Deep Learning Image Reconstruction for Pediatric Head CT: A Focus on the Image Quality

소아용 두부 컴퓨터단층촬영에서 딥러닝 영상 재구성 적용: 영상 품질에 대한 고찰

  • Nim Lee (Department of Radiology, Medical Research Institute, College of Medicine, Ewha Womans University Mokdong Hospital) ;
  • Hyun-Hae Cho (Department of Radiology, Medical Research Institute, College of Medicine, Ewha Womans University Mokdong Hospital) ;
  • So Mi Lee (Department of Radiology, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital) ;
  • Sun Kyoung You (Department of Radiology, Chungnam National University Hospital, Chungnam National University College of Medicine)
  • 이님 (이화여자대학교 의과대학 이화여자대학교 목동병원 영상의학과) ;
  • 조현혜 (이화여자대학교 의과대학 이화여자대학교 목동병원 영상의학과) ;
  • 이소미 (경북대학교 의과대학 칠곡경북대학교병원 영상의학과) ;
  • 유선경 (충남대학교 의과대학 충남대학교병원 영상의학과)
  • Received : 2021.04.28
  • Accepted : 2022.05.27
  • Published : 2023.01.01
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Abstract

Purpose To assess the effect of deep learning image reconstruction (DLIR) for head CT in pediatric patients. Materials and Methods We collected 126 pediatric head CT images, which were reconstructed using filtered back projection, iterative reconstruction using adaptive statistical iterative reconstruction (ASiR)-V, and all three levels of DLIR (TrueFidelity; GE Healthcare). Each image set group was divided into four subgroups according to the patients' ages. Clinical and dose-related data were reviewed. Quantitative parameters, including the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), and qualitative parameters, including noise, gray matter-white matter (GM-WM) differentiation, sharpness, artifact, acceptability, and unfamiliar texture change were evaluated and compared. Results The SNR and CNR of each level in each age group increased among strength levels of DLIR. High-level DLIR showed a significantly improved SNR and CNR (p < 0.05). Sequential reduction of noise, improvement of GM-WM differentiation, and improvement of sharpness was noted among strength levels of DLIR. Those of high-level DLIR showed a similar value as that with ASiR-V. Artifact and acceptability did not show a significant difference among the adapted levels of DLIR. Conclusion Adaptation of high-level DLIR for the pediatric head CT can significantly reduce image noise. Modification is needed while processing artifacts.

목적 소아 환자에서 두부 컴퓨터단층촬영(이하 CT)에 대한 딥러닝 이미지 재구성(deep learning image reconstruction; 이하 DLIR; TrueFidelity; GE Healthcare, Milwaukee, WI, USA)의 효과를 평가하고자 한다. 대상과 방법 총 126개의 소아 두부 CT 이미지를 수집했으며, adaptive statistical iterative reconstruction (이하 ASiR)-V를 사용한 반복적 재구성 및 세 가지 수준의 DLIR을 사용한 재구성을 시행하였다. 각 이미지 세트 그룹은 환자의 연령에 따라 4개의 그룹으로 구분하였으며 각 연령군의 임상 및 방사선량 관련 데이터를 검토하였다. 양적 매개 변수에는 signal to noise ratio (이하 SNR) 및 contrast to noise ratio (이하 CNR)가 포함되었으며 질적 매개 변수로 영상의 잡음(noise), 회백질의 구분 정도, 선명도, 인공물 및 수용 가능성(acceptability), 영상의 질감이 포함되었고 이에 대한 평가와 비교를 시행하였다. 결과 모든 연령 그룹의 모든 수준의 SNR 및 CNR은 높은 수준의 DLIR 사용 시 증가하였다. ASiR-V와 비교했을 때 높은 수준의 DLIR은 SNR 및 CNR이 개선되었다(p < 0.05). 그리고 DLIR의 수준이 증가될수록 순차적인 잡음 감소, 회백질 구분 개선, 선명도 개선이 나타났다. 이러한 변수들에서 높은 수준의 DLIR 사용 시 ASiR-V와 유사한 정도의 수치가 측정되었다. 인공물과 수용 가능성의 경우에 적용된 DLIR 수준 간에 큰 차이를 보이지 않았다. 결론 소아 두부 CT에 고수준 DLIR을 적용하면 영상의 노이즈를 줄일 수 있으나 인공물 처리에 대한 개선이 필요하다.

Keywords

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