자기 지도 학습훈련 기반의 Noise2Void 네트워크를 이용한 PET 영상의 잡음 제거 평가

• 영문명: The Evaluation of Denoising PET Image Using Self Supervised Noise2Void Learning Training: A Phantom Study
• 발행기관: 대한방사선과학회(구 대한방사선기술학회)
• 저자명: 윤석환(Seokhwan Yoon) 박찬록(Chanrok Park)
• 간행물 정보: 『방사선기술과학』방사선기술과학 제44권 제6호, 655~661쪽, 전체 7쪽
• 주제분류: 의약학 > 방사선과학
• 파일형태: PDF
• 발행일자: 2021.12.30

영문 초록

Positron emission tomography (PET) images is affected by acquisition time, short acquisition times results in low gamma counts leading to degradation of image quality by statistical noise. Noise2Void(N2V) is self supervised denoising model that is convolutional neural network (CNN) based deep learning. The purpose of this study is to evaluate denoising performance of N2V for PET image with a short acquisition time. The phantom was scanned as a list mode for 10 min using Biograph mCT40 of PET/CT (Siemens Healthcare, Erlangen, Germany). We compared PET images using NEMA image-quality phantom for standard acquisition time (10 min), short acquisition time (2min) and simulated PET image (S2 min). To evaluate performance of N2V, the peak signal to noise ratio (PSNR), normalized root mean square error (NRMSE), structural similarity index (SSIM) and radio-activity recovery coefficient (RC) were used. The PSNR, NRMSE and SSIM for 2 min and S2 min PET images compared to 10min PET image were 30.983, 33.936, 9.954, 7.609 and 0.916, 0.934 respectively. The RC for spheres with S2 min PET image also met European Association of Nuclear Medicine Research Ltd. (EARL) FDG PET accreditation program. We confirmed generated S2 min PET image from N2V deep learning showed improvement results compared to 2 min PET image and The PET images on visual analysis were also comparable between 10 min and S2 min PET images. In conclusion, noisy PET image by means of short acquisition time using N2V denoising network model can be improved image quality without underestimation of radioactivity.

목차

Ⅰ. 서론

Ⅱ. 대상 및 방법

Ⅲ. 결과

Ⅳ. 고찰

Ⅴ. 결론

키워드

합성 곱 신경망 자기 지도 학습훈련 영상 잡음 제거 Noise2Void 양전자방출단층촬영 Convolutional neural network Self-supervised learning training Image denoising Noise2Void PET