생성적 적대 신경망(Generative Adversarial Network)을 이용하여 획득한 18F-FDG Brain PET/CT 인공지능 영상의 비교평가

• 영문명: Comparative Evaluation of 18F-FDG Brain PET/CT AI Images Obtained Using Generative Adversarial Network
• 발행기관: 대한핵의학기술학회
• 저자명: 김종완(Jong-Wan Kim) 김정열(Jung-Yul Kim) 임한상(Han-sang Lim) 김재삼(Jae-sam Kim)
• 간행물 정보: 『핵의학기술』Vol.24 No.1, 15~19쪽, 전체 5쪽
• 주제분류: 의약학 > 방사선과학
• 파일형태: PDF
• 발행일자: 2020.09.30

영문 초록

Purpose Generative Adversarial Network(GAN) is one of deep learning technologies. This is a way to create a real fake image after learning the real image. In this study, after acquiring artificial intelligence images through GAN, We were compared and evaluated with real scan time images. We want to see if these technologies are potentially useful. Materials and Methods 30 patients who underwent 18F-FDG Brain PET/CT scanning at Severance Hospital, were acquired in 15-minute List mode and reconstructed into 1,2,3,4,5 and 15minute images, respectively. 25 out of 30 patients were used as learning images for learning of GAN and 5 patients used as verification images for confirming the learning model. The program was implemented using the Python and Tensorflow frameworks. After learning using the Pix2Pix model of GAN technology, this learning model generated artificial intelligence images. The artificial intelligence image generated in this way were evaluated as Mean Square Error(MSE), Peak Signal to Noise Ratio(PSNR), and Structural Similarity Index(SSIM) with real scan time image. Results The trained model was evaluated with the verification image. As a result, The 15-minute image created by the 5-minute image rather than 1-minute after the start of the scan showed a smaller MSE, and the PSNR and SSIM increased. Conclusion Through this study, it was confirmed that AI imaging technology is applicable. In the future, if these artificial intelligence imaging technologies are applied to nuclear medicine imaging, it will be possible to acquire images even with a short scan time, which can be expected to reduce artifacts caused by patient movement and increase the efficiency of the scanning room.

목차

서 론

실험재료 및 방법

결 과

고찰 및 결론

요 약

Reference

키워드

AI Generative Adverasarial Network(GAN) Pix2Pix