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정진호Jin Ho Jung
서강대학교
Clin. Nucl. Med., Nov 01 2024, 49 (11) | https://doi.org/10.1097/RLU.0000000000005441
Cross-Modality Image Translation From Brain 18 F-FDG PET/CT Images to Fluid-Attenuated Inversion Recovery Images Using the CypixGAN Framework
Lee, Sangwon PhD∗; Jung, Jin Ho PhD∗; Choi, Yong PhD∗; Seok, Eunyeong MS∗; Jung, Jiwoong PhD∗; Lim, Hyunkeong PhD†; Kim, Dongwoo MD†,‡; Yun, Mijin MD, PhD†
From the ∗Department of Electronic Engineering, Sogang University, Seoul, Republic of Korea
†Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
‡Department of Nuclear Medicine, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang, Republic of Korea.
S.L. and J.H.J. contributed equally to this work and should be considered co-first authors.
Correspondence to: Mijin Yun, MD, PhD, Yong Choi, PhD
From the ∗Department of Electronic Engineering, Sogang University, Seoul, Republic of Korea
†Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
‡Department of Nuclear Medicine, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang, Republic of Korea.
S.L. and J.H.J. contributed equally to this work and should be considered co-first authors.
Correspondence to: Mijin Yun, MD, PhD, Yong Choi, PhD
Abstract
Purpose
PET/CT and MRI can accurately diagnose dementia but are expensive and inconvenient for patients. Therefore, we aimed to generate synthetic fluid-attenuated inversion recovery (FLAIR) images from 18F-FDG PET and CT images of the human brain using a generative adversarial network (GAN)–based deep learning framework called the CypixGAN, which combined the CycleGAN framework with the L1 loss function of the pix2pix.
Patients and Methods
Data from 143 patients who underwent PET/CT and MRI were used for training (n = 79), validation (n = 20), and testing (n = 44) the deep learning frameworks. Synthetic FLAIR images were generated using the pix2pix, CycleGAN, and CypixGAN, and white matter hyperintensities (WMHs) were then segmented. The performance of CypixGAN was compared with that of the other frameworks.
Results
The CypixGAN outperformed the pix2pix and CycleGAN in generating synthetic FLAIR images with superior visual quality. Peak signal-to-noise ratio and structural similarity index (mean ± standard deviation) estimated using the CypixGAN (20.23 ± 1.31 and 0.80 ± 0.02, respectively) were significantly higher than those estimated using the pix2pix (19.35 ± 1.43 and 0.79 ± 0.02, respectively) and CycleGAN (18.74 ± 1.49 and 0.78 ± 0.02, respectively) (P < 0.001). WMHs in synthetic FLAIR images generated using the CypixGAN closely resembled those in ground-truth images, as indicated by the low absolute percentage volume differences and high dice similarity coefficients.
Conclusions
The CypixGAN generated high-quality FLAIR images owing to the preservation of spatial information despite using unpaired images. This framework may help improve diagnostic performance and cost-effectiveness of PET/CT when MRI scan is unavailable.
PET/CT and MRI can accurately diagnose dementia but are expensive and inconvenient for patients. Therefore, we aimed to generate synthetic fluid-attenuated inversion recovery (FLAIR) images from 18F-FDG PET and CT images of the human brain using a generative adversarial network (GAN)–based deep learning framework called the CypixGAN, which combined the CycleGAN framework with the L1 loss function of the pix2pix.
Patients and Methods
Data from 143 patients who underwent PET/CT and MRI were used for training (n = 79), validation (n = 20), and testing (n = 44) the deep learning frameworks. Synthetic FLAIR images were generated using the pix2pix, CycleGAN, and CypixGAN, and white matter hyperintensities (WMHs) were then segmented. The performance of CypixGAN was compared with that of the other frameworks.
Results
The CypixGAN outperformed the pix2pix and CycleGAN in generating synthetic FLAIR images with superior visual quality. Peak signal-to-noise ratio and structural similarity index (mean ± standard deviation) estimated using the CypixGAN (20.23 ± 1.31 and 0.80 ± 0.02, respectively) were significantly higher than those estimated using the pix2pix (19.35 ± 1.43 and 0.79 ± 0.02, respectively) and CycleGAN (18.74 ± 1.49 and 0.78 ± 0.02, respectively) (P < 0.001). WMHs in synthetic FLAIR images generated using the CypixGAN closely resembled those in ground-truth images, as indicated by the low absolute percentage volume differences and high dice similarity coefficients.
Conclusions
The CypixGAN generated high-quality FLAIR images owing to the preservation of spatial information despite using unpaired images. This framework may help improve diagnostic performance and cost-effectiveness of PET/CT when MRI scan is unavailable.
논문정보
- Research article
- 2024년 11월 (BRIC 등록일 2024-12-09)
- 국내 연구진
- 의약학 > 응용의학
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