Youngbeom Seo, Heesung Yang, Eunjung Kong, Vivek Sanker, Atman Desai, Jungwon Lee, So Hee Park, You Seon Song, Ikchan Jeon
{"title":"在脑癌中使用条件GAN从MR到合成18F-FDOPA PET/MR融合图像的跨模态图像到图像转换。","authors":"Youngbeom Seo, Heesung Yang, Eunjung Kong, Vivek Sanker, Atman Desai, Jungwon Lee, So Hee Park, You Seon Song, Ikchan Jeon","doi":"10.1007/s00234-025-03704-z","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>This study aims to identify the possibility of cross-modality image-to-image translation from magnetic resonance (MR) to synthetic positron emission tomography (PET)/MR fusion images using conditional generative adversarial networks (CGAN).</p><p><strong>Methods: </strong>Retrospective study was conducted involving 32 simultaneous 6-[<sup>18</sup>F]-fluoro-L-3,4-dihydroxyphenylalanine (<sup>18</sup>F-FDOPA) PET/MR imaging examinations from 27 patients diagnosed with brain cancer. We applied paired axial T1-weighted contrast MR (T1C) and PET/T1C fusion images to translate from T1C to synthetic PET/T1C fusion images using the Pix2Pix algorithm of CGAN. To access the image similarity between real and synthetic PET/T1C fusion images, we calculated correlation coefficients for the maximum/mean tumor-to-background ratio (TBR<sub>max/mean</sub>) and quantitative analyses were performed using peak signal-to-noise ratio (PSNR), mean squared error (MSE), structural similarity index (SSIM), and feature similarity index measure (FSIM).</p><p><strong>Results: </strong>Total 2167 pairs of T1C and PET/T1C fusion images were obtained, which were randomly assigned to training and test datasets in 9:1 ratio (1950 and 217 pairs), and training data were further divided into training and validation datasets in 4:1 ratio (1560 and 390 pairs). The correlation coefficients were 0.706 (CI:0.533-0.822) for TBR<sub>max</sub> (p < 0.001) and 0.901 (CI:0.831-0.943) for TBR<sub>mean</sub> (p < 0.001). The quantitative analyses were PSNR of 31.075 ± 3.976, MSE of 0.001 ± 0.001, SSIM of 0.868 ± 0.079, and FSIM of 0.922 ± 0.044, respectively.</p><p><strong>Conclusion: </strong>CGAN based on simultaneous <sup>18</sup>F-FDOPA PET/MR imaging data demonstrated the potential for cross-modality image-to-image translation from T1C to PET/T1C fusion images, though limitations in small dataset and lack of external validation requiring further research.</p>","PeriodicalId":19422,"journal":{"name":"Neuroradiology","volume":" ","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Cross-modality image-to-image translation from MR to synthetic <sup>18</sup>F-FDOPA PET/MR fusion images using conditional GAN in brain cancer.\",\"authors\":\"Youngbeom Seo, Heesung Yang, Eunjung Kong, Vivek Sanker, Atman Desai, Jungwon Lee, So Hee Park, You Seon Song, Ikchan Jeon\",\"doi\":\"10.1007/s00234-025-03704-z\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>This study aims to identify the possibility of cross-modality image-to-image translation from magnetic resonance (MR) to synthetic positron emission tomography (PET)/MR fusion images using conditional generative adversarial networks (CGAN).</p><p><strong>Methods: </strong>Retrospective study was conducted involving 32 simultaneous 6-[<sup>18</sup>F]-fluoro-L-3,4-dihydroxyphenylalanine (<sup>18</sup>F-FDOPA) PET/MR imaging examinations from 27 patients diagnosed with brain cancer. We applied paired axial T1-weighted contrast MR (T1C) and PET/T1C fusion images to translate from T1C to synthetic PET/T1C fusion images using the Pix2Pix algorithm of CGAN. To access the image similarity between real and synthetic PET/T1C fusion images, we calculated correlation coefficients for the maximum/mean tumor-to-background ratio (TBR<sub>max/mean</sub>) and quantitative analyses were performed using peak signal-to-noise ratio (PSNR), mean squared error (MSE), structural similarity index (SSIM), and feature similarity index measure (FSIM).</p><p><strong>Results: </strong>Total 2167 pairs of T1C and PET/T1C fusion images were obtained, which were randomly assigned to training and test datasets in 9:1 ratio (1950 and 217 pairs), and training data were further divided into training and validation datasets in 4:1 ratio (1560 and 390 pairs). The correlation coefficients were 0.706 (CI:0.533-0.822) for TBR<sub>max</sub> (p < 0.001) and 0.901 (CI:0.831-0.943) for TBR<sub>mean</sub> (p < 0.001). 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引用次数: 0
摘要
目的：本研究旨在确定使用条件生成对抗网络（CGAN）从磁共振（MR）到合成正电子发射断层扫描(PET)/磁共振融合图像的跨模态图像到图像转换的可能性。方法：对27例诊断为脑癌的患者同时进行32次6-[18F]-氟- l -3,4-二羟基苯丙氨酸（18F- fdopa） PET/MR成像检查进行回顾性研究。我们使用配对轴向t1加权对比MR （T1C）和PET/T1C融合图像，使用CGAN的Pix2Pix算法将T1C转换为合成PET/T1C融合图像。为了获得真实和合成PET/T1C融合图像之间的图像相似性，我们计算了最大/平均肿瘤与背景比（TBRmax/mean）的相关系数，并使用峰值信噪比（PSNR）、均方误差（MSE）、结构相似指数（SSIM）和特征相似指数测量（FSIM）进行了定量分析。结果：共获得T1C和PET/T1C融合图像2167对，按9:1的比例随机分配到训练数据集和测试数据集（1950对和217对），训练数据进一步按4:1的比例划分到训练数据集和验证数据集（1560对和390对）。结论：基于同步18F-FDOPA PET/MR成像数据的CGAN显示了从T1C到PET/T1C融合图像的跨模态图像转换的潜力，尽管数据集小且缺乏外部验证，需要进一步研究。
Cross-modality image-to-image translation from MR to synthetic 18F-FDOPA PET/MR fusion images using conditional GAN in brain cancer.
Objective: This study aims to identify the possibility of cross-modality image-to-image translation from magnetic resonance (MR) to synthetic positron emission tomography (PET)/MR fusion images using conditional generative adversarial networks (CGAN).
Methods: Retrospective study was conducted involving 32 simultaneous 6-[18F]-fluoro-L-3,4-dihydroxyphenylalanine (18F-FDOPA) PET/MR imaging examinations from 27 patients diagnosed with brain cancer. We applied paired axial T1-weighted contrast MR (T1C) and PET/T1C fusion images to translate from T1C to synthetic PET/T1C fusion images using the Pix2Pix algorithm of CGAN. To access the image similarity between real and synthetic PET/T1C fusion images, we calculated correlation coefficients for the maximum/mean tumor-to-background ratio (TBRmax/mean) and quantitative analyses were performed using peak signal-to-noise ratio (PSNR), mean squared error (MSE), structural similarity index (SSIM), and feature similarity index measure (FSIM).
Results: Total 2167 pairs of T1C and PET/T1C fusion images were obtained, which were randomly assigned to training and test datasets in 9:1 ratio (1950 and 217 pairs), and training data were further divided into training and validation datasets in 4:1 ratio (1560 and 390 pairs). The correlation coefficients were 0.706 (CI:0.533-0.822) for TBRmax (p < 0.001) and 0.901 (CI:0.831-0.943) for TBRmean (p < 0.001). The quantitative analyses were PSNR of 31.075 ± 3.976, MSE of 0.001 ± 0.001, SSIM of 0.868 ± 0.079, and FSIM of 0.922 ± 0.044, respectively.
Conclusion: CGAN based on simultaneous 18F-FDOPA PET/MR imaging data demonstrated the potential for cross-modality image-to-image translation from T1C to PET/T1C fusion images, though limitations in small dataset and lack of external validation requiring further research.
期刊介绍：
Neuroradiology aims to provide state-of-the-art medical and scientific information in the fields of Neuroradiology, Neurosciences, Neurology, Psychiatry, Neurosurgery, and related medical specialities. Neuroradiology as the official Journal of the European Society of Neuroradiology receives submissions from all parts of the world and publishes peer-reviewed original research, comprehensive reviews, educational papers, opinion papers, and short reports on exceptional clinical observations and new technical developments in the field of Neuroimaging and Neurointervention. The journal has subsections for Diagnostic and Interventional Neuroradiology, Advanced Neuroimaging, Paediatric Neuroradiology, Head-Neck-ENT Radiology, Spine Neuroradiology, and for submissions from Japan. Neuroradiology aims to provide new knowledge about and insights into the function and pathology of the human nervous system that may help to better diagnose and treat nervous system diseases. Neuroradiology is a member of the Committee on Publication Ethics (COPE) and follows the COPE core practices. Neuroradiology prefers articles that are free of bias, self-critical regarding limitations, transparent and clear in describing study participants, methods, and statistics, and short in presenting results. Before peer-review all submissions are automatically checked by iThenticate to assess for potential overlap in prior publication.
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