MAN-GAN: a mask-adaptive normalization based generative adversarial networks for liver multi-phase CT image generation
Abstract Liver multiphase enhanced computed tomography (MPECT) is vital in clinical practice, but its utility is limited by various factors. We aimed to develop a deep learning network capable of automatically generating MPECT images from standard non-contrast CT scans. Dataset 1 included 374 patien...
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Nature Portfolio
2025-07-01
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| Online Access: | https://doi.org/10.1038/s41598-025-10754-z |
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| author | Wei Zhao Wenting Chen Li Fan Youlan Shang Yisong Wang Weijun Situ Wenzheng Li Tianming Liu Yixuan Yuan Jun Liu |
| author_facet | Wei Zhao Wenting Chen Li Fan Youlan Shang Yisong Wang Weijun Situ Wenzheng Li Tianming Liu Yixuan Yuan Jun Liu |
| author_sort | Wei Zhao |
| collection | DOAJ |
| description | Abstract Liver multiphase enhanced computed tomography (MPECT) is vital in clinical practice, but its utility is limited by various factors. We aimed to develop a deep learning network capable of automatically generating MPECT images from standard non-contrast CT scans. Dataset 1 included 374 patients and was divided into three parts: a training set, a validation set and a test set. Dataset 2 included 144 patients with one specific liver disease and was used as an internal test dataset. We further collected another dataset comprising 83 patients for external validation. Then, we propose a Mask-Adaptive Normalization-based Generative Adversarial Network with Cycle-Consistency Loss (MAN-GAN) to achieve non-contrast CT to MPECT translation. To assess the efficiency of MAN-GAN, we conducted a comparative analysis with state-of-the-art methods commonly employed in diverse medical image synthesis tasks. Moreover, two subjective radiologist evaluation studies were performed to verify the clinical usefulness of the generated images. MAN-GAN outperformed the baseline network and other state-of-the-art methods in all generations of the three phases. These results were verified in internal and external datasets. According to radiological evaluation, the image quality of generated three phase images are all above average. Moreover, the similarities between real images and generated images in all three phases are satisfactory. MAN-GAN demonstrates the feasibility of liver MPECT image translation based on non-contrast images and achieves state-of-the-art performance via the subtraction strategy. It has great potential for solving the dilemma of liver CT contrast canning and aiding further liver interaction clinical scenarios. |
| format | Article |
| id | doaj-art-9d07dd19a44e4811927f0d93411b8a05 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-9d07dd19a44e4811927f0d93411b8a052025-08-20T03:42:29ZengNature PortfolioScientific Reports2045-23222025-07-0115111110.1038/s41598-025-10754-zMAN-GAN: a mask-adaptive normalization based generative adversarial networks for liver multi-phase CT image generationWei Zhao0Wenting Chen1Li Fan2Youlan Shang3Yisong Wang4Weijun Situ5Wenzheng Li6Tianming Liu7Yixuan Yuan8Jun Liu9Department of Radiology, The Second Xiangya Hospital, Central South UniversityDepartment of Electrical Engineering, City University of Hong KongDepartment of Radiology, The Second Xiangya Hospital, Central South UniversityDepartment of Radiology, The Second Xiangya Hospital, Central South UniversityDepartment of Radiology, The Second Xiangya Hospital, Central South UniversityDepartment of Radiology, The Second Xiangya Hospital, Central South UniversityDepartment of Radiology, and National Clinical Research Center for Geriatric Disorders, Xiangya HospitalSchool of Computing, The University of GeorgiaDepartment of Electronic Engineering, The Chinese University of Hong KongDepartment of Radiology, The Second Xiangya Hospital, Central South UniversityAbstract Liver multiphase enhanced computed tomography (MPECT) is vital in clinical practice, but its utility is limited by various factors. We aimed to develop a deep learning network capable of automatically generating MPECT images from standard non-contrast CT scans. Dataset 1 included 374 patients and was divided into three parts: a training set, a validation set and a test set. Dataset 2 included 144 patients with one specific liver disease and was used as an internal test dataset. We further collected another dataset comprising 83 patients for external validation. Then, we propose a Mask-Adaptive Normalization-based Generative Adversarial Network with Cycle-Consistency Loss (MAN-GAN) to achieve non-contrast CT to MPECT translation. To assess the efficiency of MAN-GAN, we conducted a comparative analysis with state-of-the-art methods commonly employed in diverse medical image synthesis tasks. Moreover, two subjective radiologist evaluation studies were performed to verify the clinical usefulness of the generated images. MAN-GAN outperformed the baseline network and other state-of-the-art methods in all generations of the three phases. These results were verified in internal and external datasets. According to radiological evaluation, the image quality of generated three phase images are all above average. Moreover, the similarities between real images and generated images in all three phases are satisfactory. MAN-GAN demonstrates the feasibility of liver MPECT image translation based on non-contrast images and achieves state-of-the-art performance via the subtraction strategy. It has great potential for solving the dilemma of liver CT contrast canning and aiding further liver interaction clinical scenarios.https://doi.org/10.1038/s41598-025-10754-zMultiphase enhanced computed tomographyGenerative adversarial networksImage synthesis |
| spellingShingle | Wei Zhao Wenting Chen Li Fan Youlan Shang Yisong Wang Weijun Situ Wenzheng Li Tianming Liu Yixuan Yuan Jun Liu MAN-GAN: a mask-adaptive normalization based generative adversarial networks for liver multi-phase CT image generation Scientific Reports Multiphase enhanced computed tomography Generative adversarial networks Image synthesis |
| title | MAN-GAN: a mask-adaptive normalization based generative adversarial networks for liver multi-phase CT image generation |
| title_full | MAN-GAN: a mask-adaptive normalization based generative adversarial networks for liver multi-phase CT image generation |
| title_fullStr | MAN-GAN: a mask-adaptive normalization based generative adversarial networks for liver multi-phase CT image generation |
| title_full_unstemmed | MAN-GAN: a mask-adaptive normalization based generative adversarial networks for liver multi-phase CT image generation |
| title_short | MAN-GAN: a mask-adaptive normalization based generative adversarial networks for liver multi-phase CT image generation |
| title_sort | man gan a mask adaptive normalization based generative adversarial networks for liver multi phase ct image generation |
| topic | Multiphase enhanced computed tomography Generative adversarial networks Image synthesis |
| url | https://doi.org/10.1038/s41598-025-10754-z |
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