Deep Learning-Based Synthetic Brain Images from CT/MRI Data: A Review | ||||
| Advanced Sciences and Technology Journal | ||||
| Articles in Press, Accepted Manuscript, Available Online from 22 August 2025 | ||||
| Document Type: Review Article | ||||
| DOI: 10.21608/astj.2025.352770.1048 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Ahmed S. El-Hossiny   ; Mostafa El-Hussien Ibrahem ; Abdel Rahman Shaaban
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| Biomedical and Systems Engineering Department, Higher Institute of Engineering, El-Shorouk Academy, Cairo, Egypt | ||||
| Abstract | ||||
| Generative Artificial Intelligent models have emerged as powerful tools in various specialties, revolutionizing the landscape of image synthesis. In the medical field, Generative Adversarial Networks (GANs) have shown tremendous potential for addressing critical challenges and unlocking new opportunities for programmers. This review paper provides an overview of the applications of GANs for medical image synthesis for the human brain, through magnetic resonance imaging (MRI) and computed tomography (CT) images discussing their role in generating realistic and diverse medical images for training robust machine learning models. The review paper discusses the need for large, annotated datasets, the differences that can be influenced by the data being paired or unpaired, the quantity of the image data set, the usage of different types of GANs and other deep learning (DL) methods for the brain modality translation, and comparing the results of mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) for papers from 2017 to 2023 | ||||
| Keywords | ||||
| Deep learning; Generative Adversarial Networks; Paired Data; Unpaired Data; synthetic CT | ||||
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