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 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
El-Hossiny, A., Ibrahem, M., & Shaaban, A. R. (2025). Deep Learning-Based Synthetic Brain Images from CT/MRI Data: A Review. Advanced Sciences and Technology Journal, 3(1), 1-9. doi: 10.21608/astj.2025.352770.1048
MLA
Ahmed S. El-Hossiny; Mostafa El-Hussien Ibrahem; Abdel Rahman Shaaban. "Deep Learning-Based Synthetic Brain Images from CT/MRI Data: A Review", Advanced Sciences and Technology Journal, 3, 1, 2025, 1-9. doi: 10.21608/astj.2025.352770.1048
HARVARD
El-Hossiny, A., Ibrahem, M., Shaaban, A. R. (2025). 'Deep Learning-Based Synthetic Brain Images from CT/MRI Data: A Review', Advanced Sciences and Technology Journal, 3(1), pp. 1-9. doi: 10.21608/astj.2025.352770.1048
VANCOUVER
El-Hossiny, A., Ibrahem, M., Shaaban, A. R. Deep Learning-Based Synthetic Brain Images from CT/MRI Data: A Review. Advanced Sciences and Technology Journal, 2025; 3(1): 1-9. doi: 10.21608/astj.2025.352770.1048
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