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August 22, 2025

Deep Learning-Based Synthetic Brain Images from CT/MRI Data: A Review

Key Points

Generative adversarial networks significantly improve the generation of synthetic brain images from MRI and CT data.
Key metrics assessed include mean absolute error, peak signal-to-noise ratio, and structural similarity index measure for diverse models.
This review analyzes the current applications and methods of deep learning in synthesizing medical images for training purposes.
Findings suggest a need for large annotated datasets to enhance image quality and clinical applicability of machine learning models.

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

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Deep Learning-Based Synthetic Brain Images from CT/MRI Data: A Review

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