The Role of the Generative Adversarial Network in Medical Image Reconstruction: A Systematic Review.

Background: In the realm of medical imaging, obtaining clear, high-resolution images is challenging due to a multitude of factors encompassing the intricacies of imaging systems, diverse imaging environments, and the potential impact of human-related variables. The imperative initial step in the assessment of medical images involves medical image processing, a field that leverages the power of machine learning and deep learning models to cultivate intelligent systems, thereby imbuing these images with heightened interpretability and enhancing diagnostic efficiency. The advent of Generative Adversarial Networks (GANs) represents a transformative technological breakthrough, ushering in a new era in the realm of medical image analysis. GANs have introduced a robust framework for the manifold applications of medical images. These applications vary from the enhancement of medical images to their precise segmentation, accurate classification, meticulous reconstruction, and even synthesis. This study aimed to give a general insight into the role of Generative Adversarial Networks (GANs) in medical image reconstruction. This comprehensive background provides the necessary context for understanding the pivotal role of GANs in revolutionizing the domain of medical imaging and underscores their impact on the development of sophisticated and intuitive systems for the advancement of medical diagnostics. Materials and Methods: PubMed, ScienceDirect, Web of Science databases, and Google Scholar were explored using different combinations of keywords: "Generative Adversarial Networks (GANs)"," Deep Learning", "Image Reconstruction", "Medical Imaging" and "Artificial Intelligence". Also, an additional search was performed on Semantic Scholar. Finally, 20 most related and recent papers were included in the study. Results: Generative Adversarial Networks (GANs), consisting of a generator and a discriminator neural network in a competitive framework, have demonstrated their effectiveness in medical image reconstruction. They excel in generating high-fidelity images from incomplete medical data by training on complete image datasets and leveraging this knowledge to fill in the gaps. GANs also play a pivotal role in generating multimodal datasets from a single modality source, thereby expanding the diversity of training data for improved accuracy in medical image analysis. This versatility of GANs finds practical application in various algorithms designed for medical image reconstruction, such as Medical Image Reconstruction using Generative Adversarial Networks (MirGAN) and GAN-Based Medical Image Super-Resolution via High-Resolution Representation Learning (Med-SRNet). These techniques are tailored to tasks like medical image reconstruction and super-resolution, enhancing the quality of medical images. As a result, they simplify the process of image analysis and diagnosis in the field of medicine. In this context, GANs have emerged as a transformative technology, significantly contributing to the improvement of medical imaging quality and the facilitation of more accurate analysis and diagnosis of medical conditions. Conclusion: In summary, although GANs have exhibited substantial promise in the realm of medical image reconstruction, they have also their challenges. These limitations encompass restricted data accessibility, intricate computational demands, interpretability issues, susceptibility to overfitting, and quality control concerns. [ABSTRACT FROM AUTHOR]