A Deep Learning Model for Multi-Domain MRI Synthesis Using Generative Adversarial Networks

Han, Le Hoang Ngoc; Hien, Ngo Le Huy; Huy, Luu Van; Hieu, Nguyen Van

doi:10.15388/24-INFOR556

Informatica

A Deep Learning Model for Multi-Domain MRI Synthesis Using Generative Adversarial Networks

Volume 35, Issue 2 (2024), pp. 283–309

Le Hoang Ngoc Han Ngo Le Huy Hien Luu Van Huy Nguyen Van Hieu

https://doi.org/10.15388/24-INFOR556

Pub. online: 29 April 2024 Type: Research Article

Open Access

Received
1 July 2023

Accepted
1 April 2024

Published
29 April 2024

Abstract

In recent years, Magnetic Resonance Imaging (MRI) has emerged as a prevalent medical imaging technique, offering comprehensive anatomical and functional information. However, the MRI data acquisition process presents several challenges, including time-consuming procedures, prone motion artifacts, and hardware constraints. To address these limitations, this study proposes a novel method that leverages the power of generative adversarial networks (GANs) to generate multi-domain MRI images from a single input MRI image. Within this framework, two primary generator architectures, namely ResUnet and StarGANs generators, were incorporated. Furthermore, the networks were trained on multiple datasets, thereby augmenting the available data, and enabling the generation of images with diverse contrasts obtained from different datasets, given an input image from another dataset. Experimental evaluations conducted on the IXI and BraTS2020 datasets substantiate the efficacy of the proposed method compared to an existing method, as assessed through metrics such as Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR) and Normalized Mean Absolute Error (NMAE). The synthesized images resulting from this method hold substantial potential as invaluable resources for medical professionals engaged in research, education, and clinical applications. Future research gears towards expanding experiments to larger datasets and encompassing the proposed approach to 3D images, enhancing medical diagnostics within practical applications.

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Biographies

Han Le Hoang Ngoc

hanlehngoc080601@gmail.com

L.H.N. Han is a junior researcher at the Department of Software Engineering, The University of Danang – University of Science and Technology. Her research interests are data science and computer vision, with a special focus on MRI image processing. She obtained her BSc from the University of Danang – University of Science and Technology in Vietnam with a specialization in data science and artificial intelligence.

Hien Ngo Le Huy

ngolehuyhien@gmail.com

N.L.H. Hien is a postgraduate researcher at the School of Built Environment, Engineering, and Computing, Leeds Beckett University, United Kingdom. He obtained an MSc in information and technology at Leeds Beckett University under the Erasmus Mundus in Green Networking and Cloud Computing program. During the last six years, he has published research papers mainly related to the development of machine learning and deep learning models and their applications. His research interests include artificial intelligence, green ICT, and sustainable computing.

Huy Luu Van

lvhuy@dut.udn.com

L.V. Huy is a teaching assistant at the University of Danang – University of Science and Technology. His research interests include computer vision and natural language processing. He obtained his MSc and BSc degrees from the University of Danang – University of Science and Technology in Vietnam.

Hieu Nguyen Van

nvhieuqt@dut.udn.com

N.V. Hieu is a full senior lecturer at the University of Danang – University of Science and Technology. His research interests include deep learning, modelling, optimization, decision making and computational impacts. He obtained his PhD from Saint Petersburg State Institute of Technology and his MSc and BSc degrees from Saint Petersburg State University in Russia.

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Keywords

medical imaging MRI synthesis deep learning GANs

Funding

This work was funded by The University of Danang, University of Science and Technology and the Master, PhD Scholarship Programme of Vingroup Innovation Foundation (VINIF), the code number of Project: VINIF.2023.Ths.045.

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