CT to MRI Image Translation Using CycleGAN: A Deep Learning Approach for Cross-Modality Medical Imaging

Anamika Jha, Hitoshi Iima

2024

Abstract

Medical imaging plays a crucial role in healthcare, with Magnetic Resonance Imaging (MRI) and Computed tomography (CT) as key modalities, each having unique strengths and weaknesses. MRI offers exceptional soft tissue contrast, but it is slow and costly, while CT is faster but involves ionizing radiation. To address this paradox, we leverage deep learning, employing CycleGAN to translate CT scans into MRI-like images. This approach eliminates the need for additional radiation exposure or costs. Our results, which show the effectiveness of our image translation method with an MAE of 0.5309, MSE of 0.37901, and PSNR of 52.344, demonstrate the promise of this invention in lowering healthcare costs, expanding diagnostic capabilities, and improving patient outcomes. The model was trained for 500 epochs with a batch size of 500 on an Nvidia GPU, RTX A6OOO.

Paper Citation

in Harvard Style

Jha A. and Iima H. (2024). CT to MRI Image Translation Using CycleGAN: A Deep Learning Approach for Cross-Modality Medical Imaging. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 951-957. DOI: 10.5220/0012422900003636

in Bibtex Style

@conference{icaart24,
author={Anamika Jha and Hitoshi Iima},
title={CT to MRI Image Translation Using CycleGAN: A Deep Learning Approach for Cross-Modality Medical Imaging},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={951-957},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012422900003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - CT to MRI Image Translation Using CycleGAN: A Deep Learning Approach for Cross-Modality Medical Imaging
SN - 978-989-758-680-4
AU - Jha A.
AU - Iima H.
PY - 2024
SP - 951
EP - 957
DO - 10.5220/0012422900003636
PB - SciTePress