@inproceedings{ea6c187aa1544c6f9492e99bb2623785,
title = "3D Super Resolution for Non-Isotropic Medical Image Through Multi-Input 3D ResUnet",
abstract = "Fluid-attenuated inversion recovery imaging (FLAIR) is a magnetic resonance (MR) method that is frequently utilized to diagnose brain lesions. However, it usually provides transverse section imaging in high resolution but with very low resolution in the other axis. This non-isotropy huddles its wide utilization in research including machine learning. In this study, we applied a deep-learning based super resolution (SR) technique to convert non-isotropic FLAIR images into isotropic one. We proposed a multi-input 3D ResUnet that uses both FLAIR and T1-weighted MR images as its inputs. As a result, our proposed model successfully reconstructed isotropic FLAIR images (SSIM: 0.9947 ± 0.0009), and out-performed the FLAIR only single-input 3D ResUnet.",
keywords = "Fluid-attenuated inversion recovery (FLAIR) imaging, multi-input 3D ResUnet, non-isotropy, Super Resolution",
author = "Youjin Seo and Jeong, \{Byeong Chang\} and Yeji Yoon and Daegyeom Kim and Min, \{Ju Hong\} and Han, \{Cheol E.\}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 International Joint Conference on Neural Networks, IJCNN 2023 ; Conference date: 18-06-2023 Through 23-06-2023",
year = "2023",
doi = "10.1109/IJCNN54540.2023.10191100",
language = "English",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "IJCNN 2023 - International Joint Conference on Neural Networks, Proceedings",
}