@inproceedings{d2dac356897e4975aa9fdcb77750d58a,
title = "CT-Guided 3D Super-Resolution Method for Left Atrial Model",
abstract = "In atrial fibrillation treatment, it is important to visualize and analyze an accurate left atrial (LA) model from cardiac computed tomography (CT) images. In recent years, 3D-CNNs have been applied to acquire an accurate LA model from CT images. However, due to the hardware limitations, only LA models with low-resolution can be obtained. In this paper, we present a 3D super-resolution method that utilizes the high-resolution original CT volume as a guide by combining features with the same receptive field in layer level. Experimental results show that the proposed method achieves high performance in terms of quantitative and qualitative evaluations.",
keywords = "3D-CNN, atrial fibrillation, computed tomography, deep learning, left atrial model, super-resolution",
author = "Yoo, {Jae Ik} and Shin, {Hong Kyu} and Seung Park and Lee, {Dae In} and Ko, {Sung Jea}",
note = "Funding Information: ACKNOWLEDGMENT This work was supported by the Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (2017-0-00250, Intelligent Defense Boundary Surveillance Technology Using Collaborative Reinforced Learning of Embedded Edge Camera and Image Analysis). Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Consumer Electronics, ICCE 2021 ; Conference date: 10-01-2021 Through 12-01-2021",
year = "2021",
month = jan,
day = "10",
doi = "10.1109/ICCE50685.2021.9427717",
language = "English",
series = "Digest of Technical Papers - IEEE International Conference on Consumer Electronics",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2021 IEEE International Conference on Consumer Electronics, ICCE 2021",
}