@inproceedings{ba9e9c5330fd462aae644dd9bae349cd,
title = "DPDudoNet: Deep-Prior Based Dual-Domain Network for Low-Dose Computed Tomography Reconstruction",
abstract = "Low-dose computed tomography (LDCT) reconstruction has been an active research field for years. Although deep learning (DL)-based methods have achieved incredible success in this field, most of the existing DL-based reconstruction models lack interpretability and generalizability. In this paper, we propose a novel deep prior-based dual-domain network (DPDudoNet) by unrolling the model-based algorithm using iteratively-cascaded DenseNet and deconvolutional network. The proposed model embeds the intrinsic imaging model constraints, inherited from the foundational model-based algorithm, to tackle the issue of lacking interpretability. Besides, it contains fewer learnable parameters, compared to many representative networks, thus leading to simpler decision boundary and better generalizability. Moreover, a random initialization of the network based on Gaussian distribution is introduced as a deep prior for the LDCT reconstruction. The proposed model integrates the deep prior into both the image and sinogram domains via a dual-domain update scheme. Experimental results on the public AAPM LDCT dataset show that our proposed method has significant improvement over both the state-of-the-art (SOTA) DL-based methods and the traditional model-based algorithms with less model parameters and less computational load.",
keywords = "Data consistency, DenseNet, Dual-domain, Generalizability, Interpretability, Low-dose computed tomography",
author = "Komolafe, {Temitope Emmanuel} and Yuhang Sun and Nizhuan Wang and Kaicong Sun and Guohua Cao and Dinggang Shen",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 5th International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 ; Conference date: 22-09-2022 Through 22-09-2022",
year = "2022",
month = sep,
day = "22",
doi = "10.1007/978-3-031-17247-2_13",
language = "English",
isbn = "9783031172465",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "123--132",
editor = "Nandinee Haq and Patricia Johnson and Andreas Maier and Chen Qin and Tobias W{\"u}rfl and Jaejun Yoo",
booktitle = "Machine Learning for Medical Image Reconstruction -",
}