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.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China (62131015), the Key R&D Program of Guangdong Province (2021B0101420006), Science and Technology Commission of Shanghai Municipality (STCSM) (21010502600).
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Komolafe, T.E., Sun, Y., Wang, N., Sun, K., Cao, G., Shen, D. (2022). DPDudoNet: Deep-Prior Based Dual-Domain Network for Low-Dose Computed Tomography Reconstruction. In: Haq, N., Johnson, P., Maier, A., Qin, C., Würfl, T., Yoo, J. (eds) Machine Learning for Medical Image Reconstruction. MLMIR 2022. Lecture Notes in Computer Science, vol 13587. Springer, Cham. https://doi.org/10.1007/978-3-031-17247-2_13
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