Abstract
For the task of metal artifact reduction (MAR), although deep learning (DL)-based methods have achieved promising performances, most of them suffer from two problems: 1) the CT imaging geometry constraint is not fully embedded into the network during training, leaving room for further performance improvement; 2) the model interpretability is lack of sufficient consideration. Against these issues, we propose a novel interpretable dual domain network, termed as InDuDoNet, which combines the advantages of model-driven and data-driven methodologies. Specifically, we build a joint spatial and Radon domain reconstruction model and utilize the proximal gradient technique to design an iterative algorithm for solving it. The optimization algorithm only consists of simple computational operators, which facilitate us to correspondingly unfold iterative steps into network modules and thus improve the interpretablility of the framework. Extensive experiments on synthesized and clinical data show the superiority of our InDuDoNet. Code is available in https://github.com/hongwang01/InDuDoNet.
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Notes
- 1.
We utilize a CNN to flexibly learn \(\widetilde{X}\) and \(\widetilde{Y}\) from training data as shown in Fig. 1.
- 2.
- 3.
More analysis on network parameter and testing time are in supplementary material.
- 4.
More comparisons of MAR and bone segmentation are in supplementary material.
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Acknowledgements
This research was supported by National Key R&D Program of China (2020YFA0713900), the Macao Science and Technology Development Fund under Grant 061/2020/A2, Key-Area Research and Development Program of Guangdong Province, China (No. 2018B010111001), the Scientific and Technical Innovation 2030-“New Generation Artificial Intelligence” Project (No. 2020AAA0104100), the China NSFC projects (62076196, 11690011, 61721002, U1811461).
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Wang, H. et al. (2021). InDuDoNet: An Interpretable Dual Domain Network for CT Metal Artifact Reduction. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12906. Springer, Cham. https://doi.org/10.1007/978-3-030-87231-1_11
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