Name
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 littps://github.com/hongwang01/InDuDoNet. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-87231-1_11 | MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2021, PT VI |
Keywords | DocType | Volume |
Metal artifact reduction, Imaging geometry, Physical interpretability, Multi-class segmentation, Generalization ability | Conference | 12906 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hong Wang | 1 | 0 | 0.34 |
| Yuexiang Li | 2 | 0 | 1.69 |
| Haimiao Zhang | 3 | 0 | 0.34 |
| Jiawei Chen | 4 | 87 | 14.73 |
| Kai Ma | 5 | 49 | 18.48 |
| Deyu Meng | 6 | 2025 | 105.31 |
| Yefeng Zheng | 7 | 1391 | 114.67 |