Abstract
Computed tomography (CT) is critical for various clinical applications, e.g., radiation treatment planning and also PET attenuation correction in MRI/PET scanner. However, CT exposes radiation during acquisition, which may cause side effects to patients. Compared to CT, magnetic resonance imaging (MRI) is much safer and does not involve radiations. Therefore, recently researchers are greatly motivated to estimate CT image from its corresponding MR image of the same subject for the case of radiation planning. In this paper, we propose a data-driven approach to address this challenging problem. Specifically, we train a fully convolutional network (FCN) to generate CT given the MR image. To better model the nonlinear mapping from MRI to CT and produce more realistic images, we propose to use the adversarial training strategy to train the FCN. Moreover, we propose an image-gradient-difference based loss function to alleviate the blurriness of the generated CT. We further apply Auto-Context Model (ACM) to implement a context-aware generative adversarial network. Experimental results show that our method is accurate and robust for predicting CT images from MR images, and also outperforms three state-of-the-art methods under comparison.
Original language | English |
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Title of host publication | Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings |
Editors | Lena Maier-Hein, Alfred Franz, Pierre Jannin, Simon Duchesne, Maxime Descoteaux, D. Louis Collins |
Publisher | Springer Verlag |
Pages | 417-425 |
Number of pages | 9 |
ISBN (Print) | 9783319661780 |
DOIs | |
Publication status | Published - 2017 |
Event | 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017 - Quebec City, Canada Duration: 2017 Sept 11 → 2017 Sept 13 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 10435 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Other
Other | 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017 |
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Country/Territory | Canada |
City | Quebec City |
Period | 17/9/11 → 17/9/13 |
Bibliographical note
Publisher Copyright:© Springer International Publishing AG 2017.
Keywords
- Auto-context
- Deep learning
- GAN
- Generative models
- Image synthesis
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science