Abstract
MR-only radiotherapy treatment planning requires accurate MR-to-CT synthesis. Current deep learning methods for MR-to-CT synthesis depend on pairwise aligned MR and CT training images of the same patient. However, misalignment between paired images could lead to errors in synthesized CT images. To overcome this, we propose to train a generative adversarial network (GAN) with unpaired MR and CT images. A GAN consisting of two synthesis convolutional neural networks (CNNs) and two discriminator CNNs was trained with cycle consistency to transform 2D brain MR image slices into 2D brain CT image slices and vice versa. Brain MR and CT images of 24 patients were analyzed. A quantitative evaluation showed that the model was able to synthesize CT images that closely approximate reference CT images, and was able to outperform a GAN model trained with paired MR and CT images.
Original language | English |
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Title of host publication | Simulation and Synthesis in Medical Imaging |
Subtitle of host publication | Second International Workshop, SASHIMI 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 10, 2017, Proceedings |
Editors | Ali Gooya, Alejandro F. Frangi, Sotirios A. Tsaftaris, Jerry L. Prince |
Place of Publication | Cham |
Publisher | Springer |
Pages | 14-23 |
Number of pages | 10 |
ISBN (Electronic) | 978-3-319-68127-6 |
ISBN (Print) | 978-3-319-68126-9 |
DOIs | |
Publication status | Published - 1 Jan 2017 |
Externally published | Yes |
Event | 2nd International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2017 - Quebec City, Canada Duration: 10 Sep 2017 → 10 Sep 2017 Conference number: 2 |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer |
Volume | 10557 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Workshop
Workshop | 2nd International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2017 |
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Abbreviated title | SASHIMI |
Country/Territory | Canada |
City | Quebec City |
Period | 10/09/17 → 10/09/17 |
Keywords
- CT synthesis
- Deep learning
- Generative adversarial networks
- Radiotherapy
- Treatment planning