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Generating synthetic CTs from magnetic resonance images using generative adversarial networks
Open accessAbstract
Purpose: While MR‐only treatment planning using synthetic CTs (synCTs) offers potential for streamlining clinical workflow, a need exists for an efficient and automated synCT generation in the brain to facilitate near real‐time MR‐only planning. This work describes a novel method for generating brain synCTs based on generative adversarial networks (GANs), a deep learning model that trains two competing networks simultaneously, and compares it to a deep convolutional neural network (CNN). Methods: Post‐Gadolinium T1‐Weighted and CT‐SIM images from fifteen brain cancer patients were retrospectively analyzed. The GAN model was developed to generate synCTs using T1‐weighted MRI images as the input using a residual network (ResNet) as the generator. The discriminator is a CNN with five convolutional layers that classified the input image as real or synthetic. Fivefold cross‐validation was performed to validate our model. GAN performance was compared to CNN based on mean absolute error (MAE), structural similarity index (SSIM), and peak signal‐to‐noise ratio (PSNR) metrics between the synCT and CT images. Results: GAN training took ~11 h with a new case testing time of 5.7 ± 0.6 s. For GAN, MAEs between synCT and CT‐SIM were 89.3 ± 10.3 Hounsfield units (HU) and 41.9 ± 8.6 HU across the entire FOV and tissues, respectively. However, MAE in the bone and air was, on average, ~240–255 HU. By comparison, the CNN model had an average full FOV MAE of 102.4 ± 11.1 HU. For GAN, the mean PSNR was 26.6 ± 1.2 and SSIM was 0.83 ± 0.03. GAN synCTs preserved details better than CNN, and regions of abnormal anatomy were well represented on GAN synCTs. Conclusions: We developed and validated a GAN model using a single T1‐weighted MR image as the input that generates robust, high quality synCTs in seconds. Our method offers strong potential for supporting near real‐time MR‐only treatment planning in the brain.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
Image-to-Image Translation with Conditional Adversarial Networks
Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.19 · Cited by 12,696
“Least squares generative adversarial networks,”
Mao, X., Li, Q., Xie, H., Lau, R. Y. K., Wang, Z., & Smolley, S. P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.304 · Cited by 5,437
Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization
Huang, X., & Belongie, S. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.167 · Cited by 5,076
Multimodality image registration by maximization of mutual information
Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., & Suetens, P. (1997) · IEEE Transactions on Medical Imaging · doi:10.1109/42.563664 · Cited by 4,547
An overlap invariant entropy measure of 3D medical image alignment
Studholme, C., Hill, D. L. G., & Hawkes, D. J. (1999) · Pattern Recognition · doi:10.1016/s0031-3203(98)00091-0 · Cited by 2,180
MR-based synthetic CT generation using a deep convolutional neural network method
Han, X. (2017) · Medical Physics · doi:10.1002/mp.12155 · Cited by 711
Medical Image Synthesis with Context-Aware Generative Adversarial Networks
Nie, D., Trullo, R., Lian, J., Petitjean, C., Ruan, S., Wang, Q., & Shen, D. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-66179-7_48 · Cited by 691
Hofmann, M., Steinke, F., Scheel, V., Charpiat, G., Farquhar, J., Aschoff, P., Brady, M., Schölkopf, B., & Pichler, B. J. (2008) · Journal of Nuclear Medicine · doi:10.2967/jnumed.107.049353 · Cited by 495
Johansson, A., Karlsson, M., & Nyholm, T. (2011) · Medical Physics · doi:10.1118/1.3578928 · Cited by 324
Magnetic Resonance Imaging of Short T2 Components in Tissue
Gatehouse, P. D., & Bydder, G. M. (2003) · Clinical Radiology · doi:10.1053/crad.2003.1157 · Cited by 317
New developments in MRI for target volume delineation in radiotherapy
Khoo, V. S., & Joon, D. L. (2006) · The British Journal of Radiology · doi:10.1259/bjr/41321492 · Cited by 207
Target volumes in radiotherapy for high-grade malignant glioma of the brain
Jansen, E. P. M., Dewit, L. G. H., van Herk, M., & Bartelink, H. G. M. M. (2000) · Radiotherapy and Oncology · doi:10.1016/s0167-8140(00)00216-4 · Cited by 123
Alternative methods for attenuation correction for PET images in MR-PET scanners
Kops, E. R., & Herzog, H. (2007) · 2006 IEEE Nuclear Science Symposium Conference Record · doi:10.1109/nssmic.2007.4437073 · Cited by 80
Zheng, W., Kim, J. P., Kadbi, M., Movsas, B., Chetty, I. J., & Glide‐Hurst, C. K. (2015) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2015.07.001 · Cited by 72
Self-citationImaging and Target Volume Delineation in Glioma
Whitfield, G. A., Kennedy, S. R., Djoukhadar, I., & Jackson, A. (2014) · Clinical Oncology · doi:10.1016/j.clon.2014.04.026 · Cited by 58
Robust CT Synthesis for Radiotherapy Planning: Application to the Head and Neck Region
Burgos, N., Cardoso, M. J., Guerreiro, F., Veiga, C., Marc Modat, A., McClelland, J. R., Knopf, A. C., Punwani, S., Atkinson, D., Arridge, S., et al. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24571-3_57 · Cited by 42
Jonsson, J. H., Akhtari, M. M., Karlsson, M. G., Johansson, A., Asklund, T., & Nyholm, T. (2015) · Radiation Oncology · doi:10.1186/s13014-014-0308-1 · Cited by 32
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Shifting machine learning for healthcare from development to deployment and from models to data
Zhang, A., Xing, L., Zou, J., & Wu, J. C. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00898-y · Cited by 420
Yu, H., Yang, L. T., Zhang, Q., Armstrong, D., & Deen, M. J. (2021) · Neurocomputing · doi:10.1016/j.neucom.2020.04.157 · Cited by 412
Deep learning for Alzheimer's disease diagnosis: A survey
Khojaste-Sarakhsi, M., Haghighi, S. S., Ghomi, S. M. T. F., & Marchiori, E. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102332 · Cited by 194
Systematic Review of Advanced AI Methods for Improving Healthcare Data Quality in Post COVID-19 Era
Isgut, M., Gloster, L., Choi, K., Venugopalan, J., & Wang, M. D. (2023) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2022.3216531 · Cited by 35
Metadata
- Authors
- Emami, H., Dong, M., Nejad‐Davarani, S. P., & Glide‐Hurst, C. K.
- Venue
- Medical Physics
- Publisher
- Wiley
- Published
- 2018-07-10
- Volume
- 45
- Issue
- 8
- Pages
- 3627-3636
- ISSN
- 0094-2405 (ISSN-L)
- 2473-4209
- OpenAlex
- W2808312419
- Cited by
- 300
- References
- 22
- FWCI
- 30.58
- Type
- journal article
- Language
- en
- Open access
- green
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