Abstract
The ability to generate multiple contrasts for the same patient is unique about MRI and of very high clinical value. In this work, we take up the problem of modality synthesis in multimodal MRI and propose an efficient, multiresolution encoder-decoder network trained like an autoencoder that can predict missed inputs at the output. This can help in avoiding the acquisition of redundant information, thereby saving time. We formulate and demonstrate our proposed AutoSyncoder network in a GAN and cyclic GAN setting, and evaluate on the BRATS-15 multimodal glioma dataset. A PSNR ranging between 29 to 30.5 dB, and SSIM over 0.88 is achieved for all the modalities, with simplistic training, thereby establishing the potential of our approach.
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References
Chartsias, A., Joyce, T., Giuffrida, M.V., Tsaftaris, S.A.: Multimodal MR synthesis via modality-invariant latent representation. IEEE Trans. Med. Imaging 37(3), 803–814 (2018)
Choi, Y., Choi, M., Kim, M., Ha, J.W., Kim, S., Choo, J.: StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation, pp. 8789–8797 (2018)
Jog, A., Carass, A., Roy, S., Pham, D.L., Prince, J.L.: Random forest regression for magnetic resonance image synthesis. Med. Image Anal. 35, 475–488 (2017)
Kim, S., Jang, H., Jang, J., Lee, Y.H., Hwang, D.: Deep-learned short tau inversion recovery imaging using multi-contrast MR images. Magn. Reson. Med. 84, 2994–3008 (2020)
Lee, D., Moon, W.J., Ye, J.C.: Assessing the importance of magnetic resonance contrasts using collaborative generative adversarial networks. Nature Mach. Intell. 2(1), 34–42 (2020)
Li, H., et al.: DiamondGAN: unified multi-modal generative adversarial networks for MRI sequences synthesis. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 795–803. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32251-9_87
Liu, X., Wang, M., Zha, Z.J., Hong, R.: Cross-modality feature learning via convolutional autoencoder. ACM Trans. Multimedia Comput. Commun. Appl. 15(1s), 7:1–7:20 (2019)
Mason, A., et al.: Comparison of objective image quality metrics to expert radiologists’ scoring of diagnostic quality of MR images. IEEE Trans. Med. Imaging 39(4), 1064–1072 (2020)
Menze, B., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34, 1993–2024 (2014)
Sharma, A., Hamarneh, G.: Missing MRI pulse sequence synthesis using multi-modal generative adversarial network. IEEE Trans. Med. Imaging 39(4), 1170–1183 (2020)
Van Nguyen, H., Zhou, K., Vemulapalli, R.: Cross-domain synthesis of medical images using efficient location-sensitive deep network. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 677–684. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24553-9_83
Yang, Q., Li, N., Zhao, Z., Fan, X., Chang, E.I.C., Xu, Y.: MRI cross-modality image-to-image translation. Sci. Rep. 10(1), 3753 (2020)
Ye, D.H., Zikic, D., Glocker, B., Criminisi, A., Konukoglu, E.: Modality propagation: coherent synthesis of subject-specific scans with data-driven regularization. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8149, pp. 606–613. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40811-3_76
Yurt, M., Dar, S.U.H., Erdem, A., Erdem, E., Cukur, T.: mustGAN: multi-stream generative adversarial networks for MR image synthesis. arXiv:1909.11504 [cs, eess], September 2019
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Raju, J., Murugesan, B., Ram, K., Sivaprakasam, M. (2020). AutoSyncoder: An Adversarial AutoEncoder Framework for Multimodal MRI Synthesis. In: Deeba, F., Johnson, P., Würfl, T., Ye, J.C. (eds) Machine Learning for Medical Image Reconstruction. MLMIR 2020. Lecture Notes in Computer Science(), vol 12450. Springer, Cham. https://doi.org/10.1007/978-3-030-61598-7_10
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DOI: https://doi.org/10.1007/978-3-030-61598-7_10
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