Abstract
Magnetic Resonance Image (MRI) reconstruction from undersampled data is an important ill-posed problem for biomedical imaging. For this problem, there is a significant tradeoff between the reconstructed image quality and image acquisition time reduction due to data sampling. Recently a plethora of solutions based on deep learning have been proposed in the literature to reach improved image reconstruction quality compared to traditional analytical reconstruction methods. In this paper, a novel densely connected residual generative adversarial network (DCR-GAN) is being proposed for fast and high-quality reconstruction of MR images. DCR blocks enable the reconstruction network to go deeper by preventing feature loss in the sequential convolutional layers. DCR block concatenates feature maps from multiple steps and gives them as the input to subsequent convolutional layers in a feed-forward manner. In this new model, the DCR block’s potential to train relatively deeper structures is utilized to improve quantitative and qualitative reconstruction results in comparison to the other conventional GAN-based models. We can see from the reconstruction results that the novel DCR-GAN leads to improved reconstruction results without a significant increase in the parameter complexity or run times.
This work is supported by TUBITAK (The Scientific and Technological Research Council of Turkey) under project no. 119E248.
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References
Kocanaogullari, D., Eksioglu, E.M.: Deep learning for MRI reconstruction using a novel projection based cascaded network. In: 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP), pp. 1–6 (2019)
Dar, S.U., Yurt, M., Karacan, L., Erdem, A., Erdem, E., Çukur, T.: Image synthesis in multi-contrast MRI with conditional generative adversarial networks. IEEE Trans. Med. Imaging 38(10), 2375–2388 (2019)
Eksioglu, E.M.: Decoupled algorithm for MRI reconstruction using nonlocal block matching model: BM3D-MRI. J. Math. Imaging Vis. 56(3), 430–440 (2016)
Eksioglu, E.M., Tanc, A.K.: Denoising AMP for MRI reconstruction: BM3D-AMP-MRI. SIAM J. Imag. Sci. 11(3), 2090–2109 (2018). https://doi.org/10.1137/18M1169655
Falvo, A., Comminiello, D., Scardapane, S., Scarpiniti, M., Uncini, A.: A wide multimodal dense U-Net for fast magnetic resonance imaging. In: 2020 28th European Signal Processing Conference (EUSIPCO), pp. 1274–1278. IEEE (2021)
Ghodrati, V., et al.: MR image reconstruction using deep learning: evaluation of network structure and loss functions. Quant. Imaging Med. Surg. 9(9), 1516 (2019). https://doi.org/10.21037/qims.2019.08.10
Goodfellow, I.J., et al.: Generative Adversarial Networks. arXiv preprint arXiv:1406.2661 (2014)
Han, Y., Sunwoo, L., Ye, J.C.: \({k}\)-space deep learning for accelerated MRI. IEEE Trans. Med. Imaging 39(2), 377–386 (2019)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Hochreiter, S.: The vanishing gradient problem during learning recurrent neural nets and problem solutions. Internat. J. Uncertain. Fuzziness Knowl. Based Syst. 06(02), 107–116 (1998). https://doi.org/10.1142/S0218488598000094
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)
Hyun, C.M., Kim, H.P., Lee, S.M., Lee, S., Seo, J.K.: Deep learning for undersampled MRI reconstruction. Phys. Med. Biol. 63(13), 135007 (2018). https://doi.org/10.1088/1361-6560/aac71a
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015). https://doi.org/10.1038/nature14539
Mardani, M., et al.: Deep generative adversarial neural networks for compressive sensing MRI. IEEE Trans. Med. Imaging 38(1), 167–179 (2019). https://doi.org/10.1109/TMI.2018.2858752
Park, B., Yu, S., Jeong, J.: Densely connected hierarchical network for image denoising. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 2104–2113 (2019)
Quan, T.M., Nguyen-Duc, T., Jeong, W.K.: Compressed sensing MRI reconstruction using a generative adversarial network with a cyclic loss. IEEE Trans. Med. Imaging 37(6), 1488–1497 (2018)
Schlemper, J., Caballero, J., Hajnal, J.V., Price, A.N., Rueckert, D.: A deep cascade of convolutional neural networks for dynamic MR image reconstruction. IEEE Trans. Med. Imaging 37(2), 491–503 (2017)
Shaul, R., David, I., Shitrit, O., Riklin Raviv, T.: Subsampled brain MRI reconstruction by Generative Adversarial Neural networks. Med. Image Anal. 65, 101747 (2020)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Wang, S., et al.: Accelerating magnetic resonance imaging via deep learning. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 514–517. IEEE (2016)
Yang, G., et al.: DAGAN: deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction. IEEE Trans. Med. Imaging 37(6), 1310–1321 (2018)
Yuan, Y., et al.: Prostate segmentation with encoder-decoder densely connected convolutional network (Ed-Densenet). In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 434–437 (2019)
Zaitsev, M., Maclaren, J., Herbst, M.: Motion artifacts in MRI: A complex problem with many partial solutions. J. Magn. Reson. Imaging 42(4), 887–901 (2015). https://doi.org/10.1002/jmri.24850
Zbontar, J., et al.: fastMRI: an open dataset and benchmarks for accelerated MRI. arXiv preprint arXiv:1811.08839 (2018)
Zhang, Y., Tian, Y., Kong, Y., Zhong, B., Fu, Y.: Residual dense network for image restoration. IEEE Trans. Pattern Anal. Mach. Intell. 43(7), 2480–2495 (2021)
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This work is supported by TUBITAK (The Scientific and Technological Research Council of Turkey) under project no. 119E248.
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Aghabiglou, A., Eksioglu, E.M. (2021). MR Image Reconstruction Based on Densely Connected Residual Generative Adversarial Network–DCR-GAN. In: Wojtkiewicz, K., Treur, J., Pimenidis, E., Maleszka, M. (eds) Advances in Computational Collective Intelligence. ICCCI 2021. Communications in Computer and Information Science, vol 1463. Springer, Cham. https://doi.org/10.1007/978-3-030-88113-9_55
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