Abstract
Convolutional Neural Networks (CNNs) have achieved great advances on Magnetic Resonance Imaging (MRI) reconstruction. However, CNNs are still suffering from significant aliasing artifacts for undersampled data with high acceleration rates. This is mainly due to the huge gap between the highly undersampled k-space data and its fully-sampled counterpart. To mitigate this problem, we constructed a series of well-organized undersampled k-space data, each of which has very small frequency gap with its neighbors. By sequentially using these undersampled data and their fully-sampled ones to train a given CNN model \(\mathcal {N}\), the model \(\mathcal {N}\) can gradually know how to fill the progressively increased frequency gaps and thus reduce the aliasing artifacts. Experiments on the MSSEG dataset demonstrated the effectiveness of the proposed training method.
X-X. Li and F. Zhang—This work was supported in part by the Zhejiang Provincial Natural Science Foundation of China under Grants LGF22F020027, GF22F037921 and LGF20H180002, in part by the program of the Education Department of Zhejiang Province under No. Y202147723 and Y202147457, in part by the National Natural Science Foundation of China uncer Grant 62271448.
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References
Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: International Conference On Machine Learning, pp. 214–223. PMLR (2017)
Commowick, O., Cervenansky, F., Ameli, R.: Msseg challenge proceedings: multiple sclerosis lesions segmentation challenge using a data management and processing infrastructure. In: MICCAI (2016)
Commowick, O., et al.: Multiple sclerosis lesions segmentation from multiple experts: The MICCAI 2016 challenge dataset. Neuroimage 244, 118589 (2021)
Defazio, A., Murrell, T., Recht, M.: MRI banding removal via adversarial training. Adv. Neural. Inf. Process. Syst. 33, 7660–7670 (2020)
Donoho, D.L.: Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006)
Eo, T., Jun, Y., Kim, T., Jang, J., Lee, H.J., Hwang, D.: Kiki-net: cross-domain convolutional neural networks for reconstructing undersampled magnetic resonance images. Magn. Reson. Med. 80(5), 2188–2201 (2018)
Fabian, Z., Heckel, R., Soltanolkotabi, M.: Data augmentation for deep learning based accelerated MRI reconstruction with limited data. In: International Conference on Machine Learning, pp. 3057–3067. PMLR (2021)
Hore, A., Ziou, D.: Image quality metrics: Psnr vs. ssim. In: 2010 20th International Conference On Pattern Recognition, pp. 2366–2369. IEEE (2010)
Huang, J., Chen, C., Axel, L.: Fast multi-contrast MRI reconstruction. Magn. Reson. Imaging 32(10), 1344–1352 (2014)
Jaspan, O.N., Fleysher, R., Lipton, M.L.: Compressed sensing MRI: a review of the clinical literature. Br. J. Radiol. 88(1056), 20150487 (2015)
Jun, Y., Shin, H., Eo, T., Hwang, D.: Joint deep model-based mr image and coil sensitivity reconstruction network (joint-ICNet) for fast MRI. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5270–5279 (2021)
Lai, W.S., Huang, J.B., Ahuja, N., Yang, M.H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 624–632 (2017)
Lai, W.S., Huang, J.B., Ahuja, N., Yang, M.H.: Fast and accurate image super-resolution with deep laplacian pyramid networks. IEEE Trans. Pattern Anal. Mach. Intell. 41(11), 2599–2613 (2019)
Li, X.-X., Chen, Z., Lou, X.-J., Yang, J., Chen, Y., Shen, D.: Multimodal MRI Acceleration via deep cascading networks with peer-layer-wise dense connections. In: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (eds.) MICCAI 2021. LNCS, vol. 12906, pp. 329–339. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87231-1_32
Lustig, M., Donoho, D., Pauly, J.M.: Sparse MRI: The application of compressed sensing for rapid mr imaging. Mag. Res. Med. Off. J. In. Soci. Mag. Res. Med. 58(6), 1182–1195 (2007)
Lyu, Q., et al.: Multi-contrast super-resolution MRI through a progressive network. IEEE Trans. Med. Imaging 39(9), 2738–2749 (2020)
Muckley, M.J., et al.: Results of the 2020 fastMRI challenge for machine learning MR image reconstruction. IEEE Trans. Med. Imaging 40(9), 2306–2317 (2021)
Polak, D., et al.: Joint multi-contrast variational network reconstruction (jvn) with application to rapid 2d and 3d imaging. Magn. Reson. Med. 84(3), 1456–1469 (2020)
Saharia, C., Ho, J., Chan, W., Salimans, T., Fleet, D.J., Norouzi, M.: Image super-resolution via iterative refinement. arXiv preprint arXiv:2104.07636 (2021)
Schlemper, J., Caballero, J., Hajnal, J.V., Price, A., Rueckert, D.: A deep cascade of convolutional neural networks for mr image reconstruction. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 647–658. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59050-9_51
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 (2018)
Sun, L., Fan, Z., Fu, X., Huang, Y., Ding, X., Paisley, J.: A deep information sharing network for multi-contrast compressed sensing MRI reconstruction. IEEE Trans. Image Process. 28(12), 6141–6153 (2019)
Wiatrak, M., Albrecht, S.V., Nystrom, A.: Stabilizing generative adversarial networks: A survey. arXiv preprint arXiv:1910.00927 (2019)
Xiang, L., et al.: Deep-learning-based multi-modal fusion for fast mr reconstruction. IEEE Trans. Biomed. Eng. 66(7), 2105–2114 (2019)
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)
Zhang, Z., Romero, A., Muckley, M.J., Vincent, P., Yang, L., Drozdzal, M.: Reducing uncertainty in undersampled MRI reconstruction with active acquisition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2049–2058 (2019)
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Xing, TY., Li, XX., Chen, ZJ., Zheng, XY., Zhang, F. (2022). Effectively Training MRI Reconstruction Network via Sequentially Using Undersampled k-Space Data with Very Low Frequency Gaps. In: Bansal, M.S., Cai, Z., Mangul, S. (eds) Bioinformatics Research and Applications. ISBRA 2022. Lecture Notes in Computer Science(), vol 13760. Springer, Cham. https://doi.org/10.1007/978-3-031-23198-8_4
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