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k-t NEXT: Dynamic MR Image Reconstruction Exploiting Spatio-Temporal Correlations

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 (MICCAI 2019)


Dynamic magnetic resonance imaging (MRI) exhibits high correlations in k-space and time. In order to accelerate the dynamic MR imaging and to exploit k-t correlations from highly undersampled data, here we propose a novel deep learning based approach for dynamic MR image reconstruction, termed k-t NEXT (k-t NEtwork with X-f Transform). In particular, inspired by traditional methods such as k-t BLAST and k-t FOCUSS, we propose to reconstruct the true signals from aliased signals in x-f domain to exploit the spatio-temporal redundancies. Building on that, the proposed method then learns to recover the signals by alternating the reconstruction process between the x-f space and image space in an iterative fashion. This enables the network to effectively capture useful information and jointly exploit spatio-temporal correlations from both complementary domains. Experiments conducted on highly undersampled short-axis cardiac cine MRI scans demonstrate that our proposed method outperforms the current state-of-the-art dynamic MR reconstruction approaches both quantitatively and qualitatively.

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This work was supported by EPSRC programme grant SmartHeart (EP/P001009/1).

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Correspondence to Chen Qin .

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Qin, C. et al. (2019). k-t NEXT: Dynamic MR Image Reconstruction Exploiting Spatio-Temporal Correlations. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11765. Springer, Cham.

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  • Print ISBN: 978-3-030-32244-1

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