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

  • Chen QinEmail author
  • Jo Schlemper
  • Jinming Duan
  • Gavin Seegoolam
  • Anthony Price
  • Joseph Hajnal
  • Daniel Rueckert
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11765)

Abstract

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.

Notes

Acknowledgement

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

Supplementary material

490275_1_En_56_MOESM1_ESM.zip (1.6 mb)
Supplementary material 1 (zip 1612 KB)

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Chen Qin
    • 1
    Email author
  • Jo Schlemper
    • 1
  • Jinming Duan
    • 1
    • 3
  • Gavin Seegoolam
    • 1
  • Anthony Price
    • 2
  • Joseph Hajnal
    • 2
  • Daniel Rueckert
    • 1
  1. 1.Department of ComputingImperial College LondonLondonUK
  2. 2.Division of Imaging Sciences and Biomedical Engineering DepartmentKing’s College London, St. Thomas’ HospitalLondonUK
  3. 3.School of Computer ScienceUniversity of BirminghamBirminghamUK

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