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

Qin C, Schlemper J, Duan J, Seegoolam G, Price A, Hajnal J, Rueckert D (2019)

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Publication Type: Conference contribution

Publication year: 2019

Journal

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 11765 LNCS

Pages Range: 505-513

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Shenzhen, CHN

ISBN: 9783030322441

DOI: 10.1007/978-3-030-32245-8_56

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.

Involved external institutions

How to cite

APA:

Qin, C., Schlemper, J., Duan, J., Seegoolam, G., Price, A., Hajnal, J., & Rueckert, D. (2019). k-t NEXT: Dynamic MR Image Reconstruction Exploiting Spatio-Temporal Correlations. In Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 505-513). Shenzhen, CHN: Springer Science and Business Media Deutschland GmbH.

MLA:

Qin, Chen, et al. "k-t NEXT: Dynamic MR Image Reconstruction Exploiting Spatio-Temporal Correlations." Proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Shenzhen, CHN Ed. Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou, Springer Science and Business Media Deutschland GmbH, 2019. 505-513.

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