Fast reconstruction of accelerated dynamic MRI using manifold kernel regression
Bhatia KK, Caballero J, Price AN, Sun Y, Hajnal JV, Rueckert D (2015)
Publication Type: Conference contribution
Publication year: 2015
Journal
Publisher: Springer Verlag
Book Volume: 9351
Pages Range: 510-518
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Munich, DEU
ISBN: 9783319245737
DOI: 10.1007/978-3-319-24574-4_61
Abstract
We present a novel method for fast reconstruction of dynamic MRI from undersampled k-space data, thus enabling highly accelerated acquisition. The method is based on kernel regression along the manifold structure of the sequence derived directly from k-space data. Unlike compressed sensing techniques which require solving a complex optimisation problem, our reconstruction is fast, taking under 5 seconds for a 30 frame sequence on conventional hardware. We demonstrate our method on 10 retrospectively undersampled cardiac cine MR sequences, showing improved performance over state-of-the-art compressed sensing.
Involved external institutions
Imperial College London / The Imperial College of Science, Technology and Medicine
United Kingdom (GB)
King’s College London
United Kingdom (GB)
National University of Singapore (NUS)
Singapore (SG)
United Kingdom (GB)
King’s College London
United Kingdom (GB)
National University of Singapore (NUS)
Singapore (SG)
How to cite
APA:
Bhatia, K.K., Caballero, J., Price, A.N., Sun, Y., Hajnal, J.V., & Rueckert, D. (2015). Fast reconstruction of accelerated dynamic MRI using manifold kernel regression. In Alejandro F. Frangi, Nassir Navab, Joachim Hornegger, William M. Wells (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 510-518). Munich, DEU: Springer Verlag.
MLA:
Bhatia, Kanwal K., et al. "Fast reconstruction of accelerated dynamic MRI using manifold kernel regression." Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015, Munich, DEU Ed. Alejandro F. Frangi, Nassir Navab, Joachim Hornegger, William M. Wells, Springer Verlag, 2015. 510-518.
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