@inproceedings{589b1a1b20424012b20106d0647eff49,
title = "Fast reconstruction of accelerated dynamic MRI using manifold kernel regression",
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.",
author = "Bhatia, \{Kanwal K.\} and Jose Caballero and Price, \{Anthony N.\} and Ying Sun and Hajnal, \{Jo V.\} and Daniel Rueckert",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015 ; Conference date: 05-10-2015 Through 09-10-2015",
year = "2015",
doi = "10.1007/978-3-319-24574-4\_61",
language = "English",
isbn = "9783319245737",
series = "Lecture Notes in Computer Science",
publisher = "Springer Verlag",
pages = "510--518",
editor = "Nassir Navab and Joachim Hornegger and Wells, \{William M.\} and Frangi, \{Alejandro F.\}",
booktitle = "Medical Image Computing and Computer-Assisted Intervention {\textendash} MICCAI 2015 - 18th International Conference, Proceedings",
}