@inproceedings{9a74c291909c47e9b15a43add9a98e9c,
title = "k-t NEXT: dynamic MR image reconstruction exploiting spatio-temporal correlations",
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.",
author = "Chen Qin and Jo Schlemper and Jinming Duan and Gavin Seegoolam and Anthony Price and Joseph Hajnal and Daniel Rueckert",
year = "2019",
month = oct,
day = "10",
doi = "10.1007/978-3-030-32245-8_56",
language = "English",
isbn = "9783030322441",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "505--513",
editor = "Shen, {Dinggang } and Liu, {Tianming } and Peters, {Terry M. } and Staib, {Lawrence H. } and Essert, {Caroline } and Zhou, {Sean } and Yap, {Pew-Thian } and Khan, {Ali }",
booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2019",
edition = "1",
}