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

Chen Qin, Jo Schlemper, Jinming Duan, Gavin Seegoolam, Anthony Price, Joseph V. Hajnal, Daniel Rueckert

Research output: Chapter in Book/Report/Conference proceedingChapter

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.
Original languageEnglish
Title of host publicationMEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2019, PT II
Pages505-513
Volume11765
DOIs
Publication statusPublished - 10 Oct 2019

Publication series

NameLecture Notes in Computer Science

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