k-t NEXT: dynamic MR image reconstruction exploiting spatio-temporal correlations

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Authors

  • Chen Qin
  • Jo Schlemper
  • Gavin Seegoolam
  • Anthony Price
  • Joseph Hajnal
  • Daniel Rueckert

Colleges, School and Institutes

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.

Details

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2019
Subtitle of host publication22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part II
EditorsDinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan
Publication statusPublished - 10 Oct 2019

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume11765
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349