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Fast reconstruction of accelerated dynamic MRI using manifold kernel regression

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

Kanwal K. Bhatia, Jose Caballero, Anthony N. Price, Ying Sun, Jo V. Hajnal, Daniel Rueckert

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer-Verlag Berlin Heidelberg
Pages510-518
Number of pages9
Volume9351
ISBN (Print)9783319245737
DOIs
Publication statusPublished - 18 Nov 2015
Event18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015 - Munich, Germany
Duration: 5 Oct 20159 Oct 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9351
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015
CountryGermany
CityMunich
Period5/10/20159/10/2015

King's Authors

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.

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