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Semi-automatic cardiac and respiratory gated MRI for cardiac assessment during exercise

Research output: Chapter in Book/Report/Conference proceedingOther chapter contribution

Original languageEnglish
Title of host publicationMolecular Imaging, Reconstruction and Analysis of Moving Body Organs, and Stroke Imaging and Treatment - 5th International Workshop, CMMI 2017 2nd International Workshop, RAMBO 2017 and 1st International Workshop, SWITCH 2017 Held in Conjunction with MICCAI 2017, Proceedings
PublisherSpringer Verlag
Pages86-95
Number of pages10
Volume10555 LNCS
ISBN (Print)9783319675633
DOIs
Publication statusE-pub ahead of print - 9 Sep 2017
Event5th International Workshop on Computational Methods for Molecular Imaging, CMMI 2017, 2nd International Workshop on Reconstruction and Analysis of Moving Body Organs, RAMBO 2017 and 1st International Stroke Workshop on Imaging and Treatment Challenges, SWITCH 2017 held in Conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017 - Quebec City, Canada
Duration: 14 Sep 201714 Sep 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10555 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference5th International Workshop on Computational Methods for Molecular Imaging, CMMI 2017, 2nd International Workshop on Reconstruction and Analysis of Moving Body Organs, RAMBO 2017 and 1st International Stroke Workshop on Imaging and Treatment Challenges, SWITCH 2017 held in Conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017
CountryCanada
CityQuebec City
Period14/09/201714/09/2017

King's Authors

Abstract

Imaging of the heart during exercise can improve detection and treatment of heart diseases but is challenging using current clinically applied cardiac MRI (cMRI) techniques. Real-time (RT) imaging strategies have recently been proposed for exercise cMRI, but respiratory motion and unreliable cardiac gating introduce significant errors in quantification of cardiac function. Self-navigated cMRI sequences are currently not routinely available in a clinical environment. We aim to establish a method for cardiac and respiratory gated cine exercise cMRI that can be applied in a clinical cMRI setting. We developed a retrospective, image-based cardiac and respiratory gating and reconstruction framework based on widely available highly accelerated dynamic imaging. From the acquired dynamic images, respiratory motion was estimated using manifold learning. Cardiac periodicity was obtained by identifying local maxima in the temporal frequency spectrum of the spatial means of the images. We then binned the dynamic images in respiratory and cardiac phases and subsequently registered and averaged them to reconstruct a respiratory and cardiac gated cine stack. We evaluated our method in healthy volunteers and patients with heart diseases and demonstrate good agreement with existing RT acquisitions (R =.82). We show that our reconstruction pipeline yields better image quality and has lower inter- and intra-observer variability compared to RT imaging. Subsequently, we demonstrate that our method is able to detect a pathological response to exercise in patients with heart diseases, illustrating its potential benefit in cardiac diagnostic and prognostic assessment.

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