Universität zu Köln

Leiner, Tim ORCID: 0000-0003-1885-5499, Rueckert, Daniel, Suinesiaputra, Avan, Baessler, Bettina ORCID: 0000-0002-3244-3864, Nezafat, Reza, Isgum, Ivana and Young, Alistair A. (2019). Machine learning in cardiovascular magnetic resonance: basic concepts and applications. J. Cardiov. Magn. Reson., 21 (1). LONDON: BMC. ISSN 1532-429X

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Abstract

Machine learning (ML) is making a dramatic impact on cardiovascular magnetic resonance (CMR) in many ways. This review seeks to highlight the major areas in CMR where ML, and deep learning in particular, can assist clinicians and engineers in improving imaging efficiency, quality, image analysis and interpretation, as well as patient evaluation. We discuss recent developments in the field of ML relevant to CMR in the areas of image acquisition & reconstruction, image analysis, diagnostic evaluation and derivation of prognostic information. To date, the main impact of ML in CMR has been to significantly reduce the time required for image segmentation and analysis. Accurate and reproducible fully automated quantification of left and right ventricular mass and volume is now available in commercial products. Active research areas include reduction of image acquisition and reconstruction time, improving spatial and temporal resolution, and analysis of perfusion and myocardial mapping. Although large cohort studies are providing valuable data sets for ML training, care must be taken in extending applications to specific patient groups. Since ML algorithms can fail in unpredictable ways, it is important to mitigate this by open source publication of computational processes and datasets. Furthermore, controlled trials are needed to evaluate methods across multiple centers and patient groups.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Leiner, Tim UNSPECIFIED orcid.org/0000-0003-1885-5499 UNSPECIFIED Rueckert, Daniel UNSPECIFIED UNSPECIFIED UNSPECIFIED Suinesiaputra, Avan UNSPECIFIED UNSPECIFIED UNSPECIFIED Baessler, Bettina UNSPECIFIED orcid.org/0000-0002-3244-3864 UNSPECIFIED Nezafat, Reza UNSPECIFIED UNSPECIFIED UNSPECIFIED Isgum, Ivana UNSPECIFIED UNSPECIFIED UNSPECIFIED Young, Alistair A. UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-131297
DOI:	10.1186/s12968-019-0575-y
Journal or Publication Title:	J. Cardiov. Magn. Reson.
Volume:	21
Number:	1
Date:	2019
Publisher:	BMC
Place of Publication:	LONDON
ISSN:	1532-429X
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language LATE GADOLINIUM ENHANCEMENT; K-T BLAST; ARTIFICIAL-INTELLIGENCE; TEXTURE ANALYSIS; MYOCARDIAL SEGMENTATION; NEURAL-NETWORKS; LEFT-VENTRICLE; INFARCTION; IMAGES; MRI Multiple languages Cardiac & Cardiovascular Systems; Radiology, Nuclear Medicine & Medical Imaging Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/13129