Machine learning in cardiovascular magnetic resonance: Basic concepts and applications

Tim Leiner*, Daniel Rueckert, Avan Suinesiaputra, Bettina Baeßler, Reza Nezafat, Ivana Išgum, Alistair A. Young

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

142 Citations (Scopus)

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.

Original languageEnglish
Article number61
JournalJournal of Cardiovascular Magnetic Resonance
Volume21
Issue number1
DOIs
Publication statusPublished - 7 Oct 2019

Keywords

  • Cardiovascular magnetic resonance
  • Deep learning
  • Machine learning
  • Radiomics

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