Objectives: The aim of this study was to perform a systematic review and meta-analysis to estimate the normal ranges of magnetic resonance imaging (MRI)-based feature tracking (FT) and to identify sources of variations. Similar analyses were also performed for strain encoding, displacement encoding with stimulated echoes, and myocardial tagging.

Background: MRI-FT is a novel technique for quantification of myocardial deformation using MRI cine images. However, the reported 95% confidence intervals (CIs) from the 2 largest studies have no overlaps.

Methods: Four databases (EMBASE, SCOPUS, PUBMED, and Web of Science) were systematically searched for MRI strains of the left (LV) and right (RV) ventricles. The key terms for MRI-FT were "tissue tracking," "feature tracking," "cardiac magnetic resonance," "cardiac MRI," "CMR," and "strain." A random effects model was used to pool LV global longitudinal strain (GLS), global circumferential strain (GCS), global radial strain (GRS), and RVGLS. Meta-regressions were used to identify the sources of variations.

Results: 659 healthy subjects were included from 18 papers for MRI-FT. Pooled mean of LVGLS was -20.1% (95% CI: -20.9% to -19.3%), LVGCS -23% (95% CI: -24.3% to -21.7%), LVGRS 34.1% (95% CI: 28.5% to 39.7%), and RVGLS -21.8% (95% CI: -23.3% to -20.2%). Although there were no publication biases except for LVGCS, significant heterogeneities were found. Meta-regression showed that variation of LVGCS was associated with field strength (β = 3.2; p = 0.041). Variations of LVGLS, LVGRS, and RVGLS were not associated with any of age, sex, software, field strength, sequence, LV ejection fraction, or LV size. LVGCS seems the most robust in MRI-FT. Among the MRI-derived strain techniques, the normal ranges were mostly concordant in LVGLS and LVGCS but varied substantially in LVGRS and RVGLS.

Conclusions: The pooled means of 4 MRI-derived myocardial strain methods in normal subjects are demonstrated. Differences in field strength were attributed to variations of LVGCS.

Item Type:	Refereed Article
Keywords:	DENSE, SENC, feature tracking, healthy, magnetic resonance, meta-analysis, normal range, strain, tagging
Research Division:	Biomedical and Clinical Sciences
Research Group:	Cardiovascular medicine and haematology
Research Field:	Cardiology (incl. cardiovascular diseases)
Objective Division:	Health
Objective Group:	Clinical health
Objective Field:	Clinical health not elsewhere classified
UTAS Author:	Vo, HQ (Mr Quang Vo)
UTAS Author:	Marwick, TH (Professor Tom Marwick)
UTAS Author:	Negishi, K (Dr Kazuaki Negishi)
ID Code:	126648
Year Published:	2018
Web of Science® Times Cited:	85
Deposited By:	Menzies Institute for Medical Research
Deposited On:	2018-06-20
Last Modified:	2022-08-25
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