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. 2021 May 13;23(1):54.
doi: 10.1186/s12968-021-00742-3.

Multivendor comparison of global and regional 2D cardiovascular magnetic resonance feature tracking strains vs tissue tagging at 3T

Sebastian Militaru  1   2 , Roman Panovsky  3   4 , Vincent Hanet  1   2 , Mihaela Silvia Amzulescu  1   2 , Hélène Langet  5 , Mary Mojica Pisciotti  3 , Anne-Catherine Pouleur  1   2 , Jean-Louis J Vanoverschelde  1   2 , Bernhard L Gerber  6   7
Affiliations

Multivendor comparison of global and regional 2D cardiovascular magnetic resonance feature tracking strains vs tissue tagging at 3T

Sebastian Militaru et al. J Cardiovasc Magn Reson. .

Abstract

Background: Cardiovascular magnetic resonance (CMR) 2D feature tracking (FT) left ventricular (LV) myocardial strain has seen widespread use to characterize myocardial deformation. Yet, validation of CMR FT measurements remains scarce, particularly for regional strain. Therefore, we aimed to perform intervendor comparison of 3 different FT software against tagging.

Methods: In 61 subjects (18 healthy subjects, 18 patients with chronic myocardial infarction, 15 with dilated cardiomyopathy, and 10 with LV hypertrophy due to hypertrophic cardiomyopathy or aortic stenosis) were prospectively compared global (G) and regional transmural peak-systolic Lagrangian longitudinal (LS), circumferential (CS) and radial strains (RS) by 3 FT software (cvi42, Segment, and Tomtec) among each other and with tagging at 3T. We also evaluated the ability of regional LS, CS, and RS by different FT software vs tagging to identify late gadolinium enhancement (LGE) in the 18 infarct patients.

Results: GLS and GCS by all 3 software had an excellent agreement among each other (ICC = 0.94-0.98 for GLS and ICC = 0.96-0.98 for GCS respectively) and against tagging (ICC = 0.92-0.94 for GLS and ICC = 0.88-0.91 for GCS respectively), while GRS showed inconsistent agreement between vendors (ICC 0.10-0.81). For regional LS, the agreement was good (ICC = 0.68) between 2 vendors but less vs the 3rd (ICC 0.50-0.59) and moderate to poor (ICC 0.44-0.47) between all three FT software and tagging. Also, for regional CS agreement between 2 software was higher (ICC = 0.80) than against the 3rd (ICC = 0.58-0.60), and both better agreed with tagging (ICC = 0.70-0.72) than the 3rd (ICC = 0.57). Regional RS had more variation in the agreement between methods ranging from good (ICC = 0.75) to poor (ICC = 0.05). Finally, the accuracy of scar detection by regional strains differed among the 3 FT software. While the accuracy of regional LS was similar, CS by one software was less accurate (AUC 0.68) than tagging (AUC 0.80, p < 0.006) and RS less accurate (AUC 0.578) than the other two (AUC 0.76 and 0.73, p < 0.02) to discriminate segments with LGE.

Conclusions: We confirm good agreement of CMR FT and little intervendor difference for GLS and GCS evaluation, with variable agreement for GRS. For regional strain evaluation, intervendor difference was larger, especially for RS, and the diagnostic performance varied more substantially among different vendors for regional strain analysis.

Keywords: Feature tracking; Magnetic resonance imaging; Strain; Tagging.

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Conflict of interest statement

None.

Figures

Fig. 1
Fig. 1
Example of strain analysis by tagging and the 3 feature tracking (FT) software in a patient with a lateral myocardial infarction
Fig. 2
Fig. 2
Example of global longitudinal strain (GLS), global circumferential strain (GCS) and global radial strain (GRS) in a typical healthy subject, a patient with myocardial infarction (ISCH), a patient with dilated cardiomyopathy (DCM) (c) and a patient with left ventricular cardiomyopathy (LVH)
Fig. 3
Fig. 3
Average normal GLS GCS and GRS by different software in healthy subjects. *: p < 0.001 vs cvi42, Segment and tagging. #: p < 0.05 vs cvi42 and Tagging
Fig. 4
Fig. 4
Bullseye showing the mean ± SD of normal values of regional longitudinal strain (LS) (a) circumferential strain (CS) (b) and radial strain (RS) (c) values by tagging and the 3 different FT software in healthy subjects
Fig. 5
Fig. 5
Scatter and Bland–Altman plots for comparisons between (a) GLS and (b) GCS by different FT software against tagging and among each other
Fig. 5
Fig. 5
Scatter and Bland–Altman plots for comparisons between (a) GLS and (b) GCS by different FT software against tagging and among each other
Fig. 6
Fig. 6
Bullseyes graphs showing the intraclass correlation coefficient at regional level between FT and tagging for LS (a), CS (b) and RS (c) in the study population
Fig. 6
Fig. 6
Bullseyes graphs showing the intraclass correlation coefficient at regional level between FT and tagging for LS (a), CS (b) and RS (c) in the study population
Fig. 7
Fig. 7
Receiver operating characteristics curve analysis comparing diagnostic abilities of detection of scar (any LGE) of infarcted segments by regional LS, CS, and RS by tagging and the 3 FT software
See this image and copyright information in PMC

References

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