Influence of observer experience on cardiac magnetic resonance strain measurements using feature tracking and conventional tagging

Abstract

Aim: CMR quantitative myocardial strain analysis is increasingly being utilized in clinical routine. CMR feature tracking (FT) is now considered an alternative to the reference standard for strain assessment -CMR tagging. The impact of observer experience on the validity of FT results has not yet been investigated. The aim of this study was therefore to evaluate the observer experience-dependency of CMR FT and to compare results with the reference standard.

Methods: CSPAMM and SSFP-Cine sequences were acquired in 38 individuals (19 patients with HFpEF,19 controls) in identical midventricular short-axis locations. Global peak systolic circumferential strain (PSCS) together with LV ejection fraction (EF) and volumes were assessed by three observers (5,3 and 0 years of CMR-strain experience). Intermodality, intra- as well inter-observer variability were assessed.

Results: Correlation between tagging and FT derived PSCS depended on observer experience (r = 0.69, r = 0.58 and r = 0.53). For the inexperienced observer tagging and FT derived PSCS differed significantly (p = 0.0061). Intra-observer reproducibility of tagging derived PSCS were similar for all observers (coefficient of variation (CV): 6%, 6.8% and 4.9%) while reproducibility of FT derived PSCS (CV: 7.4%, 9.4% and 15.8%) varied depending on observer experience. Inter-observer reproducibility of tagging derived PSCS for observer 1 and 2 as well as 1 and 3 for tagging (CV: 6.17%, 9.18%) was superior in comparison to FT (CV: 11.8%, 16.4%).

Conclusions: Reliability and accuracy of FT based strain analysis, more than tagging based strain analysis, is dependent on reader experience. CMR strain experience or dedicated training in strain evaluation is necessary for FT to deliver accurate strain data, comparable to that of CMR tagging.

Fig. 1

Example of tagging (upper images: 1A–2C) and Feature Tracking (lower images: 3A-4C) derived strain assessment in a healthy volunteer completed by each of the three observers (A: experienced reader; B: intermediately experienced reader; C: inexperienced reader). Contour lines are placed in a diastolic image in a cspamm and SSFP image (1A&3A). The respective software (tagging and FT) propagates the contour throughout the cardiac cycle (2A–C&4A–C), however corrections may be necessary. Tagging and Feature Tracking derived strain curves (1D & 3D) for observer 1 (blue graph), observer 2 (red graph) and observer 3(yellow graph). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 2

Inter-observer correlation between observers 1 and 2 (tagging: A; Feature Tracking C) as well as 1 and 3 (tagging: B; Feature Tracking: D) for PSCS in healthy volunteers (blue dots) and in patients with HFPEF (red triangles). Bland-Altman Plots for interobserver agreement between observers 1 and 2/1 and 3 for tagging (E/F) and Feature Tracking (G/H) derived PSCS, the plots show higher inter-observer agreement for tagging derived PSCS. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 3

Bland-Altman Plots for inter-observer agreement between observers 1 and 2/ 1 and 3 for tagging (A/B) and Feature Tracking (C/D) derived PSCS, the plots show higher inter-observer agreement for tagging derived PSCS.