Myocardial strain imaging: review of general principles, validation, and sources of discrepancies

Abstract

Myocardial tissue tracking imaging techniques have been developed for a more accurate evaluation of myocardial deformation (i.e. strain), with the potential to overcome the limitations of ejection fraction (EF) and to contribute, incremental to EF, to the diagnosis and prognosis in cardiac diseases. While most of the deformation imaging techniques are based on the similar principles of detecting and tracking specific patterns within an image, there are intra- and inter-imaging modality inconsistencies limiting the wide clinical applicability of strain. In this review, we aimed to describe the particularities of the echocardiographic and cardiac magnetic resonance deformation techniques, in order to understand the discrepancies in strain measurement, focusing on the potential sources of variation: related to the software used to analyse the data, to the different physics of image acquisition and the different principles of 2D vs. 3D approaches. As strain measurements are not interchangeable, it is highly desirable to work with validated strain assessment tools, in order to derive information from evidence-based data. There is, however, a lack of solid validation of the current tissue tracking techniques, as only a few of the commercial deformation imaging softwares have been properly investigated. We have, therefore, addressed in this review the neglected issue of suboptimal validation of tissue tracking techniques, in order to advocate for this matter.

Keywords: cMR; echocardiography; feature tracking; review; speckle tracking imaging; strain; tagging.

Figure 1

General workflow of strain computation.

Figure 2

The principles of tissue tracking techniques illustrated on different imaging modalities. The myocardial speckled pattern (on 2D and 3D echocardiography), or anatomical features (on cine-cMR images), or tagging information (on cMR tagging) are identified within an image and followed over time in the subsequent images of the sequence by searching the most probable pattern correspondence.

Figure 3

Example of differences in global and regional strain estimates (A longitudinal and B circumferential) by different modalities and softwares in a patient with hypertrophic cardiomyopathy. Regional strain values are represented in 17 and 18 segment colour-coded bullseyes plots. Excluded segments due to poor image quality or tracking are not colour-coded.

Figure 4

Influence of temporal resolution on strain measurement (data extrapolated from a high temporal resolution STE image undersampled at lower frame rates).

Figure 5

Influence of tag deposition delay of strain computation in cMR tagging [data extrapolated from a high resolution STE image acquired at high heart rate (120 bpm)].

Figure 6

Influence of endocardial layer position on strain measurements. (Example of different layer positions on endocardial strain in a cMR-FT image).

Figure 7

Difference in Eulerian and Lagrangian strain computation.

Figure 8

Reported normal (mean and 95% confidence interval) global strain values in healthy subjects for different imaging modalities. Data from refs.^,,, Normal GLS, GCS, and GRS values were compared using random effects models weighted by inverse variance and heterogeneity between methods was compared using the Cochran Q test and the inconsistency factor. For all strain measurements, I² and Q indicated significant heterogeneity among studies and methods.