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.

Background: Pulmonary transit time (PTT) from first-pass perfusion imaging is a novel parameter to evaluate hemodynamic congestion by cardiac magnetic resonance (cMR). We sought to evaluate the additional prognostic value of PTT in heart failure with reduced ejection fraction over other well-validated predictors of risk including the Meta-Analysis Global Group in Chronic Heart Failure risk score and ischemic cause. Methods: We prospectively followed 410 patients with chronic heart failure with reduced ejection fraction (61±13 years, left ventricular (LV) ejection fraction 24±7%) who underwent a clinical cMR to assess the prognostic value of PTT for a primary endpoint of overall mortality and secondary composite endpoint of cardiovascular death and heart failure hospitalization. Normal reference values of PTT were evaluated in a population of 40 asymptomatic volunteers free of cardiovascular disease. Results PTT was significantly increased in patients with heart failure with reduced ejection fraction as compared to controls (9±6 beats and 7±2 beats, respectively, P <0.001), and correlated not only with New York Heart Association class, cMR–LV and cMR–right ventricular (RV) volumes, cMR-RV and cMR-LV ejection fraction, and feature tracking global longitudinal strain, but also with cardiac output. Over 6-year median follow-up, 182 patients died and 200 reached the secondary endpoint. By multivariate Cox analysis, PTT was an independent and significant predictor of both endpoints after adjustment for Meta-Analysis Global Group in Chronic Heart Failure risk score and ischemic cause. Importantly in multivariable analysis, PTT in beats had significantly higher additional prognostic value to predict not only overall mortality (χ 2 to improve, 12.3; hazard ratio, 1.35 [95% CI, 1.16–1.58]; P <0.001) but also the secondary composite endpoints (χ 2 to improve=20.1; hazard ratio, 1.23 [95% CI, 1.21–1.60]; P <0.001) than cMR-LV ejection fraction, cMR-RV ejection fraction, LV–feature tracking global longitudinal strain, or RV–feature tracking global longitudinal strain. Importantly, PTT was independent and complementary to both pulmonary artery pressure and reduced RV ejection fraction<42% to predict overall mortality and secondary combined endpoints. Conclusions: Despite limitations in temporal resolution, PTT derived from first-pass perfusion imaging provides higher and independent prognostic information in heart failure with reduced ejection fraction than clinical and other cMR parameters, including LV and RV ejection fraction or feature tracking global longitudinal strain. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT03969394.

The high accuracy against sonomicrometry and good agreement of global LS and global CS by 2DST and 2DTagg confirm the overall validity of 2DST strain measurement. Yet, higher intertechnique segmental variability and lower ability for detecting infarct suggest that 2DST strain estimates may be less performant on regional than on global basis.

Cardiac motion atlases provide a space of reference in which the motions of a cohort of subjects can be directly compared. Motion atlases can be used to learn descriptors that are linked to different pathologies and which can subsequently be used for diagnosis. To date, all such atlases have been formed and applied using data from the same modality. In this work we propose a framework to build a multimodal cardiac motion atlas from 3D magnetic resonance (MR) and 3D ultrasound (US) data. Such an atlas will benefit from the complementary motion features derived from the two modalities, and furthermore, it could be applied in clinics to detect cardiovascular disease using US data alone. The processing pipeline for the formation of the multimodal motion atlas initially involves spatial and temporal normalisation of subjects' cardiac geometry and motion. This step was accomplished following a similar pipeline to that proposed for single modality atlas formation. The main novelty of this paper lies in the use of a multi-view algorithm to simultaneously reduce the dimensionality of both the MR and US derived motion data in order to find a common space between both modalities to model their variability. Three different dimensionality reduction algorithms were investigated: principal component analysis, canonical correlation analysis and partial least squares regression (PLS). A leave-one-out cross validation on a multimodal data set of 50 volunteers was employed to quantify the accuracy of the three algorithms. Results show that PLS resulted in the lowest errors, with a reconstruction error of less than 2.3 mm for MR-derived motion data, and less than 2.5  mm for US-derived motion data. In addition, 1000 subjects from the UK Biobank database were used to build a large scale monomodal data set for a systematic validation of the proposed algorithms. Our results demonstrate the feasibility of using US data alone to analyse cardiac function based on a multimodal motion atlas.