While an enormous number of studies have documented pathological alterations of the myocardial native longitudinal relaxation time (T1) and the fraction of the extracellular myocardial volume (ECV), it has also become clear that continuously evolving T1 mapping sequence, acquisition and evaluation techniques have a substantial impact on quantitative results, making the translation of reported findings into routine clinical use particularly challenging. To provide a basis for the discussion of pathological myocardial T1 and ECV alterations, the present review aims to summarize the methodological aspects of myocardial T1 mapping along with technical and physiological factors influencing results and normal ranges of myocardial native T1 and ECV reported across studies. 2. Sequences and protocols Numerous techniques have been introduced for the generation of cardiac T1 maps [11-22], each of which possesses specific advantages and disadvantages with respect to acquisition time, spatial resolution, accuracy (systematic bias of estimated T1 compared to true T1), and

Non-invasive identification and differentiation of myocardial diseases represents the primary objectives of cardiac magnetic resonance (CMR) longitudinal relaxation time (T1) and extracellular volume (ECV) mapping. Given the fact that myocardial T1 and ECV values overlap throughout and within left ventricular phenotypes, a central issue to be addressed is whether and to what extent myocardial T1 and ECV mapping provides additional or superior diagnostic information to standard CMR imaging, and whether native T1 mapping could be employed as a non-contrast alternative to late gadolinium enhancement (LE) imaging. The present review aims to summarize physiological and pathophysiological alterations in native T1 and ECV values and summarized myocardial T1 and ECV alterations associated with cardiac diseases to support the translation of research findings into routine CMR imaging.

Purpose To present and validate a method for automated extraction and analysis of the temporal evolution of the mitral valve (MV) vortex ring from MR 4D‐flow data. Methods The proposed algorithm uses the divergence‐free part of the velocity vector field for Q criterion‐based identification and tracking of MV vortex ring core and region within the left ventricle (LV). The 4D‐flow data of 20 subjects (10 healthy controls, 10 patients with ischemic heart disease) were used to validate the algorithm against visual analysis as well as to assess the method’s sensitivity to manual LV segmentation. Quantitative MV vortex ring parameters were analyzed with respect to both their differences between healthy subjects and patients and their correlation with transmitral peak velocities. Results The algorithm successfully extracted MV vortex rings throughout the entire cardiac cycle, which agreed substantially with visual analysis (Cohen’s kappa = 0.77). Furthermore, vortex cores and regions were robustly detected even if a static end‐diastolic LV segmentation mask was applied to all frames (Dice coefficients 0.82 ± 0.08 and 0.94 ± 0.02 for core and region, respectively). Early diastolic MV vortex ring vorticity, kinetic energy and circularity index differed significantly between healthy controls and patients. In contrast to vortex shape parameters, vorticity and kinetic energy correlated strongly with transmitral peak velocities. Conclusion An automated method for temporal MV vortex ring extraction demonstrating robustness with respect to LV segmentation strategies is introduced. Quantitative vortex parameter analysis indicates importance of the MV vortex ring for LV diastolic (dys)function.