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Background
Fast and accurate T1ρ mapping in myocardium is still a major challenge, particularly in small animal models. The complex sequence design owing to electrocardiogram and respiratory gating leads to quantification errors in in vivo experiments, due to variations of the T\(_{1p}\) relaxation pathway. In this study, we present an improved quantification method for T\(_{1p}\) using a newly derived formalism of a T\(_{1p}\)\(^{*}\) relaxation pathway.
Methods
The new signal equation was derived by solving a recursion problem for spin-lock prepared fast gradient echo readouts. Based on Bloch simulations, we compared quantification errors using the common monoexponential model and our corrected model. The method was validated in phantom experiments and tested in vivo for myocardial T\(_{1p}\) mapping in mice. Here, the impact of the breath dependent spin recovery time T\(_{rec}\) on the quantification results was examined in detail.
Results
Simulations indicate that a correction is necessary, since systematically underestimated values are measured under in vivo conditions. In the phantom study, the mean quantification error could be reduced from − 7.4% to − 0.97%. In vivo, a correlation of uncorrected T\(_{1p}\) with the respiratory cycle was observed. Using the newly derived correction method, this correlation was significantly reduced from r = 0.708 (p < 0.001) to r = 0.204 and the standard deviation of left ventricular T\(_{1p}\) values in different animals was reduced by at least 39%.
Conclusion
The suggested quantification formalism enables fast and precise myocardial T\(_{1p}\) quantification for small animals during free breathing and can improve the comparability of study results. Our new technique offers a reasonable tool for assessing myocardial diseases, since pathologies that cause a change in heart or breathing rates do not lead to systematic misinterpretations. Besides, the derived signal equation can be used for sequence optimization or for subsequent correction of prior study results.
Aims
The role of diastolic dysfunction (DD) in prognostic evaluation in heart failure (HF) patients with impaired systolic function remains unclear. We investigated the impact of echocardiography-defined DD on survival in HF patients with mid-range (HFmrEF, EF 41–49%) and reduced ejection fraction (HFrEF, EF < 40%).
Methods and results
A total of 2018 consecutive hospitalized HF patients were retrospectively included and divided in two groups based on baseline EF: HFmrEF group (n = 951, aged 69 ± 13 years, 74.2% male) and HFrEF group (n = 1067, aged 68 ± 13 years, 76.3% male). Clinical data were collected and analysed. All patients completed ≥1 year clinical follow-up. The primary endpoint was defined as all-cause death (including heart transplantation) and cardiovascular (CV)-related death. All-cause mortality (30.8% vs. 24.9%, P = 0.003) and CV mortality (19.1% vs. 13.5%, P = 0.001) were significantly higher in the HFrEF group than the HFmrEF group during follow-up [median 24 (13–36) months]. All-cause mortality increased in proportion to DD severity (mild, moderate, and severe) in either HFmrEF (17.1%, 25.4%, and 37.0%, P < 0.001) or HFrEF (18.9%, 30.3%, and 39.2%, P < 0.001) patients. The risk of all-cause mortality [hazard ratio (HR) = 1.347, P = 0.015] and CV mortality (HR = 1.508, P = 0.007) was significantly higher in HFrEF patients with severe DD compared with non-severe DD after adjustment for identified clinical and echocardiographic covariates. For HFmrEF patients, severe DD was independently associated with increased all-cause mortality (HR = 1.358, P = 0.046) but not with CV mortality (HR = 1.155, P = 0.469).
Conclusions
Echocardiography-defined severe DD is independently associated with increased all-cause mortality in patients with HFmrEF and HFrEF.