Cardiovascular magnetic resonance imaging of myocardium and pulmonary arterial pressure

Heart failure (HF) is a clinical syndrome characterized by high morbidity and mortality. HF can be associated with alteration in myocardial tissue composition such as focal myocardial fibrosis, also referred to as scarring, as well as the development of pulmonary hypertension (PH). The prevalence of HF and PH is increasing worldwide, mainly due to the aging population. This emphasizes the need for early diagnosis, identification of underlying etiology and appropriate treatment. Cardiovascular magnetic resonance (CMR) is not only the reference standard for the assessment of cardiac morphology, function, and myocardial viability, but can also provide precise quantitative information on myocardial tissue composition and cardiovascular hemodynamics. In study I, we found that synthetic late gadolinium enhancement (SynLGE) generated from post-contrast T1 mapping was highly accurate in identifying both ischemic and non-ischemic focal myocardial fibrosis. This method proved to be a valuable complement to conventional late gadolinium enhancement (LGE) imaging. Study II, employed T1 mapping to quantify the dynamic distribution of an intravenous iron substitution agent, ferric carboxymaltose, into different tissues including the myocardium. This understanding is of value for determining the potential benefits of iron substitution therapy in patients with HF. In studies III and IV we investigated the application of the compressed sensing (CS) acceleration technique to multi-two-dimensional (CS-M2D) and true fourdimensional (4D) flow image acquisition by CMR. CS enabled a substantial reduction in image acquisition time while maintaining accuracy in determining mean pulmonary artery pressure (mPAP). Moreover, in study IV, we validated CSM2D and CS-4D flow and achieved excellent agreement in mPAP estimation when compared to invasively measured mPAP by right heart catheterization as the reference standard. In conclusion, this thesis highlights the importance of quantitative CMR in understanding etiological and pathophysiological aspects in HF. These findings are valuable for the early detection of HF and PH and show promise for guiding therapeutic decision to prevent disease progression and complications in patients with HF

Stationary tissue background correction increases the precision of clinical evaluation of intra-cardiac shunts by cardiovascular magnetic resonance

We aimed to evaluate the clinical utility of stationary tissue background phase correction for affecting precision in the measurement of Qp/Qs by cardiovascular magnetic resonance (CMR). We enrolled consecutive patients (n = 91) referred for CMR at 1.5T without suspicion of cardiac shunt, and patients (n = 10) with verified cardiac shunts in this retrospective study. All patients underwent phase contrast flow quantification in the ascending aorta and pulmonary trunk. Flow was quantified using two semi-automatic software platforms (SyngoVia VA30, Vendor 1; Segment 2.0R4534, Vendor 2). Measurements were performed both uncorrected and corrected for linear (Vendor 1 and Vendor 2) or quadratic (Vendor 2) background phase. The proportion of patients outside the normal range of Qp/Qs was compared using the McNemar's test. Compared to uncorrected measurements, there were fewer patients with a Qp/Qs outside the normal range following linear correction using Vendor 1 (10% vs 18%, p < 0.001), and Vendor 2 (10% vs 18%, p < 0.001), and following quadratic correction using Vendor 2 (7% vs 18%, p < 0.001). No patient with known shunt was reclassified as normal following stationary background correction. Therefore, we conclude that stationary tissue background correction reduces the number of patients with a Qp/Qs ratio outside the normal range in a consecutive clinical population, while simultaneously not reclassifying any patient with known cardiac shunts as having a normal Qp/Qs. Stationary tissue background correction may be used in clinical patients to increase diagnostic precision

Blood correction reduces variability and gender differences in native myocardial T1 values at 1.5 T cardiovascular magnetic resonance – a derivation/validation approach

Abstract Background Myocardial native T1 measurements are likely influenced by intramyocardial blood. Since blood T1 is both variable and longer compared to myocardial T1, this will degrade the precision of myocardial T1 measurements. Precision could be improved by correction, but the amount of correction and the optimal blood T1 variables to correct with are unknown. We hypothesized that an appropriate correction would reduce the standard deviation (SD) of native myocardial T1. Methods Consecutive patients (n = 400) referred for CMR with known or suspected heart disease were split into a derivation cohort for model construction (n = 200, age 51 ± 18 years, 50% male) and a validation cohort for assessing model performance (n = 200, age 48 ± 17 years, 50% male). Exclusion criteria included focal septal abnormalities. A Modified Look-Locker inversion recovery sequence (MOLLI, 1.5 T Siemens Aera) was used to acquire T1 and T1* maps. T1 and T1* maps were used to measure native myocardial T1, and blood T1 and T1*. A multivariate linear regression correction model was implemented using blood measurement of R1 (1/T1), R1* (1/T1*) or hematocrit. The correction model from the derivation cohort was applied to the validation cohort, and assessed for reduction in variability with the F-test. Results Blood [LV + RV] mean R1, mean R1* and hematocrit correlated with myocardial T1 (Pearson’s r, range 0.37 to 0.45, p < 0.05 for all) in both the derivation and validation cohorts respectively, suggesting that myocardial T1 measurements are influenced by intramyocardial blood. Mean myocardial native T1 did not differ between the derivation and validation cohorts (1030 ± 42.6 ms and 1023 ± 45.2 ms respectively, p = 0.07). In the derivation cohort, correction using blood mean R1 and mean R1* yielded a decrease in myocardial T1 SD (45.2 ms to 36.6 ms, p = 0.03). When the model from the derivation cohort was applied to the validation cohort, the SD reduction was maintained (39.3 ms, p = 0.049). This 13% reduction in measurement variability leads to a 23% reduction in sample size to detect a 50 ms difference in native myocardial T1. Conclusions Correcting native myocardial T1 for R1 and R1* of blood improves the precision of myocardial T1 measurement by ~13%, and could consequently improve disease detection and reduce sample size needs for clinical research