Non-invasive estimation of mean pulmonary artery pressure by cardiovascular magnetic resonance in under 2 min scan time

3D Vortex-Energetics in the Left Pulmonary Artery for Differentiating Pulmonary Arterial Hypertension and Pulmonary Venous Hypertension Groups Using 4D Flow MRI

BACKGROUND

Pulmonary hypertension (PH) is a life-threatening. Differentiation pulmonary arterial hypertension (PAH) from pulmonary venous hypertension (PVH) is important due to distinct treatment protocols. Invasive right heart catheterization (RHC) remains the reference standard but noninvasive alternatives are needed.

PURPOSE/HYPOTHESIS

To evaluate 4D Flow MRI-derived 3D vortex energetics in the left pulmonary artery (LPA) for distinguishing PAH from PVH.

STUDY TYPE

Prospective case-control.

POPULATION/SUBJECTS

Fourteen PAH patients (11 female) and 18 PVH patients (9 female) diagnosed from RHC, 23 healthy controls (9 female).

FIELD STRENGTH/SEQUENCE

1.5 T; gradient recalled echo 4D flow and balanced steady-state free precession (bSSFP) cardiac cine sequences.

ASSESSMENT

LPA 3D vortex cores were identified using the lambda2 method. Peak vortex-contained kinetic energy (vortex-KE) and viscous energy loss (vortex-EL) were computed from 4D flow MRI. Left and right ventricular (LV, RV) stroke volume (LVSV, RVSV) and ejection fraction (LVEF, RVEF) were computed from bSSFP. In PH patients, mean pulmonary artery pressure (mPAP), pulmonary capillary wedge pressure (PCWR) and pulmonary vascular resistance (PVR) were determined from RHC.

STATISTICAL TESTS

Mann-Whitney U test for group comparisons, Spearman's rho for correlations, logistic regression for identifying predictors of PAH vs. PVH and develop models, area under the receiver operating characteristic curve (AUC) for model performance. Significance was set at P < 0.05.

RESULTS

PAH patients showed significantly lower vortex-KE (37.14 [14.68-78.52] vs. 76.48 [51.07-120.51]) and vortex-EL (9.93 [5.69-25.70] vs. 24.22 [12.20-32.01]) than PVH patients. The combined vortex-KE and LVEF model achieved an AUC of 0.89 for differentiating PAH from PVH. Vortex-EL showed significant negative correlations with mPAP (rho = -0.43), PCWP (rho = 0.37), PVR (rho = -0.64). In the PAH group, PVR was significantly negatively correlated with LPA vortex-KE (rho = -0.73) and vortex-EL (rho = -0.71), and vortex-KE significantly correlated with RVEF (rho = 0.69), RVSV, (rho = 0.70). In the PVH group, vortex-KE (rho = 0.52), vortex-EL significantly correlated with RVSV (rho = 0.58).

DATA CONCLUSION

These preliminary findings suggest that 4D flow MRI-derived LPA vortex energetics have potential to noninvasively differentiate PAH from PVH and correlate with invasive hemodynamic parameters.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 3.

MR 4D flow-derived left atrial acceleration factor for differentiating advanced left ventricular diastolic dysfunction

OBJECTIVES

The magnetic resonance (MR) 4D flow imaging-derived left atrial (LA) acceleration factor α was recently introduced as a means to non-invasively estimate LA pressure. We aimed to investigate the association of α with the severity of left ventricular (LV) diastolic dysfunction using echocardiography as the reference method.

METHODS

Echocardiographic assessment of LV diastolic function and 3-T cardiac MR 4D flow imaging were prospectively performed in 94 subjects (44 male/50 female; mean age, 62 ± 12 years). LA early diastolic peak outflow velocity (vE), systolic peak inflow velocity (vS), and early diastolic peak inflow velocity (vD) were evaluated from 4D flow data. α was calculated from α = vE / [(vS + vD) / 2]. Mean parameter values were compared by t-test; diagnostic performance of α in predicting diastolic (dys)function was investigated by receiver operating characteristic curve analysis.

RESULTS

Mean α values were 1.17 ± 0.14, 1.20 ± 0.08, 1.33 ± 0.15, 1.77 ± 0.18, and 2.79 ± 0.69 for grade 0 (n = 51), indeterminate (n = 9), grade I (n = 13), grade II (n = 13), and grade III (n = 8) LV diastolic (dys)function, respectively. α differed between subjects with non-advanced (grade < II) and advanced (grade ≥ II) diastolic dysfunction (1.20 ± 0.15 vs. 2.16 ± 0.66, p < 0.001). The area under the curve (AUC) for detection of advanced diastolic dysfunction was 0.998 (95% CI: 0.958-1.000), yielding sensitivity of 100% (95% CI: 84-100%) and specificity of 99% (95% CI: 93-100%) at cut-off α ≥ 1.58. The AUC for differentiating grade III diastolic dysfunction was also 0.998 (95% CI: 0.976-1.000) at cut-off α ≥ 2.14.

CONCLUSION

The 4D flow-derived LA acceleration factor α allows grade II and grade III diastolic dysfunction to be distinguished from non-advanced grades as well as from each other.

CLINICAL RELEVANCE STATEMENT

As a single continuous parameter, the 4D flow-derived LA acceleration factor α shows potential to simplify the multi-parametric imaging algorithm for diagnosis of advanced LV diastolic dysfunction, thereby identifying patients at increased risk for cardiovascular events.

KEY POINTS

• Detection of advanced diastolic dysfunction is typically performed using a complex, multi-parametric approach. • The 4D flow-derived left atrial acceleration factor α alone allows accurate detection of advanced left ventricular diastolic dysfunction. • As a single continuous parameter, the left atrial acceleration factor α could simplify the diagnosis of advanced diastolic dysfunction.