4D magnetic resonance flow imaging for estimating pulmonary vascular resistance in pulmonary hypertension

• Published in: Journal of magnetic resonance imaging 2016-10, Vol.44 (4), p.914-922
• Main Authors: Kheyfets, Vitaly O., Schafer, Michal, Podgorski, Chris A., Schroeder, Joyce D., Browning, James, Hertzberg, Jean, Buckner, J. Kern, Hunter, Kendal S., Shandas, Robin, Fenster, Brett E.
• Format: Article
• Language: English
• Subjects: 4D MRI; Blood Flow Velocity; Female; Humans; Hypertension, Pulmonary - diagnostic imaging; Hypertension, Pulmonary - physiopathology; Image Interpretation, Computer-Assisted - methods; Magnetic Resonance Angiography - methods; Magnetic resonance imaging; Magnetic Resonance Imaging, Cine - methods; Male; Middle Aged; NMR; Nuclear magnetic resonance; Pulmonary arteries; Pulmonary Artery - diagnostic imaging; Pulmonary Artery - physiopathology; Pulmonary Circulation; Pulmonary hypertension; pulmonary vascular resistance; Reproducibility of Results; Sensitivity and Specificity; Vascular Resistance
• ISSN: 1053-1807; 1522-2586
• DOI: 10.1002/jmri.25251

Purpose To develop an estimate of pulmonary vascular resistance (PVR) using blood flow measurements from 3D velocity‐encoded phase contract magnetic resonance imaging (here termed 4D MRI). Materials and Methods In all, 17 patients with pulmonary hypertension (PH) and five controls underwent right heart catheterization (RHC), 4D and 2D Cine MRI (1.5T) within 24 hours. MRI was used to compute maximum spatial peak systolic vorticity in the main pulmonary artery (MPA) and right pulmonary artery (RPA), cardiac output, and relative area change in the MPA. These parameters were combined in a four‐parameter multivariate linear regression model to arrive at an estimate of PVR. Agreement between model predicted and measured PVR was also evaluated using Bland–Altman plots. Finally, model accuracy was tested by randomly withholding a patient from regression analysis and using them to validate the multivariate equation. Results A decrease in vorticity in the MPA and RPA were correlated with an increase in PVR (MPA: R2 = 0.54, P < 0.05; RPA: R2 = 0.75, P < 0.05). Expanding on this finding, we identified a multivariate regression equation that accurately estimates PVR (R2 = 0.94, P < 0.05) across severe PH and normotensive populations. Bland–Altman plots showed 95% of the differences between predicted and measured PVR to lie within 1.49 Wood units. Model accuracy testing revealed a prediction error of ∼20%. Conclusion A multivariate model that includes MPA relative area change and flow characteristics, measured using 4D and 2D Cine MRI, offers a promising technique for noninvasively estimating PVR in PH patients. J. MAGN. RESON. IMAGING 2016;44:914–922.