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

Abstract

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: R(2) = 0.54, P < 0.05; RPA: R(2) = 0.75, P < 0.05). Expanding on this finding, we identified a multivariate regression equation that accurately estimates PVR (R(2) = 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.

Keywords: 4D MRI; pulmonary hypertension; pulmonary vascular resistance.

Figure 1

Example of the natural log transformation normalizing the PVR distribution. (a) A highly positively skewed distribution across the entire cohort. (b) Normalization of measured PVR in the entire cohort.

Figure 2

Streamline generation from the 4D-MRI velocity field. Flow evaluation plane was positioned 1 cm above the pulmonic valve.

Figure 3

Example velocity streamlines generated using 4D MRI, for a sample Normotensive and PH patient, at 5 points in the cardiac cycle. The plane shown is located 1 cm downstream of the pulmonary valve. Time points t₁-t₅ represent instances after the R-wave from EKG: t₁ = 0.023 ms, t₂ = 0.071 ms, t₃ = 0.118 ms, t₄ = 0.165 ms, t₅ = 0.213 ms.

Figure 4

Vorticity in the MPA (left, orange) and RPA (right, blue) correlated against PVR (P < 0.05). In both cases, vorticity is decreased proportional with PVR increase, while the magnitude of vorticity decreases from the MPA to RPA.

Figure 5

(a) Multi-variate regression between RHC measured PVR vs. a function of vorticity in the MPA (ω_MPA), RPA (ω_RPA), cardiac output (CO), and the relative area change in the MPA. (b) Bland-Altman plot of the difference between RHC and the multi-variate model PVR, with the mean±2SD at 0±0.4.

Figure 6

Monte Carlo results for the first 100 simulations of a validation study. Each point represents the relative error between the predicted PVR and measured PVR for a single randomly chosen subject that is excluded from the cohort at each simulation. The mean error for 1000 simulations is 19±17% (±SD). Where the mean is shows as a solid line while standard deviations as dashed lines.

Figure 7

The mean relative error of 100 Monte Carlo simulations vs. the number of randomly selected patients used to generate the multi-variate model. This data proves that increasing the number of patients used for generating the multi-variate model decreased the error of the estimate.