Perfusion quantification using voxel-wise proton density and median signal decay in PREFUL MRI

Contrast-free lung MRI based on Fourier decomposition is an attractive method to monitor various lung diseases. However, the accuracy of the current perfusion quantification is limited. In this study, a new approach for perfusion quantification based on voxel-wise proton density and median signal decay toward the steady state for Fourier decomposition-based techniques is proposed called Q_Quantified (Q_Quant ).

Twenty patients with chronic obstructive pulmonary disease and 18 patients with chronic thromboembolic pulmonary hypertension received phase-resolved functional lung-MRI (PREFUL) and dynamic contrast-enhanced (DCE)-MRI. Nine healthy participants received phase-resolved functional lung-MRI only. Median values of Q_Quant were compared to a Fourier decomposition perfusion quantification presented by Kjørstad et al (Q_Kjørstad ) and validated toward pulmonary blood flow derived by DCE-MRI (PBF_DCE ). Blood fraction maps determined by the new approach were calculated. Regional and global correlation coefficients were calculated, and Bland-Altman plots were created. Histogram analyses of all cohorts were created.

The introduced parameter Q_Quant showed only 2 mL/min/100 mL mean deviation to PBF_DCE in the patient cohort and showed less bias than Q_Kjørstad . Significant increases of regional correlation with PBF_DCE were achieved (r = 0.3 vs. r = 0.2, P < .01*). The trend of global correlation toward PBF_DCE is not uniform, showing higher values for Q_Kjørstad in the chronic obstructive pulmonary disease cohort than for Q_Quant and vice versa in the chronic thromboembolic pulmonary hypertension cohort. In contrast to Q_Kjørstad , Q_Quant perfusion maps indicate a physiologic dorsoventral gradient in supine position similar to PBF_DCE with similar value distribution in the histograms.

We proposed a new approach for perfusion quantification of phase-resolved functional lung measurements. The developed parameter Q_Quant reveals a higher accuracy compared to Q_Kjørstad .