# Comparison of Signal‐ and Volume‐Based Ventilation‐Weighted Assessment Using 3D FLORET UTE MRI in Patients With Various Pulmonary Disease

## TL;DR

This study compares two MRI methods for assessing lung ventilation and finds that the signal-based method correlates better with a gold-standard technique.

## Contribution

The paper introduces a direct comparison of signal- and volume-based proton MRI methods for ventilation assessment using FLORET acquisition.

## Key findings

- Signal-based and volume-based methods showed significant differences in mean and coefficient of variation (p < 0.0001).
- Signal-based ventilation correlated more strongly with 129Xe MRI than volume-based ventilation (ρ = 0.78 vs. 0.34).
- Defect maps from both methods had a median spatial overlap of 86%.

## Abstract

3D free‐breathing, proton, contrast‐agent‐free MR methods are increasingly used for pulmonary ventilation‐weighted measurements. The methods are split between: (1) signal‐based, which rely on lung parenchyma signal changes during respiration, and (2) volume‐based that utilize the Jacobian determinant of deformation fields from the image registration. This study compares both proton methods using respiratory‐resolved images acquired using fermat‐looped orthogonally encoded trajectories (FLORET) acquisition.

Free‐breathing FLORET data were acquired from participants with various pulmonary conditions (N = 29) and healthy controls (N = 7), and reconstructed into respiratory phase‐resolved images. Signal‐based regional ventilation (RVent) was quantified using the 3D phase‐resolved functional lung algorithm, and volume‐based Jacobian ventilation (JVent) was derived as the Jacobian of the deformation field from the direct image registration of the end‐expiratory image to the end‐inspiratory image. Differences between the means, coefficients of variation (CoVs), and their ventilation defect percent (VDP) were quantified by Bland–Altman plots. The spatial overlap of the defect maps was determined by multi‐class Sørensen–Dice coefficient, and Spearman correlations to 129Xe MRI were assessed.

In all study participants, statistically significant differences were found between means/CoVs of RVent and JVent parameters (both p < 0.0001), but not VDP (p = 0.38). The median spatial overlap of the defect maps was 86%. VDPRVent showed stronger correlation (ρ = 0.78, Meng Z = 4.36, p < 0.0001) to VDP129Xe than JVent (ρ = 0.34).

Although both proton lung MRI methods successfully identified ventilation defects, the stronger correlation between signal‐based and 129Xe MRI indicates that RVent may provide a more reliable assessment of lung ventilation in clinical applications in comparison to volume‐based parameters.