Fitted model parameters were robust to truncation of MBW data. There was also good agreement between predicted and measured MRI indices (mean bias ± limits of agreement: I1/3:−0.003☐.118 and ICV:−0.004☐.298). The MRI indices measured (I1/3, the fraction of pixels below one-third of the mean intensity and ICV, the coefficient of variation of pixel intensity) correlated strongly with those predicted by the MBW model fits (r=0.93,0.88 respectively). The fitted models were used to predict the distribution of gas imaged by ³He ventilation MRI measurements collected from the same visit. These were inferred for each individual from supine MBW data recorded from 25 patients with cystic fibrosis (CF) using approximate Bayesian computation. We developed computer simulations of the ventilation distribution in the lungs to model MBW measurement with 3 parameters: σV, determining the extent of VH V0, the lung volume and VD, the dead-space volume. Here we report the prediction of ventilation distributions from MBW data using a mathematical model, and the comparison of these predictions with imaging data. Indices of ventilation heterogeneity (VH) from multiple breath washout (MBW) have been shown to correlate well with VH indices derived from hyperpolarised gas ventilation MRI. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. on behalf of International Society for Magnetic Resonance in Medicine. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. The Nint = 2 spiral demonstrates the successful acquisition of DXeV images and signal-time curves in healthy subjects and chronic obstructive pulmonary disease patients. The Spearman's correlations of chronic obstructive pulmonary disease subjects were statistically different from three healthy subjects (P < 0.05). Signal-time curves were well matched in three healthy volunteers. The two-interleaved spiral (Nint = 2) was found to be the most time-efficient to obtain DXeV images and signal-time curves of whole lungs with a temporal resolution of 624 ms for 13 slices. DXeV images were also carried out in six subjects (three healthy and three chronic obstructive pulmonary disease subjects).ĭXeV images and numerical modelling of signal-time curves permitted the quantification of temporal and spatial resolutions for different numbers of spiral interleaves. A finite element model was constructed to investigate gas-flow dynamics corroborating the experimental signal-time curves. ![]() DXeV images were acquired from a gas-flow phantom to investigate the ability of Nint = 1, 2, 4, and 8 to capture signal-time curves. Spiral k-space trajectories were designed with the number of interleaves Nint = 1, 2, 4, and 8 corresponding to voxel sizes of 8 mm, 5 mm, 4 mm, and 2.5 mm, respectively, for field of view = 15 cm. To develop and optimize a rapid dynamic hyperpolarized (129) Xe ventilation (DXeV) MRI protocol and investigate the feasibility of capturing pulmonary signal-time curves in human lungs.
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