Synopsis

Keywords: Lung, Quantitative Imaging, T1 mapping, perfusion quantification, UTE

Observed TE-dependent T1 has been shown to correlate with MRI perfusion scores. Here, 22 patients with COPD were examined using DCE MRI and 2D UTE. Perfusion and T1(TE) was quantified based on these, respectively. Correlations of T1(TE) with perfusion measures were examined, with the strongest inter-patient correlation at TE₂=500μs but stronger local correlations at TE₁=70μs and TE₃=1200μs than at TE₂. The average T1 in defect areas according to DCE was shorter than in normal areas, with the smallest difference at TE₂. This implies that the local TE-dependence in defect voxels is further away from previously published behaviour in healthy volunteers.

Introduction

Diagnosis and monitoring of chronic obstructive pulmonary disease (COPD) is commonly focused on pulmonary function testing and computed tomography. MRI has been proposed to evaluate functional impairment of the lungs. The most widely practiced approach is Dynamic Contrast Enhanced (DCE) MRI to evaluate lung perfusion, which reflects both the destruction of the pulmonary vasculature and lung ventilation defects in response to hypoxic vasoconstriction. Visual perfusion scores have been shown to correlate with disease severity but require trained radiologists and are limited in spatial and numerical resolution^1,2. Pulmonary blood flow (PBF) and the extent of perfusion defects (QDP) using DCE have been demonstrated³. Alternatively, shorter lung T1 was found in patients with COPD and CF as well as inter-patient correlations with perfusion scores^4,5. It was subsequently demonstrated that observed lung T1 (T1(TE)) depends on TE, which was assumed to reflect proton compartments inside each voxel, since the inter-patient correlations of T1(TE) and other metrics varied with TE in patients^6,7,8. In order to investigate this approach further, this work focused on local, voxel-wise comparison of T1-mapping and automated quantitative evaluation of DCE- MRI in COPD patients.

Methods

22 patients (GOLD stage 0-4, age 68.0±6.2) with COPD from a prospective cohort study were included. Patients gave informed consent and the study was approved by the local ethical board. Measurements were performed on a 1.5T scanner (Magnetom Aera, Siemens Healthineers, Erlangen, Germany). Patients were examined using an MRI protocol which included morphological imaging, Time-resolved angiography With Stochastic Trajectories (TWIST) for DCE imaging and inversion recovery multi-echo 2D UTE for T1(TE) mapping⁹. For DCE, 3D images were acquired during contrast injection at 1.7s time-resolution. 2D UTE was run directly before contrast injection in free breathing, with 5 echoes at TE_1-5 = 70μs, 500µs, 1200µs, 1650µs, and 2300µs with 1-2 coronal slices, matrix size 128×128, Field of View 500×500×15mm³. 6000 radial spokes with an adiabatic inversion pulse every 300 spokes were acquired including 3s delay before inversions. Spokes were sorted by inversion time (TI) and reconstructed along a sliding window with 120 spokes width and 50% overlap using a non-uniform Fourier transform. T1(TE) maps were calculated based on these UTE images10. DCE and UTE images were segmented separately. DCE segmentation was based on additional morphological images³. PBF maps were calculated from DCE images by automatically detecting an arterial input function and applying a deconvolution. Otsu's method was applied to the maxima of the residual of this deconvolution to determine two thresholds, which split the lungs into normal and perfusion defect voxels. The fraction of defect voxels in these classification maps was defined as QDP. Only the averaged partitions in DCE images corresponding to UTE slices were used. DCE and UTE images were averaged over their time-courses and registered. The resulting transformation fields were applied to PBF and defect classification maps. Spearman's correlation coefficients were calculated for inter-patient correlations for median T1(TE) with PBF and QDP as well as local correlations of T1(TE) with PBF. T1(TE) in defect and normal areas according to classification maps was determined and compared using Wilcoxon rank-sum tests.

Results

Figure 1 shows an example set of base images and parameter maps in one patient. The inter-patient correlation coefficients were ρ_IP1-5=0.56, 0.64, 0.59, 0.60, 0.29 at TE_1-5 for T1(TE) with median PBF and ρ_IQ1-5=-0.56, -0.76, -0.63, -0.53, -0.26 with QDP. The average local correlation coefficients of T1(TE) with PBF were ρ_LP1-5=0.44, 0.39, 0.42, 0.39, 0.24. Figure 2 shows the coefficients for individual patients. At TE₁=70µs, in 18 out of 22 patients, T1 was shorter in defect areas compared to normal areas. When considered over all patients, T1 was shorter on average at all TE in defect areas. As shown in Figure 3, the relative difference in mean T1 in these areas averaged over all patients was smaller at TE₂ than at TE₁ and TE₃. Visual inspection of parameter maps suggested that stronger local correlations appeared in those patients where areas with recognizable perfusion and longer T1 remained.

Discussion

Inter-patient correlations of T1(TE) with perfusion measures showed the strongest correlation at TE₂=500µs. This agrees well with previously published inter-patient correlations between T1(TE) and clinical metrics, quantitative CT and MRI perfusion scores⁸. However, local correlations instead showed the strongest correlations at TE₁=70µs and weaker correlations at TE₂ than at TE3=1200µs. This coincides with the relative difference of T1 in normal and defect areas being smallest at TE₂. Since it was previously shown that T1 increases with TE more slowly in healthy volunteers than in patients with COPD⁸, this supports the hypothesis that the TE-dependence of T1 in normal areas is closer to that in healthy subjects than in defect areas: At TE₂, the differentiation between both is weakest. This implies that the disease state locally affects both baseline T1 as well as its TE-dependence. Altogether, these results are promising and further research is warranted to investigate the potential clinical value of quantitative T1(TE) imaging for regional lung function assessment.

Acknowledgements

No acknowledgement found.

References

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