Echo time-dependent observed T1 and quantitative perfusion in chronic obstructive pulmonary disease using magnetic resonance imaging

Abstract

Introduction: Due to hypoxic vasoconstriction, perfusion is interesting in the lungs. Magnetic Resonance Imaging (MRI) perfusion imaging based on Dynamic Contrast Enhancement (DCE) has been demonstrated in patients with Chronic Obstructive Pulmonary Diseases (COPD) using visual scores, and quantification methods were recently developed further. Inter-patient correlations of echo time-dependent observed T₁ [T₁(TE)] have been shown with perfusion scores, pulmonary function testing, and quantitative computed tomography. Here, we examined T₁(TE) quantification and quantitative perfusion MRI together and investigated both inter-patient and local correlations between T₁(TE) and quantitative perfusion.

Methods: 22 patients (age 68.0 ± 6.2) with COPD were examined using morphological MRI, inversion recovery multi-echo 2D ultra-short TE (UTE) in 1-2 slices for T₁(TE) mapping, and 4D Time-resolved angiography With Stochastic Trajectories (TWIST) for DCE. T₁(TE) maps were calculated from 2D UTE at five TEs from 70 to 2,300 μs. Pulmonary Blood Flow (PBF) and perfusion defect (QDP) maps were produced from DCE measurements. Lungs were automatically segmented on UTE images and morphological MRI and these segmentations registered to DCE images. DCE images were separately registered to UTE in corresponding slices and divided into corresponding subdivisions. Spearman's correlation coefficients were calculated for inter-patient correlations using the entire segmented slices and for local correlations separately using registered images and subdivisions for each TE. Median T₁(TE) in normal and defect areas according to QDP maps were compared.

Results: Inter-patient correlations were strongest on average at TE₂ = 500 μs, reaching up to |ρ| = 0.64 for T₁ with PBF and |ρ| = 0.76 with QDP. Generally, local correlations of T₁ with PBF were weaker at TE₂ than at TE₁ or TE₃ and with maximum values of |ρ| = 0.66 (from registration) and |ρ| = 0.69 (from subdivision). In 18 patients, T₁ was shorter in defect areas than in normal areas, with the relative difference smallest at TE₂.

Discussion: The inter-patient correlations of T₁ with PBF and QDP found show similar strength and TE-dependence as those previously reported for visual perfusion scores and quantitative computed tomography. The local correlations and median T₁ suggest that not only base T₁ but also the TE-dependence of observed T₁ in normal areas is closer to that found previously in healthy volunteers than in defect areas.

Keywords: T1 mapping; chronic obstructive pulmonary disease; dynamic contrast enhancement; functional lung imaging; lung T1; magnetic resonance imaging; quantitative perfusion.

Figure 1

Example horizontal (A) and vertical (B) deformation field as determined by image registration, scaled in number of voxels. Segmentations with subdivided areas for a DCE image (C) and the corresponding 2D UTE image (D). Areas that correspond to each other in the subdivision are drawn in identical colors.

Figure 2

(A) Original image from DCE MRI, averaged over all timepoints and three slices. (B) The same image, registered to the UTE image. (C) Image derived from inversion recovery UTE that was used as registration target. (D) PBF map after application of the distortion field produced by the image registration, with perfusion defects delineated in blue. (E) T₁ map acquired using UTE at TE₁ = 70 μs. (F) T₁ map at TE₂ = 500 μs.

Figure 3

Spearman’s correlation coefficients for the correlation of T₁(TE) with PBF, (A) voxel-wise correlation based on image registration and (B) based on subdivisions, respectively. Each patient is shown with the same color in both graphs.

Figure 4

(A) Relative difference of T₁ at TE₁ = 70 μs between voxels classified as perfusion defect and voxels classified as normal. Each point represents one individual. (B) Mean of individual median T₁ over all patients at each TE, according to defect classification.