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. 2013 Apr;26(4):424-35.
doi: 10.1002/nbm.2880. Epub 2012 Oct 13.

Quantitative analysis of hyperpolarized 129Xe ventilation imaging in healthy volunteers and subjects with chronic obstructive pulmonary disease

Rohan S Virgincar  1 , Zackary I Cleveland, S Sivaram Kaushik, Matthew S Freeman, John Nouls, Gary P Cofer, Santiago Martinez-Jimenez, Mu He, Monica Kraft, Jan Wolber, H Page McAdams, Bastiaan Driehuys
Affiliations

Quantitative analysis of hyperpolarized 129Xe ventilation imaging in healthy volunteers and subjects with chronic obstructive pulmonary disease

Rohan S Virgincar et al. NMR Biomed. 2013 Apr.

Abstract

In this study, hyperpolarized (129) Xe MR ventilation and (1) H anatomical images were obtained from three subject groups: young healthy volunteers (HVs), subjects with chronic obstructive pulmonary disease (COPD) and age-matched controls (AMCs). Ventilation images were quantified by two methods: an expert reader-based ventilation defect score percentage (VDS%) and a semi-automated segmentation-based ventilation defect percentage (VDP). Reader-based values were assigned by two experienced radiologists and resolved by consensus. In the semi-automated analysis, (1) H anatomical images and (129) Xe ventilation images were both segmented following registration to obtain the thoracic cavity volume and ventilated volume, respectively, which were then expressed as a ratio to obtain the VDP. Ventilation images were also characterized by generating signal intensity histograms from voxels within the thoracic cavity volume, and heterogeneity was analyzed using the coefficient of variation (CV). The reader-based VDS% correlated strongly with the semi-automatically generated VDP (r = 0.97, p < 0.0001) and with CV (r = 0.82, p < 0.0001). Both (129) Xe ventilation defect scoring metrics readily separated the three groups from one another and correlated significantly with the forced expiratory volume in 1 s (FEV1 ) (VDS%: r = -0.78, p = 0.0002; VDP: r = -0.79, p = 0.0003; CV: r = -0.66, p = 0.0059) and other pulmonary function tests. In the healthy subject groups (HVs and AMCs), the prevalence of ventilation defects also increased with age (VDS%: r = 0.61, p = 0.0002; VDP: r = 0.63, p = 0.0002). Moreover, ventilation histograms and their associated CVs distinguished between subjects with COPD with similar ventilation defect scores, but visibly different ventilation patterns.

Keywords: 129Xe; COPD; MRI; coefficient of variation; defect; hyperpolarized; segmentation; ventilation.

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Figures

Figure 1
Figure 1
Semi-automated analysis flowchart. Original 129Xe ventilation images (a) were registered to the 1H SSFP images (b), and then segmented to obtain an initial ventilation mask (c). Airways were removed from (c) to obtain the final ventilation mask (d) from which the ventilated volume (VV) was calculated. Original 1H SSFP anatomical images (e) were segmented to obtain an initial thoracic cavity mask (f). This mask was morphologically closed to obtain the final thoracic cavity mask (g) from which the thoracic cavity volume (TCV) was calculated. VV (white) and TCV (red) were then combined to map ventilation defects (h).
Figure 2
Figure 2
Representative 129Xe ventilation (left) and 1H SSFP anatomical image (right) slices of three healthy volunteers. Arrows identify small ventilation defects that sometimes observed even in healthy subjects.
Figure 3
Figure 3
Representative 129Xe ventilation (left) and 1H SSFP anatomical image (right) slices of three age-matched control subjects. Arrows identify small ventilation defects that sometimes observed even in healthy subjects.
Figure 4
Figure 4
Representative 129Xe ventilation (left) and 1H SSFP anatomical image (right) slices of three COPD subjects. Note, a variety of ventilation defects are observed throughout the lungs.
Figure 5
Figure 5
Group-wise comparison of (a) VDS% and VDP (b) CV for all subjects (open symbols). Error bars are 95% confidence intervals about the group mean (closed symbols). For VDS% and VPD, all groups are significantly different from one another. For CV, the HV and AMC groups are significantly different from the COPD group but not from each other. P-values for all comparisons are provided in Table 1.
Figure 6
Figure 6
Correlation of the expert reader-based VDS% with (a) VDP [VDP = 0.49×VDS% + 1.30; R2 = 0.94; p < 0.0001] and (b) CV [CV = 0.008×VDS% + 0.382; R2 = 0.68; p < 0.0001]; (c) Bland-Altman plot for VDS% and VDP for all subjects. The solid line indicates the mean difference between VDS% and VDP (6.07 ± 10.15) and the dotted lines indicate 95% limits of agreement (lower limit: -13.8, upper limit: 26.0).
Figure 7
Figure 7
Representative central slices of 129Xe ventilation images (upper panel) and normalized, whole-lung histograms (lower panel) from a HV (a) and 4 COPD subjects (b-e) ranked in increasing order of the CV. Note, subjects b-e have a similar VDS% (45-50), and VDP (21-23%) but markedly different histograms shapes.
Figure 8
Figure 8
Correlation of VDP and CV with FEV1 (a, c) and with subject age (b, d).
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