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

Abstract

In this study, hyperpolarized (HP) 129Xe MR ventilation and 1H anatomical images were obtained from 3 subject groups: young healthy volunteers (HV), subjects with chronic obstructive pulmonary disease (COPD), and age-matched control subjects (AMC). Ventilation images were quantified by 2 methods: an expert reader-based ventilation defect score percentage (VDS%) and a semi-automatic segmentation-based ventilation defect percentage (VDP). Reader-based values were assigned by two experienced radiologists and resolved by consensus. In the semi-automatic analysis, 1H anatomical images and 129Xe ventilation images were both segmented following registration, to obtain the thoracic cavity volume (TCV) and ventilated volume (VV), 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 TCV, 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 129Xe ventilation defect scoring metrics readily separated the 3 groups from one another and correlated significantly with 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 (HV and AMC), 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 COPD subjects with similar ventilation defect scores but visibly different ventilation patterns.

Regional Ventilation Changes in Severe Asthma after Bronchial Thermoplasty with3He MR Imaging and CT

“…Uniform ventilation across segments, characteristic of the healthy volunteer population, was indeed revealed by the lower standard deviation across SVPs in healthy volunteers. There were age and body mass index differences between the control group and the severe asthma group; both likely result in our control group having fewer ventilation defects than in a well age-matched group (39). Nevertheless even older healthy volunteers generally have uniform ventilation compared with patients with severe asthma, and we expect that the age differences between control and asthma groups (Tables 1, 2) contribute negligibly to any differences (3,(40)(41)(42).…”

Magnetic resonance imaging with hyperpolarized agents: methods and applications

“…The resulting “ventilation defects” (Figure 4) are influenced by regional airway obstruction and air trapping. [117, 118] The ventilation defect volume [117, 119, 120], ventilated volume [119, 121], and coefficient of variation [122, 123] derived from these images have all proven useful for demonstrating regional heterogeneity of ventilation in a wide range of obstructive lung diseases. Measures of ventilation defects are also sensitive to subclinical decline in function due to aging in healthy never-smokers [118, 123] and in smokers [124–126].…”

“…Recently, acquiring both HP gas and proton images of the total lung volume within the same breath-hold was demonstrated to improve spatial registration [131]. Multiple groups have demonstrated repeatability [120, 129, 132, 133] and validity of the VDP measure in comparison to pulmonary function testing [120, 123, 134–136] and as a biomarker of severity of lung disease [123, 134, 136]. Overall larger values for VDP are observed after inhalation of 129 Xe vs. 3 He in the same individual [137], a bias which is likely attributable to the difference in gas densities (Table 1), although this remains a matter for speculation and further research [138].…”

Semiautomated Ventilation Defect Quantification in Exercise-induced Bronchoconstriction Using Hyperpolarized Helium-3 Magnetic Resonance Imaging

“…On the other hand, in less-diseased cases where defects tended to be more peripherally located, a reader would easily identify and clearly exclude these vessels as illustrated in Figure 5d and f. This implies that readers tend to make an accurate judgment on the tubular vessels in the lightly diseased cases, whereas the semiautomated algorithm may yield a more accurate delineation of large, readily apparent defects. A prior study (30) reported that readers tend to ignore defects that comprise less than 2% of the total volume. However, this may reflect an intrinsic difficulty for semiautomated algorithms to accurately identify ventilation defects in slightly-diseased or healthy lungs.…”

“…Researchers using several semiautomated defect segmentation algorithms (10,13,14,30) all reported different degrees of systematic bias compared to the manual segmentation of HP gas MR images. In our work there is good overall agreement, even for estimates by subregion of the lungs.…”