Extending Semiautomatic Ventilation Defect Analysis for Hyperpolarized 129Xe Ventilation MRI

Rationale and Objectives: Clinical deployment of hyperpolarized Xe-129 magnetic resonance imaging requires accurate quantification and visualization of the ventilation defect percentage (VDP). Here, we improve the robustness of our previous semiautomated analysis-method to reduce operator dependence, correct for B-1 inhomogeneity and vascular structures, and extend the analysis to display multiple intensity clusters. Materials and Methods: Two segmentation methods were compared-a seeded region-growing method, previously validated by expert reader scoring, and a new linear-binning method that corrects the effects of bias field and vascular structures. The new method removes nearly all operator interventions by rescaling the Xe-129 magnetic resonance images to the 99th percentile of the cumulative distribution and applying fixed thresholds to classify Xe-129 voxels into four clusters: defect, low, medium, and high intensity. The methods were applied to 24 subjects including patients with chronic obstructive pulmonary disease (n = 8), age-matched controls (n = 8), and healthy normal subjects (n = 8). Results: Linear-binning enabled a faster and more reproducible workflow and permitted analysis of an additional 0.25 +/- 0.18 L of lung volume by accounting for vasculature. Like region-growing, linear-binning VDP correlated strongly with reader scoring (R-2 = 0.93, P < .0001), but with less systematic bias. Moreover, linear-binning maps clearly depict regions of low and high intensity that may prove useful for phenotyping subjects with chronic obstructive pulmonary disease. Conclusions: Corrected linear-binning provides a robust means to quantify Xe-129 ventilation images yielding VDP values that are indistinguishable from expert reader scores, while exploiting the entire dynamic range to depict multiple image clusters.