Extending semiautomatic ventilation defect analysis for hyperpolarized (129)Xe ventilation MRI.

RATIONALE AND OBJECTIVES: Clinical deployment of hyperpolarized (129)Xe 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 B1 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 (129)Xe magnetic resonance images to the 99th percentile of the cumulative distribution and applying fixed thresholds to classify (129)Xe 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 (129)Xe ventilation images yielding VDP values that are indistinguishable from expert reader scores, while exploiting the entire dynamic range to depict multiple image clusters.