Pulmonary 129 Xe MRI: CNN Registration and Segmentation to Generate Ventilation Defect Percent with Multi-center Validation.

Rationale and Objectives: Hyperpolarized ¹²⁹ Xe MRI quantifies ventilation-defect-percent (VDP), the ratio of ¹²⁹ Xe signal-void to the anatomic ¹ H MRI thoracic-cavity-volume. VDP is associated with airway inflammation and disease control and serves as a treatable trait in therapy studies. Semi-automated VDP pipelines require time-intensive observer interactions. Current convolutional neural network (CNN) approaches for quantifying VDP lack external validation, which limits multicenter utilization. Our objective was to develop an automated and externally validated deep-learning pipeline to quantify pulmonary ¹²⁹ Xe MRI VDP.
Materials and Methods: ¹ H and ¹²⁹ Xe MRI data from the primary site (Site1) were used to train and test a CNN segmentation and registration pipeline, while two independent sites (Site2 and Site3) provided external validation. Semi-automated and CNN-based registration error was measured using mean-absolute-error (MAE) while segmentation error was measured using generalized-Dice-similarity coefficient (gDSC). CNN and semi-automated VDP were compared using linear regression and Bland-Altman analysis.
Results: Training/testing used data from 205 participants (healthy volunteers, asthma, COPD, long-COVID; mean age=54 ± 16y; 119 females) from Site1. External validation used data from 71 participants. CNN and semi-automated ¹ H and ¹²⁹ Xe registrations agreed (MAE=0.3°, R ² =0.95 rotation; 1.1%, R ² =0.79 scaling; 0.2/0.5px, R ² =0.96/0.95, x/y-translation; all p < .001). Thoracic-cavity and ventilation segmentations were also spatially corresponding (gDSC=0.92 and 0.88, respectively). CNN VDP correlated with semi-automated VDP (Site1 R ² /ρ = .97/.95, bias=-0.5%; Site2 R ² /ρ = .85/.93, bias=-0.9%; Site3 R ² /ρ = .95/.89, bias=-0.8%, all p < .001).
Conclusion: An externally validated CNN registration/segmentation model demonstrated strong agreement with low error compared to the semi-automated method. CNN and semi-automated registrations, thoracic-cavity-volume and ventilation-volume segmentations were highly correlated with high gDSC for the datasets.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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