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Hyperpolarized gas MRI can quantify regional lung ventilation via biomarkers, including the ventilation defect percentage (VDP). VDP is computed from segmentations derived from spatially co‐registered functional hyperpolarized gas and structural proton (¹H)‐MRI. Although acquired at similar lung inflation levels, they are frequently misaligned, requiring a lung cavity estimation (LCE). Recently, single‐channel, mono‐modal deep learning (DL)‐based methods have shown promise for pulmonary image segmentation problems. Multichannel, multimodal approaches may outperform single‐channel alternatives.

We hypothesized that a DL‐based dual‐channel approach, leveraging both ¹H‐MRI and Xenon‐129‐MRI (¹²⁹Xe‐MRI), can generate LCEs more accurately than single‐channel alternatives.

Retrospective.

A total of 480 corresponding ¹H‐MRI and ¹²⁹Xe …