A multi-channel deep learning approach for lung cavity estimation using hyperpolarized gas and proton MRI

Joshua Russell Astley, Alberto M Biancardi, Helen Marshall, Paul JC Hughes, Guilhem J Collier, Laurie J Smith, James Eaden, Jim M Wild, Bilal Tahir

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Hyperpolarized (HP) gas MRI enables quantification of regional lung ventilation via clinical biomarkers such as the ventilation defect percentage (VDP). VDP is computed from segmentations derived from spatially co-registered functional HP gas MRI and structural proton ($^1$H)-MRI; although these scans are acquired at similar inflation levels, misalignments are frequent, requiring a lung cavity estimation (LCE). Here, we propose a multi-channel deep learning method for generating LCEs using HP gas and $^1$H-MRI. We compare the performance of the proposed method to single-channel alternatives.
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Thursday 7th July
Poster Session 2.2 - onsite 11:00 - 12:00, virtual 15:20 - 16:20 (UTC+2)
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