Deep learning–based synthesis of hyperpolarized gas MRI ventilation from 3D multi-inflation proton MRI
Joshua Russell Astley, Alberto M Biancardi, Helen Marshall, Laurie J Smith, Paul JC Hughes, Guilhem J Collier, Matthew Q Hatton, Jim M Wild, Bilal Tahir
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Hyperpolarized (HP) gas MRI allows visualization and quantification of regional lung ventilation; however, there is limited clinical uptake due to the requirement for highly specialized equipment and exogenous contrast agents. Alternative, non-contrast, model-based proton ($^1$H)-MRI surrogates of ventilation, which correlate moderately with HP gas MRI, have been proposed. Recently, deep learning (DL)-based methods have been used for the synthesis of HP gas MRI from free-breathing $^1$H-MRI for a single 2D section. Here, we developed and evaluated a multi-channel 3D DL method that combines modeling and data-driven approaches to synthesize HP gas MRI ventilation scans from multi-inflation $^1$H-MRI.
Wednesday 6th July
Poster Session 1.2 - onsite 11:00 - 12:00, virtual 15:20 - 16:20 (UTC+2)