E9 - A hybrid model- and deep learning-based framework for functional lung image synthesis from non-contrast multi-inflation CT

Joshua Russell Astley, Alberto M Biancardi, Helen Marshall, Guilhem J Collier, Paul JC Hughes, Jim M Wild, Bilal A Tahir

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Hyperpolarized gas MRI can visualize and quantify regional lung ventilation with exquisite detail but requires highly specialized equipment and exogenous contrast. Alternative, non-contrast techniques, including CT-based models of ventilation have shown moderate spatial correlations with hyperpolarized gas MRI. Here, we propose a hybrid framework that integrates CT-ventilation modelling and deep learning approaches. The hybrid model/DL framework generated synthetic ventilation images which accurately replicated gross ventilation defects in hyperpolarized gas MRI scans, significantly outperforming other model- and DL-only approaches. Our results show that a synergy between conventional CT-ventilation modelling and DL can improve the performance of functional lung image synthesis.
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Thursday 8th July
E4-12 (short): Image Registration / Synthesis - 13:45 - 14:30 (UTC+2)
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