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Abstract #4501

Improving Xenon-129 Lung Ventilation Image Quality with a Commercial Deep-Learning Based Image Reconstruction

Neil J Stewart1, Jose de Arcos2, Alberto M Biancardi1, Jemima H Pilgrim-Morris1, Oliver I Rodgers1, Ryan S Munro1, Guilhem J Collier1, Graham Norquay1, Helen Marshall1, Anja Brau3, Marc Lebel4, and Jim M Wild1
1The University of Sheffield, Sheffield, United Kingdom, 2GE Healthcare, Cambridge, United Kingdom, 3GE Healthcare, San Francisco, CA, United States, 4GE Healthcare, Calgary, AB, Canada

Synopsis

Keywords: Hyperpolarized MR (Gas), Hyperpolarized MR (Gas)The utility of a deep learning based reconstruction tool for improving the quality of hyperpolarized 129Xe lung ventilation images was assessed. DL-reconstructed 129Xe ventilation image quality and SNR was improved compared with conventionally reconstructed images. In a cohort of patients with asthma and/or COPD, a small bias towards increased ventilation defect percentage, and a bias towards decreased coefficient of variation, in DL-reconstructed vs. conventionally-reconstructed images, was observed. Initial feasibility of utilising this tool for reduced-cost 129Xe ventilation imaging using natural-abundance xenon, and improved spatial resolution imaging with 129-enriched xenon, is demonstrated.

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Keywords