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

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.