Deep learning reconstructed 3D zero echo time MRI for lung imaging: a preliminary study

Motivation: Lung MRI using UTE and ZTE techniques is limited by its SNR and tissue interface blurring. Deep learning based reconstruction (DLR) technique has been used to improve MRI image quality via noise reduction. Goal(s): To evaluate potentially clinical applications of breath-hold DLR ZTE lung MRI in ventilation function. Approach: DLR and conventional reconstructed ZTE lung images of thirty patients with pulmonary nodules were compared for image quality and image-based pulmonary ventilation estimation. Results: Compared to conventional reconstructed results, DLR ZTE images demonstrated improved image quality and better correlation with clinical measurements for ventilation estimation. Impact: This preliminary study demonstrated the feasibility of DLR ZTE technique in lung MRI. DLR ZTE images showed improved image quality and better correlation with clinical measurements for ventilation estimation.