3D phase-resolved functional lung (3D-PREFUL) proton MRI enables a radiation-free and non-contrast-enhanced ventilation assessment of human lungs. However, generating high-quality images usually requires a long acquisition time. Acceleration can be achieved by undersampling k-space data, but the resulting violation of the Nyquist theorem leads to image artifacts. Deep learning (DL)-based reconstruction approaches are proposed as a solution for this dilemma. Two novel loss functions are introduced to create a deep learning based reconstruction, optimized for lung MRI. The feasibility of ventilation assessment, including ventilation defect identification, from 8x undersampled MR-images of post-COVID-19 patients, reconstructed by a neural network is demonstrated.
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