Keywords: Machine Learning/Artificial Intelligence, Lung3D-phase-resolved-functional-lung (3D-PREFUL) MRI enables a non-contrast-enhanced detection of lung ventilation defects. Convolutional, recurrent neural networks (CRNN) can accelerate data acquisition by reconstructing dynamic lung images from undersampled data. However, a remaining bias in reconstruction led to incorrect detection of ventilation defects. Reducing the number of respiratory phases per reconstruction step improved the accuracy of the defect detection. This improvement is demonstrated by comparison of 2 complementary ventilation defect metrics derived from the original data and the 2x, 4x and 6x undersampled data of Asthma, COPD and post-COVID-19 patients reconstructed from a CRNN in partitions of 30,20 and 10 respiratory phases.
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