Deep Learning Segmentation of Lung Parenchyma For UTE Proton MRI

Accurate segmentation is required to perform quantitative analysis on lung parenchyma in ultrashort echo time (UTE) proton MRI. Deep learning methods offer a solution to this problem, however, previous application to UTE lung MRI is limited. A deep learning model was trained to segment lung parenchyma using fine tuned region growing masks as a reference. To test the generalizability of the model the performance of three different 3D UTE k-space trajectories were compared. Overall, the model produced high quality segmentation for the different acquisition approaches with improvements over training data in areas of high vessel density and high signal intensity.