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Abstract #0391

Deep learning-based thoracic cavity segmentation for hyperpolarized 129Xe MRI

Suphachart Leewiwatwong1, Junlan Lu2, David Mummy3, Isabelle Dummer3,4, Kevin Yarnall5, Ziyi Wang1, and Bastiaan Driehuys1,2,3
1Biomedical Engineering, Duke University, Durham, NC, United States, 2Medical Physics, Duke University, Durham, NC, United States, 3Radiology, Duke University, Durham, NC, United States, 4Bioengineering, McGill University, Montréal, QC, Canada, 5Mechanical Engineering and Materials Science, Duke University, Durham, NC, United States

Quantifying hyperpolarized 129Xe MRI of pulmonary ventilation and gas exchange requires accurate segmentation of the thoracic cavity. This is typically done either manually or semi-automatically using an additional proton scan volume-matched to the gas image. These methods are prone to operator subjectivity, image artifacts, alignment/registration issues, and SNR. Here we demonstrate using a 3D convolutional neural network (CNN) to automatically and directly delineate the thoracic cavity from 129Xe MRI alone. This 3D-CNN uses a combination of Dice-Focal, perceptual loss, and training with template-based data augmentation to demonstrate thoracic cavity segmentation with a Dice score of 0.955 vs. expert readers.

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