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Abstract #1178

Ventilation Defect Synthesis in Hyperpolarized 129Xe Ventilation MRI to Accelerate Training of Segmentation Models

Suphachart Leewiwatwong1, Junlan Lu2, Jesse Zhang3, David Mummy4, Isabelle Dummer4,5, Kevin Yarnall6, Ziyi Wang1, and Bastiaan Driehuys1,2,4
1Biomedical Engineering, Duke University, Durham, NC, United States, 2Medical Physics, Duke University, Durham, NC, United States, 3Mathematics, Duke University, Durham, NC, United States, 4Radiology, Duke University, Durham, NC, United States, 5Bioengineering, McGill University, Montréal, QC, Canada, 6Mechanical Engineering and Materials Science, Duke University, Durham, NC, United States


Quantification of 129Xe MRI relies on accurate segmentation of the thoracic cavity. This segmentation could potentially be performed directly on the 129Xe ventilation image using an automated convolutional neural network, but this task is challenging, especially in cases where peripheral ventilation defects obscure the lung boundary. Currently, overcoming this obstacle requires large, diverse training datasets created by time-consuming manual segmentation. Here, we demonstrate the use of a generative Pix2Pix model to synthesize both 129Xe images with defects, and corresponding segmentation masks. We then test the effects of this additional training data on the performance of an existing U-net segmentation algorithm.

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