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Abstract #1183

A general framework of synthesizing 129Xe MRI data for improved segmentation model training

Junlan Lu1, Jesse Zhang2, Suphachart Leewiwatwong3, Isabelle Dummer4, David Mummy5, and Bastiaan Driehuys3
1Medical Physics, Duke University, Durham, NC, United States, 2Mathematics, Duke University, Durham, NC, United States, 3Biomedical Engineering, Duke University, Durham, NC, United States, 4Biomedical Engineering, McGill University, Montreal, QC, Canada, 5Radiology, Duke University, Durham, NC, United States


Quantitative analysis of hyperpolarized 129Xe MRI, segmentation of the thoracic cavity, a crucial step that is often the bottleneck in an otherwise fully automated pipeline. This problem is attractive to solve using deep learning methods, but they are limited by their large appetite for manually segmented training data. To this end, we propose a method to automatically synthesize both 129Xe ventilation MR images and their corresponding thoracic cavity masks using general adversarial networks. This data augmentation technique can accelerate the training of deep learning segmentation models.

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