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Abstract #3459

Unsupervised Domain Adaptation for Neural Network Enhanced Turbo Spin Echo Imaging

Zechen Zhou1, Peter Börnert2, and Chun Yuan3
1Philips Research North America, Seattle, WA, United States, 2Philips Research Hamburg, Hamburg, Germany, 3Vascular Imaging Lab, University of Washington, Seattle, WA, United States


Supervised learning is widely used for deep learning based image quality enhancement for improved clinical diagnosis. However, the difficulties to acquire a large number of high-quality reference image for different MR applications can limit its generalization performance. An unsupervised domain adaptation (DA) approach is proposed and incorporated into the deep learning based image enhancement framework, which improves the performance of trained network on new datasets. Preliminary evaluation on point spread function enhanced turbo spin echo imaging has showed that the unsupervised DA approach can provide more stabilized image sharpness improvement without severe amplified noise.

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