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Abstract #0795

MRI2MRI: A deep convolutional network that accurately transforms between brain MRI contrasts

Sa Xiao1, Yue Wu1, Aaron Y Lee1, and Ariel Rokem2

1Ophthalmology, The University of Washington, Seattle, WA, United States, 2eScience Institute, The University of Washington, Seattle, WA, United States

Different brain MRI contrasts represent different tissue properties and are sensitive to different artifacts. The relationship between different contrasts is therefore complex and nonlinear. We developed a deep convolutional network that learns the mapping between different MRI contrasts. Using a publicly available dataset, we demonstrate that this algorithm accurately transforms between T1- and T2-weighted images, proton density images, time-of-flight angiograms, and diffusion MRI images. We demonstrate that these transformed images can be used to improve spatial registration between MR images of different contrasts.

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