Abstract
Diffusion magnetic resonance imaging (diffusion MRI) is a non-invasive microstructure assessment method. Scalar measures quantifying micro-structural tissue properties can be obtained using diffusion models and data processing pipelines. However, it is costly and time consuming to collect high quality diffusion data. We demonstrate how Generative Adversarial Networks (GANs) can be used to generate diffusion scalar measures from structural MR images in a single optimized step, without diffusion models and diffusion data. We show that the used Cycle-GAN model can synthesize visually realistic and quantitatively accurate diffusion-derived scalar measures.
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URL
http://arxiv.org/abs/1810.02683