Generating Diffusion MRI scalar maps from T1 weighted images using generative adversarial networks
This work addresses the high cost and time of collecting diffusion MRI data for medical imaging researchers, but it is incremental as it applies an existing GAN method to a new data type.
The authors tackled the problem of generating diffusion MRI scalar maps from T1-weighted images using Generative Adversarial Networks (GANs), specifically CycleGAN, to produce synthetic fractional anisotropy (FA) and mean diffusivity (MD) maps in a single step, and demonstrated their application in correcting geometric distortions in diffusion MRI through non-linear registration.
Diffusion magnetic resonance imaging (diffusion MRI) is a non-invasive microstructure assessment technique. Scalar measures, such as FA (fractional anisotropy) and MD (mean diffusivity), 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. Here, we therefore demonstrate how Generative Adversarial Networks (GANs) can be used to generate synthetic diffusion scalar measures from structural T1-weighted images in a single optimized step. Specifically, we train the popular CycleGAN model to learn to map a T1 image to FA or MD, and vice versa. As an application, we show that synthetic FA images can be used as a target for non-linear registration, to correct for geometric distortions common in diffusion MRI.