Generating Diffusion MRI scalar maps from T1 weighted images using generative adversarial networks

TL;DR

This paper presents a method using GANs, specifically CycleGAN, to generate diffusion MRI scalar maps from T1-weighted images, reducing the need for costly diffusion data and aiding in distortion correction.

Contribution

It introduces a novel application of CycleGAN to synthesize diffusion scalar maps from structural MRI, enabling improved image registration and distortion correction.

Findings

CycleGAN successfully maps T1 images to FA and MD maps.

Synthetic FA images improve non-linear registration accuracy.

The method reduces reliance on expensive diffusion MRI acquisitions.

Abstract

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.