Generating Diffusion MRI Scalar Maps from T1 Weighted Images Using Generative Adversarial Networks [Elektronisk resurs]

• 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. 

Ämnesord

Engineering and Technology  (hsv)

Medical Engineering  (hsv)

Teknik och teknologier  (hsv)

Medicinteknik  (hsv)

Genre

government publication  (marcgt)

Indexterm och SAB-rubrik

Diffusion MRI

Generative Adversarial Networks

CycleGAN

Distortion correction