@inproceedings{56a9b830864141d1a7b13b87fea91b07,
title = "Generating Diffusion MRI Scalar Maps from T1 Weighted Images Using Generative Adversarial Networks",
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.",
keywords = "CycleGAN, Diffusion MRI, Distortion correction, Generative Adversarial Networks",
author = "Xuan Gu and Hans Knutsson and Markus Nilsson and Anders Eklund",
year = "2019",
doi = "10.1007/978-3-030-20205-7_40",
language = "English",
isbn = "9783030202040",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "489--498",
editor = "Michael Felsberg and Per-Erik Forss{\'e}n and Jonas Unger and Ida-Maria Sintorn",
booktitle = "Image Analysis - 21st Scandinavian Conference, SCIA 2019, Proceedings",
address = "Germany",
note = "21st Scandinavian Conference on Image Analysis, SCIA 2019 ; Conference date: 11-06-2019 Through 13-06-2019",
}