Generating Diffusion MRI Scalar Maps from T1 Weighted Images Using Generative Adversarial Networks

Summary, in English

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.

• Author: Xuan Gu; Hans Knutsson; Markus Nilsson; Anders Eklund
• Editor: Michael Felsberg; Per-Erik Forssén; Jonas Unger; Ida-Maria Sintorn
• Department/s: Diagnostic Radiology, (Lund); MR Physics
• Publishing year: 2019
• Language: English
• Pages: 489-498
• Publication/Series: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
• Volume: 11482 LNCS
• Links: http://dx.doi.org/10.1007/978-3-030-20205-7_40
• Document type: Conference paper
• Publisher: Springer
• Topic: Radiology, Nuclear Medicine and Medical Imaging
• Keywords: CycleGAN; Diffusion MRI; Distortion correction; Generative Adversarial Networks
• Conference name: 21st Scandinavian Conference on Image Analysis, SCIA 2019
• Conference date: 2019-06-11 - 2019-06-13
• Conference place: Norrköping, Sweden
• Status: Published
• Research group: MR Physics
• ISBN/ISSN/Other: ISSN: 1611-3349; ISSN: 0302-9743; ISBN: 9783030202040