Hans Knutsson
Senior lecturer
Generating Diffusion MRI Scalar Maps from T1 Weighted Images Using Generative Adversarial Networks
Author
Editor
- Michael Felsberg
- Per-Erik Forssén
- Jonas Unger
- Ida-Maria Sintorn
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.
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
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