The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

 Hans Knutsson . Photo

Hans Knutsson

Senior lecturer

 Hans Knutsson . Photo

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

Author

  • Xuan Gu
  • Hans Knutsson
  • Markus Nilsson
  • Anders Eklund

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