Advanced

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

Gu, Xuan ; Knutsson, Hans LU ; Nilsson, Markus LU and Eklund, Anders (2019) 21st Scandinavian Conference on Image Analysis, SCIA 2019 In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 11482 LNCS. p.489-498
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... (More)

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.

(Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
CycleGAN, Diffusion MRI, Distortion correction, Generative Adversarial Networks
host publication
Image Analysis - 21st Scandinavian Conference, SCIA 2019, Proceedings
series title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
editor
Felsberg, Michael ; Forssén, Per-Erik ; Unger, Jonas ; Sintorn, Ida-Maria ; ; ; and
volume
11482 LNCS
pages
10 pages
publisher
Springer
conference name
21st Scandinavian Conference on Image Analysis, SCIA 2019
conference location
Norrköping, Sweden
conference dates
2019-06-11 - 2019-06-13
external identifiers
  • scopus:85066885746
ISSN
1611-3349
0302-9743
ISBN
9783030202040
DOI
10.1007/978-3-030-20205-7_40
language
English
LU publication?
yes
id
56a9b830-8641-41d1-a7b1-3b87fea91b07
date added to LUP
2019-06-25 13:15:35
date last changed
2020-01-13 02:07:00
@inproceedings{56a9b830-8641-41d1-a7b1-3b87fea91b07,
  abstract     = {<p>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.</p>},
  author       = {Gu, Xuan and Knutsson, Hans and Nilsson, Markus and Eklund, Anders},
  booktitle    = {Image Analysis - 21st Scandinavian Conference, SCIA 2019, Proceedings},
  editor       = {Felsberg, Michael and Forssén, Per-Erik and Unger, Jonas and Sintorn, Ida-Maria},
  isbn         = {9783030202040},
  issn         = {1611-3349},
  language     = {eng},
  pages        = {489--498},
  publisher    = {Springer},
  series       = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  title        = {Generating Diffusion MRI Scalar Maps from T1 Weighted Images Using Generative Adversarial Networks},
  url          = {http://dx.doi.org/10.1007/978-3-030-20205-7_40},
  doi          = {10.1007/978-3-030-20205-7_40},
  volume       = {11482 LNCS},
  year         = {2019},
}