Correction of Fat-Water Swaps in Dixon MRI
File(s)
Accepted version
Author(s)
Type
Conference Paper
Abstract
The Dixon method is a popular and widely used technique for fat-water separation in magnetic resonance imaging, and today, nearly all scanner manufacturers are offering a Dixon-type pulse sequence that produces scans with four types of images: in-phase, out-of-phase, fat-only, and water-only. A natural ambiguity due to phase wrapping and local minima in the optimization problem cause a frequent artifact of fat-water inversion where fat- and water-only voxel values are swapped. This artifact affects up to 10 % of routinely acquired Dixon images, and thus, has severe impact on subsequent analysis. We propose a simple yet very effective method, Dixon-Fix, for correcting fat-water swaps. Our method is based on regressing fat- and water-only images from in- and out-of-phase images by learning the conditional distribution of image appearance. The predicted images define the unary potentials in a globally optimal maximum-a-posteriori estimation of the swap labeling with spatial consistency. We demonstrate the effectiveness of our approach on whole-body MRI with various types of fat-water swaps.
Date Issued
2016-10-02
Date Acceptance
2016-06-02
Citation
Lecture Notes in Computer Science, 2016
ISSN
0302-9743
Publisher
Springer Verlag
Journal / Book Title
Lecture Notes in Computer Science
Copyright Statement
© the authors
Source
19th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2016)
Subjects
Artificial Intelligence & Image Processing
08 Information And Computing Sciences
Publication Status
Published
Start Date
2016-10-17
Finish Date
2016-10-21
Coverage Spatial
Athens, Greece
Date Publish Online
2016-10-02