Phase sensitive reconstruction for water/fat separation in MR imaging using inverse gradient

Authors:
Joakim Rydell;Hans Knutsson;Johanna Pettersson;Andreas Johansson;Gunnar Farnebäck;Olof Dahlqvist;Peter Lundberg;Fredrik Nyström;Magnus Borga
Affiliations:
Center for Medical Image Science and Visualization and Department of Biomedical Engineering, Linköping University, Sweden;Center for Medical Image Science and Visualization and Department of Biomedical Engineering, Linköping University, Sweden;Center for Medical Image Science and Visualization and Department of Biomedical Engineering, Linköping University, Sweden;Department of Biomedical Engineering, Linköping University, Sweden;Department of Biomedical Engineering, Linköping University, Sweden;Center for Medical Image Science and Visualization and Department of Medicine and Care, Linköping University, Sweden;Center for Medical Image Science and Visualization and Department of Medicine and Care, Linköping University, Sweden;Department of Endocrinology and Metabolism, Linköping University, Sweden;Center for Medical Image Science and Visualization and Department of Biomedical Engineering, Linköping University, Sweden
Venue:
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Year:
2007

Citing 2
Cited 0

The variational approach to shape from shading

Computer Vision, Graphics, and Image Processing
Phase unwrapping of MR phase images using Poisson equation

IEEE Transactions on Image Processing

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Abstract

This paper presents a novel method for phase unwrapping for phase sensitive reconstruction in MR imaging. The unwrapped phase is obtained by integrating the phase gradient by solving a Poisson equation. An efficient solver, which has been made publicly available, is used to solve the equation. The proposed method is demonstrated on a fat quantification MRI task that is a part of a prospective study of fat accumulation. The method is compared to a phase unwrapping method based on region growing. Results indicate that the proposed method provides more robust unwrapping. Unlike region growing methods, the proposed method is also straight-forward to implement in 3D.