Regularized iterative reconstruction for undersampled BLADE and its applications in three-point Dixon water–fat separation
Version of Record online: 8 FEB 2011
Copyright © 2010 Wiley-Liss, Inc.
Magnetic Resonance in Medicine
Volume 65, Issue 5, pages 1314–1325, May 2011
How to Cite
He, Q., Weng, D., Zhou, X. and Ni, C. (2011), Regularized iterative reconstruction for undersampled BLADE and its applications in three-point Dixon water–fat separation. Magn Reson Med, 65: 1314–1325. doi: 10.1002/mrm.22726
- Issue online: 15 APR 2011
- Version of Record online: 8 FEB 2011
- Manuscript Accepted: 20 OCT 2010
- Manuscript Revised: 21 SEP 2010
- Manuscript Received: 21 APR 2010
- sparse sampling;
- water–fat separation;
- regularized iterative reconstruction
In MRI, the suppression of fat signal is very important for many applications. Multipoint Dixon based water–fat separation methods are commonly used due to its robustness to B0 homogeneity compared with other fat suppression methods, such as spectral fat saturation. The traditional Cartesian k-space trajectory based multipoint Dixon technique is sensitive to motion, such as pulsatile blood flow, resulting in artifacts that compromise image quality. This work presents a three-point Dixon water–fat separation method using undersampled BLADE (aka PROPELLER) for motion robustness and speed. A regularized iterative reconstruction method is then proposed for reducing the streaking artifacts coming from undersampling. In this study, the performance of the regularized iterative reconstruction method is first tested by simulations and on MR phantoms. The performance of the proposed technique is then evaluated in vivo by comparing it with conventional fat suppression methods on the human brain and knee. Experiments show that the presented method delivers reliable water–fat separation results. The reconstruction method suppresses streaking artifacts typical for undersampled BLADE acquisition schemes without missing fine structures in the image. Magn Reson Med, 2011. © 2011 Wiley-Liss, Inc.