Subject-specific models of susceptibility-induced B0 field variations in breast MRI
Article first published online: 3 AUG 2012
Copyright © 2012 Wiley Periodicals, Inc.
Journal of Magnetic Resonance Imaging
Volume 37, Issue 1, pages 227–232, January 2013
How to Cite
Jordan, C. D., Daniel, B. L., Koch, K. M., Yu, H., Conolly, S. and Hargreaves, B. A. (2013), Subject-specific models of susceptibility-induced B0 field variations in breast MRI. J. Magn. Reson. Imaging, 37: 227–232. doi: 10.1002/jmri.23762
- Issue published online: 17 DEC 2012
- Article first published online: 3 AUG 2012
- Manuscript Accepted: 22 JUN 2012
- Manuscript Received: 29 SEP 2011
- NIH. Grant Numbers: R01 EB009055, RR009784
- Richard M. Lucas Foundation; General Electric Healthcare
- National Science Foundation Graduate Research Fellowship. Grant Number: DGE-0645962
- susceptibility variations;
- field map estimation;
- B0 field homogeneity;
- breast imaging
To rapidly calculate and validate subject-specific field maps based on the three-dimensional shape of the bilateral breast volume.
Materials and Methods:
Ten healthy female volunteers were scanned at 3 Tesla using a multi-echo sequence that provides water, fat, in-phase, out-of-phase, and field map images. A shape-specific binary mask was automatically generated to calculate a computed field map using a dipole field model. The measured and computed field maps were compared by visualizing the spatial distribution of the difference field map, the mean absolute error, and the 80% distribution widths of frequency histograms.
The 10 computed field maps had a mean absolute error of 38 Hz (0.29 ppm) compared with the measured field maps. The average 80% distribution widths for the histograms of all of the computed, measured, and difference field maps are 205 Hz, 233 Hz, and 120 Hz, respectively.
The computed field maps had substantial overall agreement with the measured field maps, indicating that breast MRI field maps can be computed based on the air–tissue interfaces. These estimates may provide a predictive model for field variations and thus have the potential to improve applications in breast MRI. J. Magn. Reson. Imaging 2013;37:227–232. © 2012 Wiley Periodicals, Inc.