Compressed-Sensing multispectral imaging of the postoperative spine
Article first published online: 12 JUL 2012
Copyright © 2012 Wiley Periodicals, Inc.
Journal of Magnetic Resonance Imaging
Volume 37, Issue 1, pages 243–248, January 2013
How to Cite
Worters, P. W., Sung, K., Stevens, K. J., Koch, K. M. and Hargreaves, B. A. (2013), Compressed-Sensing multispectral imaging of the postoperative spine. J. Magn. Reson. Imaging, 37: 243–248. doi: 10.1002/jmri.23750
- Issue published online: 17 DEC 2012
- Article first published online: 12 JUL 2012
- Manuscript Accepted: 5 JUN 2012
- Manuscript Received: 15 MAR 2012
- National Institutes of Health (NIH). Grant Number: R21-EB008190
- Richard M. Lucas Foundation
- General Electric Healthcare
- metallic implants;
- distortion correction;
- fast spin echo;
- compressed sensing
To apply compressed sensing (CS) to in vivo multispectral imaging (MSI), which uses additional encoding to avoid magnetic resonance imaging (MRI) artifacts near metal, and demonstrate the feasibility of CS-MSI in postoperative spinal imaging.
Materials and Methods:
Thirteen subjects referred for spinal MRI were examined using T2-weighted MSI. A CS undersampling factor was first determined using a structural similarity index as a metric for image quality. Next, these fully sampled datasets were retrospectively undersampled using a variable-density random sampling scheme and reconstructed using an iterative soft-thresholding method. The fully and undersampled images were compared using a 5-point scale. Prospectively undersampled CS-MSI data were also acquired from two subjects to ensure that the prospective random sampling did not affect the image quality.
A two-fold outer reduction factor was deemed feasible for the spinal datasets. CS-MSI images were shown to be equivalent or better than the original MSI images in all categories: nerve visualization: P = 0.00018; image artifact: P = 0.00031; image quality: P = 0.0030. No alteration of image quality and T2 contrast was observed from prospectively undersampled CS-MSI.
This study shows that the inherently sparse nature of MSI data allows modest undersampling followed by CS reconstruction with no loss of diagnostic quality. J. Magn. Reson. Imaging 2013;37:243–248. © 2012 Wiley Periodicals, Inc.