Kathryn Stevens

Publication Details

  • Compressed-Sensing multispectral imaging of the postoperative spine JOURNAL OF MAGNETIC RESONANCE IMAGING Worters, P. W., Sung, K., Stevens, K. J., Koch, K. M., Hargreaves, B. A. 2013; 37 (1): 243-248

    Abstract:

    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.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.

    View details for DOI 10.1002/jmri.23750

    View details for Web of Science ID 000312720000028

    View details for PubMedID 22791572

Stanford Medicine Resources:

Footer Links: