Compressed-sensing multispectral imaging of the postoperative spine. J Magn Reson Imaging 2013 Jan;37(1):243-8
Date
07/14/2012Pubmed ID
22791572Pubmed Central ID
PMC3473176DOI
10.1002/jmri.23750Scopus ID
2-s2.0-84872857270 (requires institutional sign-in at Scopus site) 60 CitationsAbstract
PURPOSE: 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.
RESULTS: 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.
CONCLUSION: This study shows that the inherently sparse nature of MSI data allows modest undersampling followed by CS reconstruction with no loss of diagnostic quality.
Author List
Worters PW, Sung K, Stevens KJ, Koch KM, Hargreaves BAAuthor
Kevin M. Koch PhD Adjunct Professor in the Radiology department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
AlgorithmsArtifacts
Data Compression
Diagnostic Imaging
Fourier Analysis
Humans
Image Enhancement
Image Processing, Computer-Assisted
Magnetic Resonance Imaging
Models, Statistical
Normal Distribution
Postoperative Period
Retrospective Studies
Spine
