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Compressed‐Sensing multispectral imaging of the postoperative spine
Author(s) -
Worters Pauline W.,
Sung Kyunghyun,
Stevens Kathryn J.,
Koch Kevin M.,
Hargreaves Brian A.
Publication year - 2013
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.23750
Subject(s) - undersampling , image quality , compressed sensing , multispectral image , computer science , artificial intelligence , artifact (error) , magnetic resonance imaging , iterative reconstruction , thresholding , sampling (signal processing) , computer vision , pattern recognition (psychology) , medicine , image (mathematics) , radiology , filter (signal processing)
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. J. Magn. Reson. Imaging 2013;37:243–248. © 2012 Wiley Periodicals, Inc.