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.
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.
Authors: Brian A Hargreaves; Weitian Chen; Wenmiao Lu; Marcus T Alley; Garry E Gold; Anja C S Brau; John M Pauly; Kim Butts Pauly Journal: J Magn Reson Imaging Date: 2010-04 Impact factor: 4.813
Authors: Reed F Busse; Anja C S Brau; Anthony Vu; Charles R Michelich; Ersin Bayram; Richard Kijowski; Scott B Reeder; Howard A Rowley Journal: Magn Reson Med Date: 2008-09 Impact factor: 4.668
Authors: Cesar de Cesar Netto; Lucas F Fonseca; Benjamin Fritz; Steven E Stern; Esther Raithel; Mathias Nittka; Lew C Schon; Jan Fritz Journal: Eur Radiol Date: 2017-12-07 Impact factor: 5.315