Compressed Sensing for Chemical Shift-Based Water

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Transcript Compressed Sensing for Chemical Shift-Based Water

Doneva M., Bornert P., Eggers H., Mertins A., Pauly J., and Lustig M.,
Magnetic Resonance in Medicine (64) 1749-1759 (2010)
Compressed Sensing for
Chemical Shift-Based Water-Fat
• Fat often appears bright in MR images: may
obscure pathology;
• Reliable fat suppression methods is needed.
• Common fat suppresion techniques:
• Spectral-spatial water excitation
• Spectral selective fat saturation
• Short TI inversion recovery
• Water-fat separation
• Based on chemical shift induced phase difference
between fat/water
Water-Fat Signal Model
• Single peak fat model
• Multi peak fat model
Two-Point Dixon
Read out
Multi-Point Acquisition
Read out
Op-phase 1
Op-phase 2
Water-Fat Separation Methods
• Image at echo time tl
• Multi peak fat model
Water Fat Separation
• Require the acquisition of two or more images at
different TE
• Long scan time needed
• Compressed sensing can be combined with
water-fat separation to improve sampling
Compressed Sensing
• Key elements of a successful compressed
sensing reconstruction:
• Signal sparsity
• Incoherent sampling
• Nonlinear, sparsity promoting
Signal Sparsity
Incoherent Sampling
Nonlinear Reconstruction
• Iterative reconstruction needed
• Optimization based on minimizing l1 norm
works well:
Image Acquisition
Imaging Parameters
• 1.5 T scanner (Phillips Healthcare)
• Retrospective under-sampling (Poisson-disk)
• Knee images
• Turbo spin echo, TR=500 ms, TE = 21 ms
• FOV 160 mm x 160 mm
• Matrix size 256 x 256, slice thickness 3mm,
voxel size 0.6 mmx0.6 mmx3 mm
• Echo time -0.4, 1.1, 2.6 ms (relative to spin
Imaging Parameters
• 1.5 T scanner (Phillips Healthcare)
• Abdominal images
• 3D gradient echo, TR=6.9 ms, TE1 = 1.66
ms, TE = 1.66 ms, =15
• FOV 400 mm x 320 mm x 216 mm
• Matrix size 240 x 192 x 54, bandwidth 833
Fat Signal Model
• Single Peak Fat Model
• Chemical shift of fat: -220 Hz
• Multi Peak Fat Model
• Three peak fat model: -30 Hz, -165 Hz,
-210 Hz
• Relative amplitude (0.15, 0.1, 0.75)
CS-WF Reconstruction
• Initial field map estimation
• Initialization:
• Low-resolution: center k-space
• High-resolution: perform CS
reconstruction for each echo
• Compute possible field map values for
each pixel and estimate initial field map
using region growing, and
• Estimate initial water and fat images
CS-WF Reconstruction
• Similar to Gauss-Newton algorithm
• Iteratively and simultaneously update the
water and fat images and the field map,
using the update as:
CS-WF Reconstruction
• Given the final estimate xn, compute a
projection on k-space yn=g(xn), set the
measured data at the sampling location
yn=y|acq and perform one last iteration.
2D Knee Images
Single peak fat model
2D Knee Images
Error seems to have some texture
2D Knee Images
Multi peak fat model (three peaks, three echoes)
3D Abdominal Images
3D Abdominal Images
CS-WF Reconstruction
• Low-resolution initialization: 50 GaussNewton iterations
• High-resolution initialization: 5 iteration
• One Gauss-Newton step for 3D data: 9
min (This is slow!)
• Nice and uniform water fat separation
• Good field map estimation
• Clean image without noticable artifact
• Slow reconstruction
• Moderate reduction factor
• High reduction factor results in loss of
Study Based on This Paper
• Silver HJ, et al. Comparison of gross body fat-water
magnetic resonance imaging at 3 Tesla to dual-energy
X-ray absorptiometry in obese women. Obesity (Silver
Spring). 2013 Apr;21(4):765-74
• Pang Y, Zhang X. Interpolated compressed sensing for
2D multiple slice fast MR imaging. PLoS One. 2013; 8(2)
• Pang Y, et al. Hepatic fat assessment using advanced
Magnetic Resonance Imaging.Quant Imaging Med Surg.
2012 Sep;2(3):213-8
• Sharma SD, et al. Chemical shift encoded water-fat
separation using parallel imaging and compressed
sensing. Magn Reson Med. 2013 Feb;69(2):456-66.
Study Based on This Paper
• Li W, et al. Fast cardiac T1 mapping in mice using a
model-based compressed sensing method. Magn Reson
Med. 2012 Oct;68(4):1127-34.
• Sharma SD,et al. Accelerated water-fat imaging using
restricted subspace field map estimation and
compressed sensing. Magn Reson Med. 2012
Thank you!