compressed sensing for chemical shift-based water-fat separation
DESCRIPTION
Compressed Sensing for Chemical Shift-Based Water-Fat Separation. Doneva M., Bornert P., Eggers H., Mertins A., Pauly J., and Lustig M., Magnetic Resonance in Medicine (64) 1749-1759 (2010). Background. Fat often appears bright in MR images: may obscure pathology; - PowerPoint PPT PresentationTRANSCRIPT
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Compressed Sensing for Chemical Shift-Based Water-Fat
Separation
Doneva M., Bornert P., Eggers H., Mertins A., Pauly J., and Lustig M., Magnetic Resonance in Medicine (64) 1749-1759 (2010)
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Background• 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
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Water-Fat Signal Model• Single peak fat model
• Multi peak fat model
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Two-Point Dixon
RF
GPartition
GPhase
GReadout
In-phaseRead out Op-phase
water
fatwater
fat
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Multi-Point Acquisition
RF
GPartition
GPhase
GReadout
In-phaseRead out Op-phase 1
water
fatwater
fat
Op-phase 2
water
fat
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Water-Fat Separation Methods
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• Image at echo time tl
• Multi peak fat model
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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 efficiency
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Compressed Sensing• Key elements of a successful compressed
sensing reconstruction:• Signal sparsity• Incoherent sampling• Nonlinear, sparsity promoting
reconstruction
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Signal Sparsity
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Incoherent Sampling
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Nonlinear Reconstruction• Iterative reconstruction needed• Optimization based on minimizing l1 norm
works well: 2
21minarg llull
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Image Acquisition
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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
echo)
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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
Hz/pix
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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)
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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
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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:
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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.
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2D Knee Images
Single peak fat model
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2D Knee Images
Error seems to have some texture
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2D Knee Images
Multi peak fat model (three peaks, three echoes)
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3D Abdominal Images
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3D Abdominal Images
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CS-WF Reconstruction
• Low-resolution initialization: 50 Gauss-Newton iterations
• High-resolution initialization: 5 iteration• One Gauss-Newton step for 3D data: 9
min (This is slow!)
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Discussion• 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
contrast
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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.
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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 Mar;67(3):650-9
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Thank you!