szirmay-kalos, lászló budapest uni of tech
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GPU-based Image Processing Methods in Higher Dimensions and their Application to Tomography Reconstruction . Szirmay-Kalos, László Budapest Uni of Tech. Sapporo, 2010. Positron Emission Tomography. Intensity: x. e -. e +. Line Of Response : y. - PowerPoint PPT PresentationTRANSCRIPT
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GPU-based Image Processing Methods in Higher Dimensions and their Application
to Tomography Reconstruction
Szirmay-Kalos, LászlóBudapest Uni of Tech
Sapporo, 2010
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Positron Emission Tomography
e-
e+
Line Of Response: y
Intensity: x
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Iterative Maximum Likelihood Reconstruction
Measureddetectorresponse
Source intensity as a 3D voxel array
Source estimation
Source correction
Compute expecteddetector response
Expecteddetector response
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Ill-posed reconstructionerror
Iteration number
Maximum likelihood estimate
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Regularization
• Additional information– Penalty term added to the
likelihood• Prevents overfitting• TV norm (L1 optimization)
– No smoothness condition– Preserves edges
x
dttf )('
V
dvvx )(
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TV minimalization
• In steepest descent search the derivative of the TV term is needed:– Function |x| cannot be differentiated:
• Add a small term (blurring)• Primal-dual methods
– Only local values are needed: parallelization
V
dvvx )(
xV
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Detector scattering compensation
Path probability inside the detector can be pre-computed or measured
photon
crystals
intercrystalscattering
absorption
Electronicsnumber of hits
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Pre-computation
q
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dxxwxXL )()( 1
0
))(( dttxXL
L
w
L
=
Quasi-Monte Carlo filtering
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Random sampling
Random sampling
undersampling
oversampling
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Delta-Sigma modulator
Filter kernel
pixels
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Filter kernel
Delta-Sigma modulator
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Delta-Sigma modulator
Filter kernel
Floyd-Steinberg halftoning!
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Sampling with Sigma-Delta modulation
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GPU Implementation• Simulation step:
• GPU: Quasi-SIMD massively parallel machine– Gathering = threads to equations (outputs)– “No” conditional statements or variable length loops
• Reconstruction algorithm– Geometric LOR marching: threads to LORs
(adjoint problem)– LOR filtering: threads to output LORs– TV regularization: threads to voxels
xAy high dim. integrals
108 voxels108 LORs
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TV regularization results
=0.005
=0.05=0.008
No TV
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TV results
=0.001 =0.0005 =0.0001=0.005
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Scattering in the detector
3D reconstruction, no detector scattering
compensation
Detector scattering compensation
2D reconstruction:SSRB + OSEM
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F18 mouse
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Conclusions
• Image processing algorithms can be and are worth being generalized to higher dimensions, but
• beware the curse of dimensions and use Monte Carlo methods.
• GPUs are good platforms for image processing, but adopt the gathering view and refrain from conditionals.