I
ICGs
Illumination buffer, photon transport
256–258
Image operators, atlas construction
775–777
Image processing
CCL in CUDA
algorithms, implementations
572–577
core algorithm
570–572
overview
569–570
performance evaluation
577–581
with CUDA/OpenCL
background subtraction
549–550
basic problem
547
core technology
548–551
cross-correlation
561
global memory only
561–562
global memory reads
560
global/shared/coalesced memory access
562–565
implementation/evaluation
551–565
number of images
564
Pearson's correlation coefficients
551
performance evaluation
565–566
real-time video background subtraction
552–554
single kernel and I/O operations
554–555
single kernel and serial/asynchronous I/O operations
558–560
single/three kernel and I/O operations
558
GPU computing status
545–546
Image reconstruction
CT
backprojection
669–672
exhaustive sampling
696–697
iterative algorithms performance
700–702
multi-objective optimization
697–698
OS-SIRT
695–696
parameter selection interface
698–700
projection's filtering
663–669
quality metric
698
regularization scheme performance
702–703
related work
675–676
steps
661–663
MRI
algorithms and implementations
713–719
core method
710–713
with field inhomogeneity compensation
711–712
future work
720–721
iterative conjugate gradients
713–715
MATLAB toolbox
718–719
multi-GPU implementation
718
non-Cartesian sampling trajectory
710–711
overview
709–710
with parallel programming
712–713
performance analysis
719–720
SpMV
717
from randomly ordered lines with CUDA
algorithms, implementations, evaluations
684–686
backprojection
686
challenges
681–682
core methods
682–684
data structures
685
forward-projection
685–687
future work
690–691
line-projection accuracy
688–690
list-mode image reconstruction
679–681
overview
684–685
Incremental alpha-expansion algorithms, Graph Cuts for computer vision
449–450
Independent current gates (ICGs), definition
370–371
Indicator functions, atomic densities as
20
Initialization function, photon transport
257
Input/output operations
CCL in CUDA
574
Insight segmentation and registration toolkit (ITK), medical imaging
core methods
737–740
CUDA integration
740–742
definition
737
filter comparison
746–748
future work
748
implementation
740–746
linear convolution
738–739
linear convolution filters
742–743
Interpolation decomposition, GRASSY platform
97
Inversion method, photon transport
248
Iterated Function Systems (IFS), and Fractal Flames algorithm
components
264–265
core technology
266–267
functions
265–266
implementation
266–270
memory-access patterns
267–269
performance evaluation
270–272
phases
267
problem overview
263–264
rendering
269
static data
270
Iterative conjugate gradients, MRI reconstruction
algorithm
713–715
high-speed implementations
715–718
kernel functions
715–717
Iterative Self-consistent Parallel Imaging Reconstruction (SPIRiT), MRI
algorithms, implementations, evaluations
727–733
basic problem
724
Calibration Consistency operator
727–729
core methods
726
data consistency projection
729
overview
735
parallel imaging
726
performance analysis
733–734
soft thresholding
729
wavelet transform
729–733
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