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KOSMAPOSL_mhkem.par
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KOSMAPOSL_mhkem.par
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KOSMAPOSLParameters :=
; Example file for using [Multiplexing] Hybrid Kernelized Expectation Maximisation (HKEM or KEM).
; The difference with the normal KOSMAPOSL is that we use multiple anatomical images and sigma_m
; for more info.
;the following disable the alpha coefficient output:
disable output :=1
; here we have the possibility to choose the parameters which define the kernel
; matrix and the name of the anatomical image. the following are the defaults values:
; 1 (default): use hybrid kernel (prior from MR and PET estimate)
; OR
; 0 kernel is MR-only
hybrid:=1
; Gaussian scaling parameter for the anatomical prior (units of image intensity)
; It controls the edge preservation from the anatomical image, the bigger the stronger
; default: 1
sigma_m:= {0.1,0.1}
; Gaussian scaling parameter for the PET estimate (units of PET image intensity)
; It controls the edge preservation from the functional image, the bigger the stronger
; default: 1
sigma_p:=1
; NB: sigma_dm and sigma_dp should be the same
; Spatial Gaussian scaling parameter for the anatomical prior (mm)
; default: 1 (usual range 1-5)
sigma_dm:=3
; Spatial Gaussian scaling parameter for the PET prior (mm)
; default: 1 (usual range 1-5)
sigma_dp:=3
; Number of neigbouring voxels to compare: (num_neighbours X num_neighbours X num_neighbours)
; default: 3
number of neighbours:= 3
; Number of non-zero elements in the feature vectors
; default: 1
number of non-zero feature elements:=1
; this is the file name of the anatomical image
anatomical image filenames:= {MRbrain.hv,CTbrain.hv}
; the following should be 1 if you want to reconstruct 2D data
only_2D:=1
; the following is the output prefix of the PET reconstructed image
kernelised output filename prefix :=MKOSMAPOSL
;the following is only set if you want to freeze the kernel matrix to a certain subiteration. Default is -1
;freeze iterative kernel at subiteration number :=21
objective function type:= PoissonLogLikelihoodWithLinearModelForMeanAndProjData
PoissonLogLikelihoodWithLinearModelForMeanAndProjData Parameters:=
input file := my_noisy_prompts_1ax_pos.hs
; here we have the possibility to choose the parameters which define the kernel matrix and the name of the MR image.
projector pair type := Matrix
Projector Pair Using Matrix Parameters :=
Matrix type := Ray Tracing
Ray tracing matrix parameters :=
number of rays in tangential direction to trace for each bin := 10
End Ray tracing matrix parameters :=
End Projector Pair Using Matrix Parameters :=
; additive projection data to handle randoms etc
additive sinogram := my_additive_sinogram_1ax_pos.hs
; norm and acf
Bin Normalisation type := From ProjData
Bin Normalisation From ProjData :=
normalisation projdata filename:= my_multfactors_1ax_pos.hs
End Bin Normalisation From ProjData:=
; if the next parameters are enabled,
; the sensitivity will be computed and saved
; use %d where you want the subset-number (a la printf)
; we do this here for illustration, but also for re-use later on (to save some time)
; CAREFUL: use correct number of subsets in name to avoid confusion
subset sensitivity filenames:= sens_%d.hv
recompute sensitivity := 1
xy output image size (in pixels) := 256
end PoissonLogLikelihoodWithLinearModelForMeanAndProjData Parameters:=
; if you want to continue from a particular image
; initial estimate:= reconML.hv
; Number of subsets should usually be a divisor of num_views/8
; the following is an example for the Siemens mMR
number of subsets:= 21
number of subiterations:= 630
save estimates at subiteration intervals:= 21
END KOSMAPOSLParameters:=