uTIlzReg is for research purposes only and has not been approved for clinical use.
Presentation:
uTIlzReg_LDDMM_Beg is a program to perform LDDMM registration between 2D/3D images.
Its main goal is to compare images with initial momenta.
It can perform multikernel image registration (Risser TMI 2011).
The computations of uTIlzReg_LDDMM_Beg are done in the source (= template) image coordinate system.
Remark that our implementation is not the original JHU one, so it may lead to slighly different results.
References:
LDDMM Registration algorithm: Beg F., Miller M., Trouvé A., Younes L., Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms. International Journal of Computer Vision, 61(2); 2005
Multikernel LDDMM registration: Risser L., Vialard F.X., Wolz R., Murgasova M., Holm D., Rueckert D., ADNI: Simultaneous Multiscale Registration using Large Deformation Diffeomorphic Metric Mapping. IEEE Transactions on Medical Imaging 30(10); 2011
Calling the function:
./uTIlzReg_LDDMM_Beg [Source] [Target] <options>
where [Source] and [Target] are the source (moving) and target (fixed) images, and <options> are obviously the options.
Options:
Primary options:
<iterations n> Number of iterations (default=10)
<subdivisions n> Number of subdivisons (default=10)
<MaxVeloUpdt n> Maximum velocity update at each iteration (default=0.4 voxels)
Inputs and Outputs:
<PrefixInputs n> Prefix of the files containing an initial velocity field (default="Null")
<PrefixOutputs n> Prefix of the files containing the outputs (default="Outputs")
<AddChannel W S T> Add a channel > W=weight (wgt of ref channel is 1) S=Source T=Target
<Mask n> Definition of a mask (default="Null")
<affineT n> Affine transfo from Trg to Src in the world domain. The 4*3 parameters are: r_xx r_xy r_xz t_x r_yx ... t_z
Kernels (Default: Gauss 1):
<Gauss S> Gaussian kernel (S = std. dev. in mm)
<M_Gauss n> Sum of Gaussian kernels (max 7)  n = k W1 S1 ... Wk Sk (k=[#kernels], W.=weight)
<M_Gauss_easy n> Sum of Gaussian kernels (max 7) with apparent weights = 1  n = k S1 ... Sk (k=[#kernels])
<M_Gauss_easier n> Sum of 7 linearly sampled Gaussian kernels with apparent weights = 1  n = Smax Smin
Secondary options:
<TranslatEstim> Allows translations of the target
<symmetric> Symmetric registration
<epsilon n> Threshold on the normalized max update of the velicty field (default=0.2)
<GreyLevAlign n> Grey level linear alignment of each channel (Inputs: Padding Src  Padding Trg)
<margins n> Margin of the image where the calculations are reduced (default=0 voxels)
<WghtVeloField n> Weight of the velocity field in the energy (default=0.001)
<RefMaxGrad n> Value to manage the convergence. Automatically configured if <0 (default=1.)
Special Outputs:
<FinalDefVec> Displacement field in mm from [Source] to [Target] (by default for the whole deformation)
<FinalDefInvVec> Displacement field in mm from [Target] to [Source]
<SplitKernels> Split the contribution of each kernel
<AOD> Amplitude of the deformations from each voxel of the source image
<DetJacobian> Determinant of the Jacobian at each voxel
<InitMomentum> Estimated initial momentum
<ShowSSD> Show the Sum of the Squared Differences at t=1 ieration after iteration
