This program implements a two passs motion compensation algorithm. First a linear registration is run based on a variation of Gupta et~al. "Fully automatic registration and segmentation of first-pass myocardial perfusion MR image sequences", Academic Radiology 17, 1375-1385 as described in in Wollny G, Kellman P, Santos A, Ledesma-Carbayo M-J, "Automatic Motion Compensation of Free Breathing acquired Myocardial Perfusion Data by using Independent Component Analysis", Medical Image Analysis, 2012, DOI:10.1016/j.media.2012.02.004, followed by a non-linear registration based Chao Li and Ying Sun, 'Nonrigid Registration of Myocardial Perfusion MRI Using Pseudo Ground Truth' , In Proc. Medical Image Computing and Computer-Assisted Intervention MICCAI 2009, 165-172, 2009. Note that for this nonlinear motion correction a preceding linear registration step is usually required. This version of the program may run all registrations in parallel.
spacial neighborhood penalty weight
temporal second derivative penalty weight
crorrelation threshhold for neighborhood analysis
input perfusion data set
output perfusion data set
File name base for the registered images. Image type and numbering scheme are taken from the input images as given in the input data set.
save cropped set to this file, the image files will use the stem of the name as file name base
save segmentation feature images and initial ICA mixing matrix
for each registration pass save the reference images to files with the given name base
for each registration pass save intermediate registered images
verbosity of output, print messages of given level and higher priorities. Supported priorities starting at lowest level are:
|info:||Low level messages|
|trace:||Function call trace|
|fail:||Report test failures|
|fatal:||Report only fatal errors|
print copyright information
print this help
print a short help
print the version number and exit
ICA components 0 = automatic estimation
don't strip the mean from the mixing curves
segment and scale the crop box around the LV (0=no segmentation)
skip images at the beginning of the series e.g. because as they are of other modalities
maximum number of iterations in ICA
|delta-peak:||difference of the peak enhancement images|
|delta-feature:||difference of the feature images|
minimal mean frequency a mixing curve can have to be considered to stem from brething. A healthy rest breating rate is 12 per minute. A negative value disables the test. A value 0.0 forces the series to be indentified as acquired with initial breath hold.
Maxiumum number of threads to use for processing,This number should be lower or equal to the number of logical processor cores in the machine. (-1: automatic estimation).
Optimizer used for minimization of the linear registration The string value will be used to construct a plug-in.. For supported plug-ins see Plugin type: minimizer/singlecost
linear transform to be used The string value will be used to construct a plug-in.. For supported plug-ins see Plugin type: 2dimage/transform
Optimizer used for minimization in the non-linear registration. The string value will be used to construct a plug-in.. For supported plug-ins see Plugin type: minimizer/singlecost
start coefficinet rate in spines, gets divided by --c-rate-divider with every pass.
Cofficient rate divider for each pass.
Start divcurl weight, gets divided by --divcurl-divider with every pass.
Divcurl weight scaling with each new pass.
Global reference all image should be aligned to. If set to a non-negative value, the images will be aligned to this references, and the cropped output image date will be injected into the original images. Leave at -1 if you don't care. In this case all images with be registered to a mean position of the movement
image cost, do not specify the src and ref parameters, these will be set by the program. The string value will be used to construct a plug-in.. For supported plug-ins see Plugin type: 2dimage/fullcost
linear registration passes (0 to disable)
non-linear registration passes (0 to disable)
Register the perfusion series given in 'segment.set' by first using automatic ICA estimation to run the linear registration and then the PGT registration. Skip two images at the beginning and otherwiese use the default parameters. Store the result in 'registered.set'.
mia-2dmyoicapgt -i segment.set -o registered.set -k 2