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MesdContent-type: text/html mesdSection: User Commands (1)Index Return to Main Contents NAMEmesd - Maximum entropy spherical deconvolution.SYNOPSISmesd [options]DESCRIPTIONmesd is a general program for maximum entropy spherical deconvolution. It also runs PASMRI, which is a special case of spherical deconvolution.The program sends its output to the standard output by default. The input data must be in voxel order. EXAMPLESRun MESD on every voxel of the data file SubjectA.Bfloat using the spike deconvolution kernel (see Options, -filter).cat SubjectA.Bfloat | mesd -filter SPIKE 1.0 -schemefile A.scheme > MESD_A.Bdouble And using the PASMRI kernel with r = 1.4: cat SubjectA.Bfloat | mesd -filter PAS 1.4 -schemefile A.scheme > PAS_A.Bdouble The basic options are similar to modelfit. See modelfit(1) for examples of running simulations, which can be run with mesd in a similar way. The format of the output in each voxel is: { exitcode
The exitcode contains the results of three tests. The first test thresholds the maximum relative error between the numerical integrals computed at convergence and those computed using a larger test point set; if the error is greater than a threshold the exitcode is increased from zero to one as a warning; if it is greater than a larger threshold the exitcode is increased to two to suggest failure. The second test thresholds the predicted error in numerical integrals computed using the test point set; if the predicted error is greater than a threshold the exitcode is increased by 10. The third test thresholds the RMS error between the measurements and their predictions from the fitted deconvolution; if the errors are greater than a threshold, the exitcode is increased by 100. An exitcode of 112 means that all three tests were failed and the result is likely to be unreliable. If all is well the exitcode is zero. Results are often still reliable even if one or two of the tests are failed. Other possible exitcodes are: 5 - The optimization failed to converge -1 - Background -100 - Something wrong in the MRI data, e.g. negative or zero measurements, so that the optimization could not run.
The standard MESD implementation is computationally demanding, particularly as the number of measurements increases (computation is approximately O(N^2), where N is the number of measurements). There are two ways to obtain significant computational speed-up: i) Turn off error checks and use a small point set for computing numerical integrals in the algorithm by adding the flag -fastmesd. Sakaie CDMRI 2008 shows that using the smallest point set (-basepointset 0) with no error checks usually has only a minor effect on the output of the algorithm, but provides a major reduction in computation time. You can increase the point set size using -basepointset with an argument higher than 0, which may produce better results in some voxels, but will increase computation time, which approximately doubles every time the point set index increases by 1. ii) Reduce the complexity of the maximum entropy encoding using -mepointset <X>. By default <X> = N, the number of measurements, and is the number of parameters in the max. ent. representation of the output function, ie the number of lambda parameters, as described in Jansons and Alexander Inverse Problems 2003. However, we can represent the function using less components and <X> here specifies the number of lambda parameters. To obtain speed-up, set <X> < N; complexity become O(<X>^2) rather than O(N^2). Note that <X> must be chosen so that the camino/PointSets directory contains a point set with that number of elements. When -mepointset decreases, the numerical integration checks make less and less of a difference and smaller point sets for numerical integration (see -basepointset) become adequate. So when <X> is low -fastmesd is worth using to get even more speed-up. The choice of <X> is a parameter of the technique. Too low and you lose angular resoloution; too high and you see no computational benefit and may even suffer from overfitting. Empirically, we have found that <X>=16 often gives good results and good speed up, but it is worth trying a few values a comparing performance. The reduced encoding is described in the following ISMRM abstract: Sweet and Alexander "Reduced Encoding Persistent Angular Structure" 572 ISMRM 2010. OPTIONSmesd processes options in command line order.Basic options for processing a data file:
AUTHORSDaniel Alexander <camino@cs.ucl.ac.uk>SEE ALSOmodelfit(1), dtfit(1), twotenfit(1), threetenfit(1), datasynth(1)BUGS
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