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/* |
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Teem: Tools to process and visualize scientific data and images . |
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Copyright (C) 2013, 2012, 2011, 2010, 2009 University of Chicago |
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Copyright (C) 2008, 2007, 2006, 2005 Gordon Kindlmann |
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Copyright (C) 2004, 2003, 2002, 2001, 2000, 1999, 1998 University of Utah |
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This library is free software; you can redistribute it and/or |
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modify it under the terms of the GNU Lesser General Public License |
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(LGPL) as published by the Free Software Foundation; either |
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version 2.1 of the License, or (at your option) any later version. |
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The terms of redistributing and/or modifying this software also |
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include exceptions to the LGPL that facilitate static linking. |
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This library is distributed in the hope that it will be useful, |
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but WITHOUT ANY WARRANTY; without even the implied warranty of |
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU |
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Lesser General Public License for more details. |
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You should have received a copy of the GNU Lesser General Public License |
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along with this library; if not, write to Free Software Foundation, Inc., |
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51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA |
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*/ |
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#include "ten.h" |
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#include "privateTen.h" |
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#define INFO "Estimate models from a set of DW images" |
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static const char *_tend_mfitInfoL = |
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(INFO |
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". More docs here."); |
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int |
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tend_mfitMain(int argc, const char **argv, const char *me, |
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hestParm *hparm) { |
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int pret; |
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hestOpt *hopt = NULL; |
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char *perr, *err; |
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airArray *mop; |
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Nrrd *nin, *nout, *nterr, *nconv, *niter; |
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char *outS, *terrS, *convS, *iterS, *modS; |
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int knownB0, saveB0, verbose, mlfit, typeOut; |
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unsigned int maxIter, minIter, starts; |
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double sigma, eps; |
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const tenModel *model; |
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tenExperSpec *espec; |
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hestOptAdd(&hopt, "v", "verbose", airTypeInt, 1, 1, &verbose, "0", |
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"verbosity level"); |
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hestOptAdd(&hopt, "m", "model", airTypeString, 1, 1, &modS, NULL, |
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"which model to fit. Use optional \"b0+\" prefix to " |
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"indicate that the B0 image should also be saved " |
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"(independent of whether it was known or had to be " |
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"estimated, according to \"-knownB0\")."); |
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hestOptAdd(&hopt, "ns", "# starts", airTypeUInt, 1, 1, &starts, "1", |
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"number of random starting points at which to initialize " |
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"fitting"); |
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hestOptAdd(&hopt, "ml", NULL, airTypeInt, 0, 0, &mlfit, NULL, |
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"do ML fitting, rather than least-squares, which also " |
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"requires setting \"-sigma\""); |
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hestOptAdd(&hopt, "sigma", "sigma", airTypeDouble, 1, 1, &sigma, "nan", |
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"Gaussian/Rician noise parameter"); |
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hestOptAdd(&hopt, "eps", "eps", airTypeDouble, 1, 1, &eps, "0.01", |
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"convergence epsilon"); |
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hestOptAdd(&hopt, "mini", "min iters", airTypeUInt, 1, 1, &minIter, "3", |
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"minimum required # iterations for fitting."); |
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hestOptAdd(&hopt, "maxi", "max iters", airTypeUInt, 1, 1, &maxIter, "100", |
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"maximum allowable # iterations for fitting."); |
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hestOptAdd(&hopt, "knownB0", "bool", airTypeBool, 1, 1, &knownB0, NULL, |
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"Indicates if the B=0 non-diffusion-weighted reference image " |
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"is known (\"true\") because it appears one or more times " |
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"amongst the DWIs, or, if it has to be estimated along with " |
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"the other model parameters (\"false\")"); |
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/* (this is now specified as part of the "-m" model description) |
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hestOptAdd(&hopt, "saveB0", "bool", airTypeBool, 1, 1, &saveB0, NULL, |
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"Indicates if the B=0 non-diffusion-weighted value " |
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"should be saved in output, regardless of whether it was " |
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"known or had to be esimated"); |
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*/ |
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hestOptAdd(&hopt, "t", "type", airTypeEnum, 1, 1, &typeOut, "float", |
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"output type of model parameters", |
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NULL, nrrdType); |
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hestOptAdd(&hopt, "i", "dwi", airTypeOther, 1, 1, &nin, "-", |
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"all the diffusion-weighted images in one 4D nrrd", |
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NULL, NULL, nrrdHestNrrd); |
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hestOptAdd(&hopt, "o", "nout", airTypeString, 1, 1, &outS, "-", |
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"output parameter vector image"); |
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hestOptAdd(&hopt, "eo", "filename", airTypeString, 1, 1, &terrS, "", |
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"Giving a filename here allows you to save out the per-sample " |
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"fitting error. By default, no such error is saved."); |
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hestOptAdd(&hopt, "co", "filename", airTypeString, 1, 1, &convS, "", |
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"Giving a filename here allows you to save out the per-sample " |
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"convergence fraction. By default, no such error is saved."); |
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hestOptAdd(&hopt, "io", "filename", airTypeString, 1, 1, &iterS, "", |
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"Giving a filename here allows you to save out the per-sample " |
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"number of iterations needed for fitting. " |
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"By default, no such error is saved."); |
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mop = airMopNew(); |
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airMopAdd(mop, hopt, (airMopper)hestOptFree, airMopAlways); |
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✓✗ |
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USAGE(_tend_mfitInfoL); |
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JUSTPARSE(); |
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airMopAdd(mop, hopt, (airMopper)hestParseFree, airMopAlways); |
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nterr = NULL; |
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nconv = NULL; |
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niter = NULL; |
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espec = tenExperSpecNew(); |
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airMopAdd(mop, espec, (airMopper)tenExperSpecNix, airMopAlways); |
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nout = nrrdNew(); |
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airMopAdd(mop, nout, (airMopper)nrrdNuke, airMopAlways); |
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if (tenModelParse(&model, &saveB0, AIR_FALSE, modS)) { |
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airMopAdd(mop, err=biffGetDone(TEN), airFree, airMopAlways); |
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fprintf(stderr, "%s: trouble parsing model \"%s\":\n%s\n", me, modS, err); |
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airMopError(mop); return 1; |
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} |
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if (tenExperSpecFromKeyValueSet(espec, nin)) { |
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airMopAdd(mop, err=biffGetDone(TEN), airFree, airMopAlways); |
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fprintf(stderr, "%s: trouble getting exper from kvp:\n%s\n", me, err); |
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airMopError(mop); return 1; |
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} |
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if (tenModelSqeFit(nout, |
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airStrlen(terrS) ? &nterr : NULL, |
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airStrlen(convS) ? &nconv : NULL, |
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airStrlen(iterS) ? &niter : NULL, |
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model, espec, nin, |
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knownB0, saveB0, typeOut, |
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minIter, maxIter, starts, eps, |
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NULL, verbose)) { |
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airMopAdd(mop, err=biffGetDone(TEN), airFree, airMopAlways); |
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fprintf(stderr, "%s: trouble fitting:\n%s\n", me, err); |
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airMopError(mop); return 1; |
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} |
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if (nrrdSave(outS, nout, NULL) |
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|| (airStrlen(terrS) && nrrdSave(terrS, nterr, NULL)) |
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|| (airStrlen(convS) && nrrdSave(convS, nconv, NULL)) |
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|| (airStrlen(iterS) && nrrdSave(iterS, niter, NULL))) { |
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airMopAdd(mop, err=biffGetDone(NRRD), airFree, airMopAlways); |
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fprintf(stderr, "%s: trouble writing output:\n%s\n", me, err); |
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airMopError(mop); return 1; |
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} |
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airMopOkay(mop); |
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return 0; |
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} |
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TEND_CMD(mfit, INFO); |