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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 "Simulate DW images from a tensor field" |
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static const char *_tend_simInfoL = |
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(INFO |
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". The output will be in the same form as the input to \"tend estim\". " |
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"The B-matrices (\"-B\") can be the output from \"tend bmat\", or the " |
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"gradients can be given directly (\"-g\"); one of these is required. " |
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"Note that the input tensor field (\"-i\") is the basis of the output " |
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"per-axis fields and image orientation. NOTE: this includes the " |
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"measurement frame used in the input tensor field, which implies that " |
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"the given gradients or B-matrices are already expressed in that " |
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"measurement frame. "); |
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int |
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tend_simMain(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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tenEstimateContext *tec; |
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airArray *mop; |
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int E, oldstuff, seed, keyValueSet, outType, preOutType; |
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Nrrd *nin, *nT2, *nbmat, *ngrad, *nout, *ntmp; |
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char *outS; |
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float b, sigma; |
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/* maybe this can go in tend.c, but for some reason its explicitly |
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set to AIR_FALSE there */ |
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hparm->elideSingleOtherDefault = AIR_TRUE; |
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hestOptAdd(&hopt, "old", NULL, airTypeInt, 0, 0, &oldstuff, NULL, |
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"don't use the new tenEstimateContext functionality"); |
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hestOptAdd(&hopt, "sigma", "sigma", airTypeFloat, 1, 1, &sigma, "0.0", |
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"Rician noise parameter"); |
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hestOptAdd(&hopt, "seed", "seed", airTypeInt, 1, 1, &seed, "42", |
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"seed value for RNG which creates noise"); |
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hestOptAdd(&hopt, "g", "grad list", airTypeOther, 1, 1, &ngrad, "", |
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"gradient list, one row per diffusion-weighted image", |
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NULL, NULL, nrrdHestNrrd); |
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hestOptAdd(&hopt, "B", "B matrix", airTypeOther, 1, 1, &nbmat, "", |
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"B matrix, one row per diffusion-weighted image. Using this " |
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"overrides the gradient list input via \"-g\"", |
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NULL, NULL, nrrdHestNrrd); |
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hestOptAdd(&hopt, "r", "reference field", airTypeOther, 1, 1, &nT2, NULL, |
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"reference anatomical scan, with no diffusion weighting", |
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NULL, NULL, nrrdHestNrrd); |
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hestOptAdd(&hopt, "i", "tensor field", airTypeOther, 1, 1, &nin, "-", |
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"input diffusion tensor field", NULL, NULL, nrrdHestNrrd); |
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hestOptAdd(&hopt, "b", "b", airTypeFloat, 1, 1, &b, "1000", |
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"b value for simulated scan"); |
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hestOptAdd(&hopt, "kvp", NULL, airTypeInt, 0, 0, &keyValueSet, NULL, |
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"generate key/value pairs in the NRRD header corresponding " |
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"to the input b-value and gradients or B-matrices. "); |
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hestOptAdd(&hopt, "t", "type", airTypeEnum, 1, 1, &outType, "float", |
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"output type of DWIs", |
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NULL, nrrdType); |
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hestOptAdd(&hopt, "o", "nout", airTypeString, 1, 1, &outS, "-", |
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"output image (floating point)"); |
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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_simInfoL); |
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PARSE(); |
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airMopAdd(mop, hopt, (airMopper)hestParseFree, airMopAlways); |
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nout = nrrdNew(); |
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airMopAdd(mop, nout, (airMopper)nrrdNuke, airMopAlways); |
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if (!( nbmat || ngrad )) { |
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fprintf(stderr, "%s: got neither B-matrix (\"-B\") " |
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"or gradient list (\"-g\")\n", me); |
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airMopError(mop); return 1; |
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} |
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if (!oldstuff) { |
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airSrandMT(seed); |
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tec = tenEstimateContextNew(); |
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airMopAdd(mop, tec, (airMopper)tenEstimateContextNix, airMopAlways); |
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preOutType = (nrrdTypeFloat == outType |
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? nrrdTypeFloat |
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: nrrdTypeDouble); |
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E = 0; |
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if (!E) E |= tenEstimateMethodSet(tec, tenEstimate1MethodLLS); |
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if (!E) E |= tenEstimateValueMinSet(tec, 0.0001); |
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if (nbmat) { |
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if (!E) E |= tenEstimateBMatricesSet(tec, nbmat, b, AIR_TRUE); |
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} else { |
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if (!E) E |= tenEstimateGradientsSet(tec, ngrad, b, AIR_TRUE); |
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} |
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if (!E) E |= tenEstimateThresholdSet(tec, 0, 0); |
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if (!E) E |= tenEstimateUpdate(tec); |
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if (!E) E |= tenEstimate1TensorSimulateVolume(tec, |
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nout, sigma, b, |
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nT2, nin, |
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preOutType, |
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keyValueSet); |
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if (E) { |
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airMopAdd(mop, err=biffGetDone(TEN), airFree, airMopAlways); |
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fprintf(stderr, "%s: trouble making DWI volume (new):\n%s\n", me, err); |
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airMopError(mop); return 1; |
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} |
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if (preOutType != outType) { |
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ntmp = nrrdNew(); |
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airMopAdd(mop, ntmp, (airMopper)nrrdNuke, airMopAlways); |
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E = 0; |
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if (!E) E |= nrrdCopy(ntmp, nout); |
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if (!E) E |= nrrdConvert(nout, ntmp, outType); |
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if (E) { |
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airMopAdd(mop, err=biffGetDone(NRRD), airFree, airMopAlways); |
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fprintf(stderr, "%s: trouble making output volume:\n%s\n", me, err); |
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airMopError(mop); return 1; |
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} |
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} |
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} else { |
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if (!nbmat) { |
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fprintf(stderr, "%s: need B-matrices for old code\n", me); |
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airMopError(mop); return 1; |
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} |
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if (tenSimulate(nout, nT2, nin, nbmat, b)) { |
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airMopAdd(mop, err=biffGetDone(TEN), airFree, airMopAlways); |
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fprintf(stderr, "%s: trouble making DWI volume:\n%s\n", me, err); |
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airMopError(mop); return 1; |
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} |
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} |
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if (nrrdSave(outS, nout, NULL)) { |
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airMopAdd(mop, err=biffGetDone(NRRD), airFree, airMopAlways); |
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fprintf(stderr, "%s: trouble writing:\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(sim, INFO); |