| File: | src/ten/tendEstim.c |
| Location: | line 329, column 5 |
| Description: | Value stored to 'EE' is never read |
| 1 | /* |
| 2 | Teem: Tools to process and visualize scientific data and images . |
| 3 | Copyright (C) 2013, 2012, 2011, 2010, 2009 University of Chicago |
| 4 | Copyright (C) 2008, 2007, 2006, 2005 Gordon Kindlmann |
| 5 | Copyright (C) 2004, 2003, 2002, 2001, 2000, 1999, 1998 University of Utah |
| 6 | |
| 7 | This library is free software; you can redistribute it and/or |
| 8 | modify it under the terms of the GNU Lesser General Public License |
| 9 | (LGPL) as published by the Free Software Foundation; either |
| 10 | version 2.1 of the License, or (at your option) any later version. |
| 11 | The terms of redistributing and/or modifying this software also |
| 12 | include exceptions to the LGPL that facilitate static linking. |
| 13 | |
| 14 | This library is distributed in the hope that it will be useful, |
| 15 | but WITHOUT ANY WARRANTY; without even the implied warranty of |
| 16 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU |
| 17 | Lesser General Public License for more details. |
| 18 | |
| 19 | You should have received a copy of the GNU Lesser General Public License |
| 20 | along with this library; if not, write to Free Software Foundation, Inc., |
| 21 | 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA |
| 22 | */ |
| 23 | |
| 24 | #include "ten.h" |
| 25 | #include "privateTen.h" |
| 26 | |
| 27 | #define INFO"Estimate tensors from a set of DW images" "Estimate tensors from a set of DW images" |
| 28 | static const char *_tend_estimInfoL = |
| 29 | (INFO"Estimate tensors from a set of DW images" |
| 30 | ". The tensor coefficient weightings associated with " |
| 31 | "each of the DWIs, the B-matrix, is given either as a separate array, " |
| 32 | "(see \"tend bmat\" usage info for details), or by the key-value pairs " |
| 33 | "in the DWI nrrd header. A \"confidence\" value is computed with the " |
| 34 | "tensor, based on a soft thresholding of the sum of all the DWIs, " |
| 35 | "according to the threshold and softness parameters. "); |
| 36 | |
| 37 | int |
| 38 | tend_estimMain(int argc, const char **argv, const char *me, |
| 39 | hestParm *hparm) { |
| 40 | int pret; |
| 41 | hestOpt *hopt = NULL((void*)0); |
| 42 | char *perr, *err; |
| 43 | airArray *mop; |
| 44 | |
| 45 | Nrrd **nin, *nin4d, *nbmat, *nterr, *nB0, *nout; |
| 46 | char *outS, *terrS, *bmatS, *eb0S; |
| 47 | float soft, scale, sigma; |
| 48 | int dwiax, EE, knownB0, oldstuff, estmeth, verbose, fixneg; |
| 49 | unsigned int ninLen, axmap[4], wlsi, *skip, skipNum, skipIdx; |
| 50 | double valueMin, thresh; |
| 51 | |
| 52 | Nrrd *ngradKVP=NULL((void*)0), *nbmatKVP=NULL((void*)0); |
| 53 | double bKVP, bval; |
| 54 | |
| 55 | tenEstimateContext *tec; |
| 56 | |
| 57 | hestOptAdd(&hopt, "old", NULL((void*)0), airTypeInt, 0, 0, &oldstuff, NULL((void*)0), |
| 58 | "instead of the new tenEstimateContext code, use " |
| 59 | "the old tenEstimateLinear code"); |
| 60 | hestOptAdd(&hopt, "sigma", "sigma", airTypeFloat, 1, 1, &sigma, "nan", |
| 61 | "Rician noise parameter"); |
| 62 | hestOptAdd(&hopt, "v", "verbose", airTypeInt, 1, 1, &verbose, "0", |
| 63 | "verbosity level"); |
| 64 | hestOptAdd(&hopt, "est", "estimate method", airTypeEnum, 1, 1, &estmeth, |
| 65 | "lls", |
| 66 | "estimation method to use. \"lls\": linear-least squares", |
| 67 | NULL((void*)0), tenEstimate1Method); |
| 68 | hestOptAdd(&hopt, "wlsi", "WLS iters", airTypeUInt, 1, 1, &wlsi, "1", |
| 69 | "when using weighted-least-squares (\"-est wls\"), how " |
| 70 | "many iterations to do after the initial weighted fit."); |
| 71 | hestOptAdd(&hopt, "fixneg", NULL((void*)0), airTypeInt, 0, 0, &fixneg, NULL((void*)0), |
| 72 | "after estimating the tensor, ensure that there are no negative " |
| 73 | "eigenvalues by adding (to all eigenvalues) the amount by which " |
| 74 | "the smallest is negative (corresponding to increasing the " |
| 75 | "non-DWI image value)."); |
| 76 | hestOptAdd(&hopt, "ee", "filename", airTypeString, 1, 1, &terrS, "", |
| 77 | "Giving a filename here allows you to save out the tensor " |
| 78 | "estimation error: a value which measures how much error there " |
| 79 | "is between the tensor model and the given diffusion weighted " |
| 80 | "measurements for each sample. By default, no such error " |
| 81 | "calculation is saved."); |
| 82 | hestOptAdd(&hopt, "eb", "filename", airTypeString, 1, 1, &eb0S, "", |
| 83 | "In those cases where there is no B=0 reference image given " |
| 84 | "(\"-knownB0 false\"), " |
| 85 | "giving a filename here allows you to save out the B=0 image " |
| 86 | "which is estimated from the data. By default, this image value " |
| 87 | "is estimated but not saved."); |
| 88 | hestOptAdd(&hopt, "t", "thresh", airTypeDouble, 1, 1, &thresh, "nan", |
| 89 | "value at which to threshold the mean DWI value per pixel " |
| 90 | "in order to generate the \"confidence\" mask. By default, " |
| 91 | "the threshold value is calculated automatically, based on " |
| 92 | "histogram analysis."); |
| 93 | hestOptAdd(&hopt, "soft", "soft", airTypeFloat, 1, 1, &soft, "0", |
| 94 | "how fuzzy the confidence boundary should be. By default, " |
| 95 | "confidence boundary is perfectly sharp"); |
| 96 | hestOptAdd(&hopt, "scale", "scale", airTypeFloat, 1, 1, &scale, "1", |
| 97 | "After estimating the tensor, scale all of its elements " |
| 98 | "(but not the confidence value) by this amount. Can help with " |
| 99 | "downstream numerical precision if values are very large " |
| 100 | "or small."); |
| 101 | hestOptAdd(&hopt, "mv", "min val", airTypeDouble, 1, 1, &valueMin, "1.0", |
| 102 | "minimum plausible value (especially important for linear " |
| 103 | "least squares estimation)"); |
| 104 | hestOptAdd(&hopt, "B", "B-list", airTypeString, 1, 1, &bmatS, NULL((void*)0), |
| 105 | "6-by-N list of B-matrices characterizing " |
| 106 | "the diffusion weighting for each " |
| 107 | "image. \"tend bmat\" is one source for such a matrix; see " |
| 108 | "its usage info for specifics on how the coefficients of " |
| 109 | "the B-matrix are ordered. " |
| 110 | "An unadorned plain text file is a great way to " |
| 111 | "specify the B-matrix.\n **OR**\n " |
| 112 | "Can say just \"-B kvp\" to try to learn B matrices from " |
| 113 | "key/value pair information in input images."); |
| 114 | hestOptAdd(&hopt, "b", "b", airTypeDouble, 1, 1, &bval, "nan", |
| 115 | "\"b\" diffusion-weighting factor (units of sec/mm^2)"); |
| 116 | hestOptAdd(&hopt, "knownB0", "bool", airTypeBool, 1, 1, &knownB0, NULL((void*)0), |
| 117 | "Indicates if the B=0 non-diffusion-weighted reference image " |
| 118 | "is known, or if it has to be estimated along with the tensor " |
| 119 | "elements.\n " |
| 120 | "\b\bo if \"true\": in the given list of diffusion gradients or " |
| 121 | "B-matrices, there are one or more with zero norm, which are " |
| 122 | "simply averaged to find the B=0 reference image value\n " |
| 123 | "\b\bo if \"false\": there may or may not be diffusion-weighted " |
| 124 | "images among the input; the B=0 image value is going to be " |
| 125 | "estimated along with the diffusion model"); |
| 126 | hestOptAdd(&hopt, "i", "dwi0 dwi1", airTypeOther, 1, -1, &nin, "-", |
| 127 | "all the diffusion-weighted images (DWIs), as separate 3D nrrds, " |
| 128 | "**OR**: One 4D nrrd of all DWIs stacked along axis 0", |
| 129 | &ninLen, NULL((void*)0), nrrdHestNrrd); |
| 130 | hestOptAdd(&hopt, "o", "nout", airTypeString, 1, 1, &outS, "-", |
| 131 | "output tensor volume"); |
| 132 | |
| 133 | mop = airMopNew(); |
| 134 | airMopAdd(mop, hopt, (airMopper)hestOptFree, airMopAlways); |
| 135 | USAGE(_tend_estimInfoL)if (!argc) { hestInfo(__stdoutp, me, (_tend_estimInfoL), hparm ); hestUsage(__stdoutp, hopt, me, hparm); hestGlossary(__stdoutp , hopt, hparm); airMopError(mop); return 0; }; |
| 136 | JUSTPARSE()if ((pret=hestParse(hopt, argc, argv, &perr, hparm))) { if (1 == pret) { fprintf(__stderrp, "%s: %s\n", me, perr); free (perr); hestUsage(__stderrp, hopt, me, hparm); airMopError(mop ); return 2; } else { exit(1); } }; |
| 137 | airMopAdd(mop, hopt, (airMopper)hestParseFree, airMopAlways); |
| 138 | |
| 139 | nout = nrrdNew(); |
| 140 | airMopAdd(mop, nout, (airMopper)nrrdNuke, airMopAlways); |
| 141 | nbmat = nrrdNew(); |
| 142 | airMopAdd(mop, nbmat, (airMopper)nrrdNuke, airMopAlways); |
| 143 | |
| 144 | /* figure out B-matrix */ |
| 145 | if (strcmp("kvp", airToLower(bmatS))) { |
| 146 | /* its NOT coming from key/value pairs */ |
| 147 | if (!AIR_EXISTS(bval)(((int)(!((bval) - (bval)))))) { |
| 148 | fprintf(stderr__stderrp, "%s: need to specify scalar b-value\n", me); |
| 149 | airMopError(mop); return 1; |
| 150 | } |
| 151 | if (nrrdLoad(nbmat, bmatS, NULL((void*)0))) { |
| 152 | airMopAdd(mop, err=biffGetDone(NRRDnrrdBiffKey), airFree, airMopAlways); |
| 153 | fprintf(stderr__stderrp, "%s: trouble loading B-matrix:\n%s\n", me, err); |
| 154 | airMopError(mop); return 1; |
| 155 | } |
| 156 | nin4d = nin[0]; |
| 157 | skip = NULL((void*)0); |
| 158 | skipNum = 0; |
| 159 | } else { |
| 160 | /* it IS coming from key/value pairs */ |
| 161 | if (1 != ninLen) { |
| 162 | fprintf(stderr__stderrp, "%s: require a single 4-D DWI volume for " |
| 163 | "key/value pair based calculation of B-matrix\n", me); |
| 164 | airMopError(mop); return 1; |
| 165 | } |
| 166 | if (oldstuff) { |
| 167 | if (knownB0) { |
| 168 | fprintf(stderr__stderrp, "%s: sorry, key/value-based DWI info not compatible " |
| 169 | "with older implementation of knownB0\n", me); |
| 170 | airMopError(mop); return 1; |
| 171 | } |
| 172 | } |
| 173 | if (tenDWMRIKeyValueParse(&ngradKVP, &nbmatKVP, &bKVP, |
| 174 | &skip, &skipNum, nin[0])) { |
| 175 | airMopAdd(mop, err=biffGetDone(TENtenBiffKey), airFree, airMopAlways); |
| 176 | fprintf(stderr__stderrp, "%s: trouble parsing DWI info:\n%s\n", me, err); |
| 177 | airMopError(mop); return 1; |
| 178 | } |
| 179 | if (AIR_EXISTS(bval)(((int)(!((bval) - (bval)))))) { |
| 180 | fprintf(stderr__stderrp, "%s: WARNING: key/value pair derived b-value %g " |
| 181 | "over-riding %g from command-line", me, bKVP, bval); |
| 182 | } |
| 183 | bval = bKVP; |
| 184 | if (ngradKVP) { |
| 185 | airMopAdd(mop, ngradKVP, (airMopper)nrrdNuke, airMopAlways); |
| 186 | if (tenBMatrixCalc(nbmat, ngradKVP)) { |
| 187 | airMopAdd(mop, err=biffGetDone(TENtenBiffKey), airFree, airMopAlways); |
| 188 | fprintf(stderr__stderrp, "%s: trouble finding B-matrix:\n%s\n", me, err); |
| 189 | airMopError(mop); return 1; |
| 190 | } |
| 191 | } else { |
| 192 | airMopAdd(mop, nbmatKVP, (airMopper)nrrdNuke, airMopAlways); |
| 193 | if (nrrdConvert(nbmat, nbmatKVP, nrrdTypeDouble)) { |
| 194 | airMopAdd(mop, err=biffGetDone(NRRDnrrdBiffKey), airFree, airMopAlways); |
| 195 | fprintf(stderr__stderrp, "%s: trouble converting B-matrix:\n%s\n", me, err); |
| 196 | airMopError(mop); return 1; |
| 197 | } |
| 198 | } |
| 199 | /* this will work because of the impositions of tenDWMRIKeyValueParse */ |
| 200 | dwiax = ((nrrdKindList == nin[0]->axis[0].kind || |
| 201 | nrrdKindVector == nin[0]->axis[0].kind) |
| 202 | ? 0 |
| 203 | : ((nrrdKindList == nin[0]->axis[1].kind || |
| 204 | nrrdKindVector == nin[0]->axis[1].kind) |
| 205 | ? 1 |
| 206 | : ((nrrdKindList == nin[0]->axis[2].kind || |
| 207 | nrrdKindVector == nin[0]->axis[2].kind) |
| 208 | ? 2 |
| 209 | : 3))); |
| 210 | if (0 == dwiax) { |
| 211 | nin4d = nin[0]; |
| 212 | } else { |
| 213 | axmap[0] = dwiax; |
| 214 | axmap[1] = 1 > dwiax ? 1 : 0; |
| 215 | axmap[2] = 2 > dwiax ? 2 : 1; |
| 216 | axmap[3] = 3 > dwiax ? 3 : 2; |
| 217 | nin4d = nrrdNew(); |
| 218 | airMopAdd(mop, nin4d, (airMopper)nrrdNuke, airMopAlways); |
| 219 | if (nrrdAxesPermute(nin4d, nin[0], axmap)) { |
| 220 | airMopAdd(mop, err=biffGetDone(NRRDnrrdBiffKey), airFree, airMopAlways); |
| 221 | fprintf(stderr__stderrp, "%s: trouble creating DWI volume:\n%s\n", me, err); |
| 222 | airMopError(mop); return 1; |
| 223 | } |
| 224 | } |
| 225 | } |
| 226 | |
| 227 | nterr = NULL((void*)0); |
| 228 | nB0 = NULL((void*)0); |
| 229 | if (!oldstuff) { |
| 230 | if (1 != ninLen) { |
| 231 | fprintf(stderr__stderrp, "%s: sorry, currently need single 4D volume " |
| 232 | "for new implementation\n", me); |
| 233 | airMopError(mop); return 1; |
| 234 | } |
| 235 | if (!AIR_EXISTS(thresh)(((int)(!((thresh) - (thresh)))))) { |
| 236 | unsigned char *isB0 = NULL((void*)0); |
| 237 | double bten[7], bnorm, *bmat; |
| 238 | unsigned int sl; |
| 239 | /* from nbmat, create an array that indicates B0 images */ |
| 240 | if (tenBMatrixCheck(nbmat, nrrdTypeDouble, 6)) { |
| 241 | biffAddf(TENtenBiffKey, "%s: problem within given b-matrix", me); |
| 242 | airMopError(mop); return 1; |
| 243 | } |
| 244 | isB0 = AIR_CAST(unsigned char *, malloc(nbmat->axis[1].size))((unsigned char *)(malloc(nbmat->axis[1].size))); |
| 245 | airMopAdd(mop, isB0, airFree, airMopAlways); |
| 246 | bmat = (double*) nbmat->data; |
| 247 | for (sl=0; sl<nbmat->axis[1].size; sl++) { |
| 248 | TEN_T_SET(bten, 1.0,( (bten)[0] = (1.0), (bten)[1] = (bmat[0]), (bten)[2] = (bmat [1]), (bten)[3] = (bmat[2]), (bten)[4] = (bmat[3]), (bten)[5] = (bmat[4]), (bten)[6] = (bmat[5]) ) |
| 249 | bmat[0], bmat[1], bmat[2],( (bten)[0] = (1.0), (bten)[1] = (bmat[0]), (bten)[2] = (bmat [1]), (bten)[3] = (bmat[2]), (bten)[4] = (bmat[3]), (bten)[5] = (bmat[4]), (bten)[6] = (bmat[5]) ) |
| 250 | bmat[3], bmat[4],( (bten)[0] = (1.0), (bten)[1] = (bmat[0]), (bten)[2] = (bmat [1]), (bten)[3] = (bmat[2]), (bten)[4] = (bmat[3]), (bten)[5] = (bmat[4]), (bten)[6] = (bmat[5]) ) |
| 251 | bmat[5])( (bten)[0] = (1.0), (bten)[1] = (bmat[0]), (bten)[2] = (bmat [1]), (bten)[3] = (bmat[2]), (bten)[4] = (bmat[3]), (bten)[5] = (bmat[4]), (bten)[6] = (bmat[5]) ); |
| 252 | bnorm = TEN_T_NORM(bten)(sqrt(( (bten)[1]*(bten)[1] + 2*(bten)[2]*(bten)[2] + 2*(bten )[3]*(bten)[3] + (bten)[4]*(bten)[4] + 2*(bten)[5]*(bten)[5] + (bten)[6]*(bten)[6] ))); |
| 253 | isB0[sl]=(bnorm==0.0); |
| 254 | bmat+=6; |
| 255 | } |
| 256 | if (tenEstimateThresholdFind(&thresh, isB0, nin4d)) { |
| 257 | airMopAdd(mop, err=biffGetDone(TENtenBiffKey), airFree, airMopAlways); |
| 258 | fprintf(stderr__stderrp, "%s: trouble finding threshold:\n%s\n", me, err); |
| 259 | airMopError(mop); return 1; |
| 260 | } |
| 261 | /* HACK to lower threshold a titch */ |
| 262 | thresh *= 0.93; |
| 263 | fprintf(stderr__stderrp, "%s: using mean DWI threshold %g\n", me, thresh); |
| 264 | } |
| 265 | tec = tenEstimateContextNew(); |
| 266 | tec->progress = AIR_TRUE1; |
| 267 | airMopAdd(mop, tec, (airMopper)tenEstimateContextNix, airMopAlways); |
| 268 | EE = 0; |
| 269 | if (!EE) tenEstimateVerboseSet(tec, verbose); |
| 270 | if (!EE) tenEstimateNegEvalShiftSet(tec, fixneg); |
| 271 | if (!EE) EE |= tenEstimateMethodSet(tec, estmeth); |
| 272 | if (!EE) EE |= tenEstimateBMatricesSet(tec, nbmat, bval, !knownB0); |
| 273 | if (!EE) EE |= tenEstimateValueMinSet(tec, valueMin); |
| 274 | for (skipIdx=0; skipIdx<skipNum; skipIdx++) { |
| 275 | /* fprintf(stderr, "%s: skipping %u\n", me, skip[skipIdx]); */ |
| 276 | if (!EE) EE |= tenEstimateSkipSet(tec, skip[skipIdx], AIR_TRUE1); |
| 277 | } |
| 278 | switch(estmeth) { |
| 279 | case tenEstimate1MethodLLS: |
| 280 | if (airStrlen(terrS)) { |
| 281 | tec->recordErrorLogDwi = AIR_TRUE1; |
| 282 | /* tec->recordErrorDwi = AIR_TRUE; */ |
| 283 | } |
| 284 | break; |
| 285 | case tenEstimate1MethodNLS: |
| 286 | if (airStrlen(terrS)) { |
| 287 | tec->recordErrorDwi = AIR_TRUE1; |
| 288 | } |
| 289 | break; |
| 290 | case tenEstimate1MethodWLS: |
| 291 | if (!EE) tec->WLSIterNum = wlsi; |
| 292 | if (airStrlen(terrS)) { |
| 293 | tec->recordErrorDwi = AIR_TRUE1; |
| 294 | } |
| 295 | break; |
| 296 | case tenEstimate1MethodMLE: |
| 297 | if (!(AIR_EXISTS(sigma)(((int)(!((sigma) - (sigma))))) && sigma > 0.0)) { |
| 298 | fprintf(stderr__stderrp, "%s: can't do %s w/out sigma > 0 (not %g)\n", |
| 299 | me, airEnumStr(tenEstimate1Method, tenEstimate1MethodMLE), |
| 300 | sigma); |
| 301 | airMopError(mop); return 1; |
| 302 | } |
| 303 | if (!EE) EE |= tenEstimateSigmaSet(tec, sigma); |
| 304 | if (airStrlen(terrS)) { |
| 305 | tec->recordLikelihoodDwi = AIR_TRUE1; |
| 306 | } |
| 307 | break; |
| 308 | } |
| 309 | if (!EE) EE |= tenEstimateThresholdSet(tec, thresh, soft); |
| 310 | if (!EE) EE |= tenEstimateUpdate(tec); |
| 311 | if (EE) { |
| 312 | airMopAdd(mop, err=biffGetDone(TENtenBiffKey), airFree, airMopAlways); |
| 313 | fprintf(stderr__stderrp, "%s: trouble setting up estimation:\n%s\n", me, err); |
| 314 | airMopError(mop); return 1; |
| 315 | } |
| 316 | if (tenEstimate1TensorVolume4D(tec, nout, &nB0, |
| 317 | airStrlen(terrS) |
| 318 | ? &nterr |
| 319 | : NULL((void*)0), |
| 320 | nin4d, nrrdTypeFloat)) { |
| 321 | airMopAdd(mop, err=biffGetDone(TENtenBiffKey), airFree, airMopAlways); |
| 322 | fprintf(stderr__stderrp, "%s: trouble doing estimation:\n%s\n", me, err); |
| 323 | airMopError(mop); return 1; |
| 324 | } |
| 325 | if (airStrlen(terrS)) { |
| 326 | airMopAdd(mop, nterr, (airMopper)nrrdNuke, airMopAlways); |
| 327 | } |
| 328 | } else { |
| 329 | EE = 0; |
Value stored to 'EE' is never read | |
| 330 | if (1 == ninLen) { |
| 331 | EE = tenEstimateLinear4D(nout, airStrlen(terrS) ? &nterr : NULL((void*)0), &nB0, |
| 332 | nin4d, nbmat, knownB0, thresh, soft, bval); |
| 333 | } else { |
| 334 | EE = tenEstimateLinear3D(nout, airStrlen(terrS) ? &nterr : NULL((void*)0), &nB0, |
| 335 | (const Nrrd*const*)nin, ninLen, nbmat, |
| 336 | knownB0, thresh, soft, bval); |
| 337 | } |
| 338 | if (EE) { |
| 339 | airMopAdd(mop, err=biffGetDone(TENtenBiffKey), airFree, airMopAlways); |
| 340 | fprintf(stderr__stderrp, "%s: trouble making tensor volume:\n%s\n", me, err); |
| 341 | airMopError(mop); return 1; |
| 342 | } |
| 343 | } |
| 344 | if (nterr) { |
| 345 | /* it was allocated by tenEstimate*, we have to clean it up */ |
| 346 | airMopAdd(mop, nterr, (airMopper)nrrdNuke, airMopAlways); |
| 347 | } |
| 348 | if (nB0) { |
| 349 | /* it was allocated by tenEstimate*, we have to clean it up */ |
| 350 | airMopAdd(mop, nB0, (airMopper)nrrdNuke, airMopAlways); |
| 351 | } |
| 352 | if (1 != scale) { |
| 353 | if (tenSizeScale(nout, nout, scale)) { |
| 354 | airMopAdd(mop, err=biffGetDone(TENtenBiffKey), airFree, airMopAlways); |
| 355 | fprintf(stderr__stderrp, "%s: trouble doing scaling:\n%s\n", me, err); |
| 356 | airMopError(mop); return 1; |
| 357 | } |
| 358 | } |
| 359 | if (nterr) { |
| 360 | if (nrrdSave(terrS, nterr, NULL((void*)0))) { |
| 361 | airMopAdd(mop, err=biffGetDone(NRRDnrrdBiffKey), airFree, airMopAlways); |
| 362 | fprintf(stderr__stderrp, "%s: trouble writing error image:\n%s\n", me, err); |
| 363 | airMopError(mop); return 1; |
| 364 | } |
| 365 | } |
| 366 | if (!knownB0 && airStrlen(eb0S)) { |
| 367 | if (nrrdSave(eb0S, nB0, NULL((void*)0))) { |
| 368 | airMopAdd(mop, err=biffGetDone(NRRDnrrdBiffKey), airFree, airMopAlways); |
| 369 | fprintf(stderr__stderrp, "%s: trouble writing estimated B=0 image:\n%s\n", |
| 370 | me, err); |
| 371 | airMopError(mop); return 1; |
| 372 | } |
| 373 | } |
| 374 | |
| 375 | if (nrrdSave(outS, nout, NULL((void*)0))) { |
| 376 | airMopAdd(mop, err=biffGetDone(NRRDnrrdBiffKey), airFree, airMopAlways); |
| 377 | fprintf(stderr__stderrp, "%s: trouble writing:\n%s\n", me, err); |
| 378 | airMopError(mop); return 1; |
| 379 | } |
| 380 | |
| 381 | airMopOkay(mop); |
| 382 | return 0; |
| 383 | } |
| 384 | TEND_CMD(estim, INFO)unrrduCmd tend_estimCmd = { "estim", "Estimate tensors from a set of DW images" , tend_estimMain, 0 }; |