int unrrdu_permuteMain(int argc, char **argv, char *me, hestParm *hparm) { hestOpt *opt = NULL; char *out, *err; Nrrd *nin, *nout; unsigned int *perm, permLen; int pret; airArray *mop; hestOptAdd(&opt, "p,permute", "ax0 ax1", airTypeUInt, 1, -1, &perm, NULL, "new axis ordering", &permLen); OPT_ADD_NIN(nin, "input nrrd"); OPT_ADD_NOUT(out, "output nrrd"); mop = airMopNew(); airMopAdd(mop, opt, (airMopper)hestOptFree, airMopAlways); USAGE(_unrrdu_permuteInfoL); PARSE(); airMopAdd(mop, opt, (airMopper)hestParseFree, airMopAlways); nout = nrrdNew(); airMopAdd(mop, nout, (airMopper)nrrdNuke, airMopAlways); if (!( permLen == nin->dim )) { fprintf(stderr,"%s: # axes in permutation (%u) != nrrd dim (%d)\n", me, permLen, nin->dim); airMopError(mop); return 1; } if (nrrdAxesPermute(nout, nin, perm)) { airMopAdd(mop, err = biffGetDone(NRRD), airFree, airMopAlways); fprintf(stderr, "%s: error permuting nrrd:\n%s", me, err); airMopError(mop); return 1; } SAVE(out, nout, NULL); airMopOkay(mop); return 0; }
int tend_estimMain(int argc, char **argv, char *me, hestParm *hparm) { int pret; hestOpt *hopt = NULL; char *perr, *err; airArray *mop; Nrrd **nin, *nin4d, *nbmat, *nterr, *nB0, *nout; char *outS, *terrS, *bmatS, *eb0S; float soft, scale, sigma; int dwiax, EE, knownB0, oldstuff, estmeth, verbose, fixneg; unsigned int ninLen, axmap[4], wlsi, *skip, skipNum, skipIdx; double valueMin, thresh; Nrrd *ngradKVP=NULL, *nbmatKVP=NULL; double bKVP, bval; tenEstimateContext *tec; hestOptAdd(&hopt, "old", NULL, airTypeInt, 0, 0, &oldstuff, NULL, "instead of the new tenEstimateContext code, use " "the old tenEstimateLinear code"); hestOptAdd(&hopt, "sigma", "sigma", airTypeFloat, 1, 1, &sigma, "nan", "Rician noise parameter"); hestOptAdd(&hopt, "v", "verbose", airTypeInt, 1, 1, &verbose, "0", "verbosity level"); hestOptAdd(&hopt, "est", "estimate method", airTypeEnum, 1, 1, &estmeth, "lls", "estimation method to use. \"lls\": linear-least squares", NULL, tenEstimate1Method); hestOptAdd(&hopt, "wlsi", "WLS iters", airTypeUInt, 1, 1, &wlsi, "1", "when using weighted-least-squares (\"-est wls\"), how " "many iterations to do after the initial weighted fit."); hestOptAdd(&hopt, "fixneg", NULL, airTypeInt, 0, 0, &fixneg, NULL, "after estimating the tensor, ensure that there are no negative " "eigenvalues by adding (to all eigenvalues) the amount by which " "the smallest is negative (corresponding to increasing the " "non-DWI image value)."); hestOptAdd(&hopt, "ee", "filename", airTypeString, 1, 1, &terrS, "", "Giving a filename here allows you to save out the tensor " "estimation error: a value which measures how much error there " "is between the tensor model and the given diffusion weighted " "measurements for each sample. By default, no such error " "calculation is saved."); hestOptAdd(&hopt, "eb", "filename", airTypeString, 1, 1, &eb0S, "", "In those cases where there is no B=0 reference image given " "(\"-knownB0 false\"), " "giving a filename here allows you to save out the B=0 image " "which is estimated from the data. By default, this image value " "is estimated but not saved."); hestOptAdd(&hopt, "t", "thresh", airTypeDouble, 1, 1, &thresh, "nan", "value at which to threshold the mean DWI value per pixel " "in order to generate the \"confidence\" mask. By default, " "the threshold value is calculated automatically, based on " "histogram analysis."); hestOptAdd(&hopt, "soft", "soft", airTypeFloat, 1, 1, &soft, "0", "how fuzzy the confidence boundary should be. By default, " "confidence boundary is perfectly sharp"); hestOptAdd(&hopt, "scale", "scale", airTypeFloat, 1, 1, &scale, "1", "After estimating the tensor, scale all of its elements " "(but not the confidence value) by this amount. Can help with " "downstream numerical precision if values are very large " "or small."); hestOptAdd(&hopt, "mv", "min val", airTypeDouble, 1, 1, &valueMin, "1.0", "minimum plausible value (especially important for linear " "least squares estimation)"); hestOptAdd(&hopt, "B", "B-list", airTypeString, 1, 1, &bmatS, NULL, "6-by-N list of B-matrices characterizing " "the diffusion weighting for each " "image. \"tend bmat\" is one source for such a matrix; see " "its usage info for specifics on how the coefficients of " "the B-matrix are ordered. " "An unadorned plain text file is a great way to " "specify the B-matrix.\n **OR**\n " "Can say just \"-B kvp\" to try to learn B matrices from " "key/value pair information in input images."); hestOptAdd(&hopt, "b", "b", airTypeDouble, 1, 1, &bval, "nan", "\"b\" diffusion-weighting factor (units of sec/mm^2)"); hestOptAdd(&hopt, "knownB0", "bool", airTypeBool, 1, 1, &knownB0, NULL, "Determines of the B=0 non-diffusion-weighted reference image " "is known, or if it has to be estimated along with the tensor " "elements.\n " "\b\bo if \"true\": in the given list of diffusion gradients or " "B-matrices, there are one or more with zero norm, which are " "simply averaged to find the B=0 reference image value\n " "\b\bo if \"false\": there may or may not be diffusion-weighted " "images among the input; the B=0 image value is going to be " "estimated along with the diffusion model"); hestOptAdd(&hopt, "i", "dwi0 dwi1", airTypeOther, 1, -1, &nin, "-", "all the diffusion-weighted images (DWIs), as seperate 3D nrrds, " "**OR**: One 4D nrrd of all DWIs stacked along axis 0", &ninLen, NULL, nrrdHestNrrd); hestOptAdd(&hopt, "o", "nout", airTypeString, 1, 1, &outS, "-", "output tensor volume"); mop = airMopNew(); airMopAdd(mop, hopt, (airMopper)hestOptFree, airMopAlways); USAGE(_tend_estimInfoL); JUSTPARSE(); airMopAdd(mop, hopt, (airMopper)hestParseFree, airMopAlways); nout = nrrdNew(); airMopAdd(mop, nout, (airMopper)nrrdNuke, airMopAlways); nbmat = nrrdNew(); airMopAdd(mop, nbmat, (airMopper)nrrdNuke, airMopAlways); /* figure out B-matrix */ if (strcmp("kvp", airToLower(bmatS))) { /* its NOT coming from key/value pairs */ if (!AIR_EXISTS(bval)) { fprintf(stderr, "%s: need to specify scalar b-value\n", me); airMopError(mop); return 1; } if (nrrdLoad(nbmat, bmatS, NULL)) { airMopAdd(mop, err=biffGetDone(NRRD), airFree, airMopAlways); fprintf(stderr, "%s: trouble loading B-matrix:\n%s\n", me, err); airMopError(mop); return 1; } nin4d = nin[0]; skip = NULL; skipNum = 0; } else { /* it IS coming from key/value pairs */ if (1 != ninLen) { fprintf(stderr, "%s: require a single 4-D DWI volume for " "key/value pair based calculation of B-matrix\n", me); airMopError(mop); return 1; } if (oldstuff) { if (knownB0) { fprintf(stderr, "%s: sorry, key/value-based DWI info not compatible " "with older implementation of knownB0\n", me); airMopError(mop); return 1; } } if (tenDWMRIKeyValueParse(&ngradKVP, &nbmatKVP, &bKVP, &skip, &skipNum, nin[0])) { airMopAdd(mop, err=biffGetDone(TEN), airFree, airMopAlways); fprintf(stderr, "%s: trouble parsing DWI info:\n%s\n", me, err); airMopError(mop); return 1; } if (AIR_EXISTS(bval)) { fprintf(stderr, "%s: WARNING: key/value pair derived b-value %g " "over-riding %g from command-line", me, bKVP, bval); } bval = bKVP; if (ngradKVP) { airMopAdd(mop, ngradKVP, (airMopper)nrrdNuke, airMopAlways); if (tenBMatrixCalc(nbmat, ngradKVP)) { airMopAdd(mop, err=biffGetDone(TEN), airFree, airMopAlways); fprintf(stderr, "%s: trouble finding B-matrix:\n%s\n", me, err); airMopError(mop); return 1; } } else { airMopAdd(mop, nbmatKVP, (airMopper)nrrdNuke, airMopAlways); if (nrrdConvert(nbmat, nbmatKVP, nrrdTypeDouble)) { airMopAdd(mop, err=biffGetDone(NRRD), airFree, airMopAlways); fprintf(stderr, "%s: trouble converting B-matrix:\n%s\n", me, err); airMopError(mop); return 1; } } /* this will work because of the impositions of tenDWMRIKeyValueParse */ dwiax = ((nrrdKindList == nin[0]->axis[0].kind || nrrdKindVector == nin[0]->axis[0].kind) ? 0 : ((nrrdKindList == nin[0]->axis[1].kind || nrrdKindVector == nin[0]->axis[1].kind) ? 1 : ((nrrdKindList == nin[0]->axis[2].kind || nrrdKindVector == nin[0]->axis[2].kind) ? 2 : 3))); if (0 == dwiax) { nin4d = nin[0]; } else { axmap[0] = dwiax; axmap[1] = 1 > dwiax ? 1 : 0; axmap[2] = 2 > dwiax ? 2 : 1; axmap[3] = 3 > dwiax ? 3 : 2; nin4d = nrrdNew(); airMopAdd(mop, nin4d, (airMopper)nrrdNuke, airMopAlways); if (nrrdAxesPermute(nin4d, nin[0], axmap)) { airMopAdd(mop, err=biffGetDone(NRRD), airFree, airMopAlways); fprintf(stderr, "%s: trouble creating DWI volume:\n%s\n", me, err); airMopError(mop); return 1; } } } nterr = NULL; nB0 = NULL; if (!oldstuff) { if (1 != ninLen) { fprintf(stderr, "%s: sorry, currently need single 4D volume " "for new implementation\n", me); airMopError(mop); return 1; } if (!AIR_EXISTS(thresh)) { if (tend_estimThresholdFind(&thresh, nbmat, nin4d)) { airMopAdd(mop, err=biffGetDone(TEN), airFree, airMopAlways); fprintf(stderr, "%s: trouble finding threshold:\n%s\n", me, err); airMopError(mop); return 1; } /* HACK to lower threshold a titch */ thresh *= 0.93; fprintf(stderr, "%s: using mean DWI threshold %g\n", me, thresh); } tec = tenEstimateContextNew(); tec->progress = AIR_TRUE; airMopAdd(mop, tec, (airMopper)tenEstimateContextNix, airMopAlways); EE = 0; if (!EE) tenEstimateVerboseSet(tec, verbose); if (!EE) tenEstimateNegEvalShiftSet(tec, fixneg); if (!EE) EE |= tenEstimateMethodSet(tec, estmeth); if (!EE) EE |= tenEstimateBMatricesSet(tec, nbmat, bval, !knownB0); if (!EE) EE |= tenEstimateValueMinSet(tec, valueMin); for (skipIdx=0; skipIdx<skipNum; skipIdx++) { /* fprintf(stderr, "%s: skipping %u\n", me, skip[skipIdx]); */ if (!EE) EE |= tenEstimateSkipSet(tec, skip[skipIdx], AIR_TRUE); } switch(estmeth) { case tenEstimate1MethodLLS: if (airStrlen(terrS)) { tec->recordErrorLogDwi = AIR_TRUE; /* tec->recordErrorDwi = AIR_TRUE; */ } break; case tenEstimate1MethodNLS: if (airStrlen(terrS)) { tec->recordErrorDwi = AIR_TRUE; } break; case tenEstimate1MethodWLS: if (!EE) tec->WLSIterNum = wlsi; if (airStrlen(terrS)) { tec->recordErrorDwi = AIR_TRUE; } break; case tenEstimate1MethodMLE: if (!(AIR_EXISTS(sigma) && sigma > 0.0)) { fprintf(stderr, "%s: can't do %s w/out sigma > 0 (not %g)\n", me, airEnumStr(tenEstimate1Method, tenEstimate1MethodMLE), sigma); airMopError(mop); return 1; } if (!EE) EE |= tenEstimateSigmaSet(tec, sigma); if (airStrlen(terrS)) { tec->recordLikelihoodDwi = AIR_TRUE; } break; } if (!EE) EE |= tenEstimateThresholdSet(tec, thresh, soft); if (!EE) EE |= tenEstimateUpdate(tec); if (EE) { airMopAdd(mop, err=biffGetDone(TEN), airFree, airMopAlways); fprintf(stderr, "%s: trouble setting up estimation:\n%s\n", me, err); airMopError(mop); return 1; } if (tenEstimate1TensorVolume4D(tec, nout, &nB0, airStrlen(terrS) ? &nterr : NULL, nin4d, nrrdTypeFloat)) { airMopAdd(mop, err=biffGetDone(TEN), airFree, airMopAlways); fprintf(stderr, "%s: trouble doing estimation:\n%s\n", me, err); airMopError(mop); return 1; } if (airStrlen(terrS)) { airMopAdd(mop, nterr, (airMopper)nrrdNuke, airMopAlways); } } else { EE = 0; if (1 == ninLen) { EE = tenEstimateLinear4D(nout, airStrlen(terrS) ? &nterr : NULL, &nB0, nin4d, nbmat, knownB0, thresh, soft, bval); } else { EE = tenEstimateLinear3D(nout, airStrlen(terrS) ? &nterr : NULL, &nB0, (const Nrrd**)nin, ninLen, nbmat, knownB0, thresh, soft, bval); } if (EE) { airMopAdd(mop, err=biffGetDone(TEN), airFree, airMopAlways); fprintf(stderr, "%s: trouble making tensor volume:\n%s\n", me, err); airMopError(mop); return 1; } } if (nterr) { /* it was allocated by tenEstimate*, we have to clean it up */ airMopAdd(mop, nterr, (airMopper)nrrdNuke, airMopAlways); } if (nB0) { /* it was allocated by tenEstimate*, we have to clean it up */ airMopAdd(mop, nB0, (airMopper)nrrdNuke, airMopAlways); } if (1 != scale) { if (tenSizeScale(nout, nout, scale)) { airMopAdd(mop, err=biffGetDone(TEN), airFree, airMopAlways); fprintf(stderr, "%s: trouble doing scaling:\n%s\n", me, err); airMopError(mop); return 1; } } if (nterr) { if (nrrdSave(terrS, nterr, NULL)) { airMopAdd(mop, err=biffGetDone(NRRD), airFree, airMopAlways); fprintf(stderr, "%s: trouble writing error image:\n%s\n", me, err); airMopError(mop); return 1; } } if (!knownB0 && airStrlen(eb0S)) { if (nrrdSave(eb0S, nB0, NULL)) { airMopAdd(mop, err=biffGetDone(NRRD), airFree, airMopAlways); fprintf(stderr, "%s: trouble writing estimated B=0 image:\n%s\n", me, err); airMopError(mop); return 1; } } if (nrrdSave(outS, nout, NULL)) { airMopAdd(mop, err=biffGetDone(NRRD), airFree, airMopAlways); fprintf(stderr, "%s: trouble writing:\n%s\n", me, err); airMopError(mop); return 1; } airMopOkay(mop); return 0; }