/* vector pp of parameters is as follows:
** pp[0]: principal eigenvalue
** pp[1]: fraction of 1st tensor
** pp[2]: phi for 1st tensor
** pp[3]: phi for 2nd tensor
*/
void
_tenLevmarPeledCB(double *pp, double *xx, int mm, int nn, void *_pvlData) {
/* char me[]="_tenLevmarPeledCB"; */
double tenA[7], tenB[7];
int ii;
tenDwiGagePvlData *pvlData;
double *egrad;
AIR_UNUSED(mm);
pvlData = AIR_CAST(tenDwiGagePvlData *, _pvlData);
/* Form the tensors using the estimated parms */
_tenPeledRotate2D(tenA, pp[0], pvlData->ten1Eval[2], pp[2]);
_tenPeledRotate2D(tenB, pp[0], pvlData->ten1Eval[2], pp[3]);
egrad = AIR_CAST(double *, pvlData->nten1EigenGrads->data);
/* skip past b0 gradient, HEY: not general purpose */
egrad += 3;
for (ii=0; ii<nn; ii++) {
double argA, argB, sigA, sigB;
argA = -pvlData->tec2->bValue*TEN_T3V_CONTR(tenA, egrad + 3*ii);
argB = -pvlData->tec2->bValue*TEN_T3V_CONTR(tenB, egrad + 3*ii);
if (pvlData->levmarUseFastExp) {
sigA = airFastExp(argA);
sigB = airFastExp(argB);
} else {
sigA = exp(argA);
sigB = exp(argB);
}
xx[ii] = pvlData->tec2->knownB0*(pp[1]*sigA + (1-pp[1])*sigB);
}
return;
}
static void
simulate(double *dwiSim, const double *parm, const tenExperSpec *espec) {
unsigned int ii;
double b0;
b0 = parm[0];
for (ii=0; ii<espec->imgNum; ii++) {
double adc, bb;
bb = espec->bval[ii];
/* safe because TEN_T3V_CONTR never looks at parm[0] */
adc = TEN_T3V_CONTR(parm, espec->grad + 3*ii);
dwiSim[ii] = b0*exp(-bb*adc);
}
return;
}
void
_tenDwiGageAnswer(gageContext *ctx, gagePerVolume *pvl) {
char me[]="_tenDwiGageAnswer";
unsigned int dwiIdx;
tenDwiGageKindData *kindData;
tenDwiGagePvlData *pvlData;
double *dwiAll, dwiMean=0, tentmp[7];
kindData = AIR_CAST(tenDwiGageKindData *, pvl->kind->data);
pvlData = AIR_CAST(tenDwiGagePvlData *, pvl->data);
dwiAll = pvl->directAnswer[tenDwiGageAll];
if (GAGE_QUERY_ITEM_TEST(pvl->query, tenDwiGageAll)) {
/* done if doV */
if (ctx->verbose) {
for (dwiIdx=0; dwiIdx<pvl->kind->valLen; dwiIdx++) {
fprintf(stderr, "%s(%d+%g,%d+%g,%d+%g): dwi[%u] = %g\n", me,
ctx->point.xi, ctx->point.xf,
ctx->point.yi, ctx->point.yf,
ctx->point.zi, ctx->point.zf,
dwiIdx, dwiAll[dwiIdx]);
}
fprintf(stderr, "%s: type(ngrad) = %d = %s\n", me,
kindData->ngrad->type,
airEnumStr(nrrdType, kindData->ngrad->type));
}
}
/*
if (GAGE_QUERY_ITEM_TEST(pvl->query, tenDwiGageB0)) {
if (GAGE_QUERY_ITEM_TEST(pvl->query, tenDwiGageJustDWI)) {
done if doV
}
*/
/* HEY this isn't valid for multiple b-values */
if (GAGE_QUERY_ITEM_TEST(pvl->query, tenDwiGageADC)) {
double logdwi, logb0;
logb0 = log(AIR_MAX(kindData->valueMin,
pvl->directAnswer[tenDwiGageB0][0]));
for (dwiIdx=1; dwiIdx<pvl->kind->valLen; dwiIdx++) {
logdwi = log(AIR_MAX(kindData->valueMin,
pvl->directAnswer[tenDwiGageJustDWI][dwiIdx-1]));
pvl->directAnswer[tenDwiGageADC][dwiIdx-1]
= (logb0 - logdwi)/kindData->bval;
}
}
if (GAGE_QUERY_ITEM_TEST(pvl->query, tenDwiGageMeanDWIValue)) {
dwiMean = 0;
for (dwiIdx=1; dwiIdx<pvl->kind->valLen; dwiIdx++) {
dwiMean += dwiAll[dwiIdx];
}
dwiMean /= pvl->kind->valLen;
pvl->directAnswer[tenDwiGageMeanDWIValue][0] = dwiMean;
}
/* note: the gage interface to tenEstimate functionality
allows you exactly one kind of tensor estimation (per kind),
so the function call to do the estimation is actually
repeated over and over again; the copy into the answer
buffer is what changes... */
if (GAGE_QUERY_ITEM_TEST(pvl->query, tenDwiGageTensorLLS)) {
tenEstimate1TensorSingle_d(pvlData->tec1, tentmp, dwiAll);
TEN_T_COPY(pvl->directAnswer[tenDwiGageTensorLLS], tentmp);
}
if (GAGE_QUERY_ITEM_TEST(pvl->query, tenDwiGageTensorLLSError)) {
pvl->directAnswer[tenDwiGageTensorLLSError][0] = pvlData->tec1->errorDwi;
}
if (GAGE_QUERY_ITEM_TEST(pvl->query, tenDwiGageTensorLLSErrorLog)) {
pvl->directAnswer[tenDwiGageTensorLLSErrorLog][0]
= pvlData->tec1->errorLogDwi;
}
if (GAGE_QUERY_ITEM_TEST(pvl->query, tenDwiGageTensorWLS)) {
tenEstimate1TensorSingle_d(pvlData->tec1, tentmp, dwiAll);
TEN_T_COPY(pvl->directAnswer[tenDwiGageTensorWLS], tentmp);
}
if (GAGE_QUERY_ITEM_TEST(pvl->query, tenDwiGageTensorNLS)) {
tenEstimate1TensorSingle_d(pvlData->tec1, tentmp, dwiAll);
TEN_T_COPY(pvl->directAnswer[tenDwiGageTensorNLS], tentmp);
}
if (GAGE_QUERY_ITEM_TEST(pvl->query, tenDwiGageTensorMLE)) {
tenEstimate1TensorSingle_d(pvlData->tec1, tentmp, dwiAll);
TEN_T_COPY(pvl->directAnswer[tenDwiGageTensorMLE], tentmp);
}
/* HEY: have to implement all the different kinds of errors */
/* BEGIN sneakiness ........ */
if (GAGE_QUERY_ITEM_TEST(pvl->query, tenDwiGageTensor)) {
gageItemEntry *item;
item = pvl->kind->table + tenDwiGageTensor;
TEN_T_COPY(pvl->directAnswer[tenDwiGageTensor],
pvl->directAnswer[item->prereq[0]]);
}
if (GAGE_QUERY_ITEM_TEST(pvl->query, tenDwiGageTensorError)) {
gageItemEntry *item;
item = pvl->kind->table + tenDwiGageTensorError;
pvl->directAnswer[tenDwiGageTensorError][0]
= pvl->directAnswer[item->prereq[0]][0];
}
if (GAGE_QUERY_ITEM_TEST(pvl->query, tenDwiGageTensorErrorLog)) {
gageItemEntry *item;
item = pvl->kind->table + tenDwiGageTensorErrorLog;
pvl->directAnswer[tenDwiGageTensorErrorLog][0]
= pvl->directAnswer[item->prereq[0]][0];
}
if (GAGE_QUERY_ITEM_TEST(pvl->query, tenDwiGageTensorLikelihood)) {
gageItemEntry *item;
item = pvl->kind->table + tenDwiGageTensorLikelihood;
pvl->directAnswer[tenDwiGageTensorLikelihood][0]
= pvl->directAnswer[item->prereq[0]][0];
}
/* END sneakiness ........ */
if (GAGE_QUERY_ITEM_TEST(pvl->query, tenDwiGageFA)) {
pvl->directAnswer[tenDwiGageFA][0]
= pvl->directAnswer[tenDwiGageTensor][0]
* tenAnisoTen_d(pvl->directAnswer[tenDwiGageTensor],
tenAniso_FA);
}
if (GAGE_QUERY_ITEM_TEST(pvl->query, tenDwiGageTensorAllDWIError)) {
const double *grads;
int gradcount;
double *ten, d;
int i;
/* HEY: should switch to tenEstimate-based DWI simulation */
ten = pvl->directAnswer[tenDwiGageTensor];
gradcount = pvl->kind->valLen -1; /* Dont count b0 */
grads = ((const double*) kindData->ngrad->data) +3; /* Ignore b0 grad */
for( i=0; i < gradcount; i++ ) {
d = dwiAll[0]*exp(- pvlData->tec1->bValue
* TEN_T3V_CONTR(ten, grads + 3*i));
pvl->directAnswer[tenDwiGageTensorAllDWIError][i] = dwiAll[i+1] - d;
}
}
if (GAGE_QUERY_ITEM_TEST(pvl->query, tenDwiGage2TensorQSeg)) {
const double *grads;
int gradcount;
double *twoten;
unsigned int valIdx, E;
twoten = pvl->directAnswer[tenDwiGage2TensorQSeg];
gradcount = pvl->kind->valLen -1; /* Dont count b0 */
grads = ((const double*) kindData->ngrad->data) +3; /* Ignore b0 grad */
if (dwiAll[0] != 0) { /* S0 = 0 */
_tenQball(pvlData->tec2->bValue, gradcount, dwiAll, grads,
pvlData->qvals);
_tenQvals2points(gradcount, pvlData->qvals, grads, pvlData->qpoints);
_tenSegsamp2(gradcount, pvlData->qvals, grads, pvlData->qpoints,
pvlData->wght + 1, pvlData->dists );
} else {
/* stupid; should really return right here since data is garbage */
for (valIdx=1; valIdx < AIR_CAST(unsigned int, gradcount+1); valIdx++) {
pvlData->wght[valIdx] = valIdx % 2;
}
}
E = 0;
for (valIdx=1; valIdx<pvl->kind->valLen; valIdx++) {
if (!E) E |= tenEstimateSkipSet(pvlData->tec2, valIdx,
pvlData->wght[valIdx]);
}
if (!E) E |= tenEstimateUpdate(pvlData->tec2);
if (!E) E |= tenEstimate1TensorSingle_d(pvlData->tec2,
twoten + 0, dwiAll);
for (valIdx=1; valIdx<pvl->kind->valLen; valIdx++) {
if (!E) E |= tenEstimateSkipSet(pvlData->tec2, valIdx,
1 - pvlData->wght[valIdx]);
}
if (!E) E |= tenEstimateUpdate(pvlData->tec2);
if (!E) E |= tenEstimate1TensorSingle_d(pvlData->tec2,
twoten + 7, dwiAll);
if (E) {
fprintf(stderr, "!%s: (trouble) %s\n", me, biffGetDone(TEN));
}
/* hack: confidence for two-tensor fit */
twoten[0] = (twoten[0] + twoten[7])/2;
twoten[7] = 0.5; /* fraction that is the first tensor (initial value) */
/* twoten[1 .. 6] = first tensor */
/* twoten[8 .. 13] = second tensor */
/* Compute fraction between tensors if not garbage in this voxel */
if (twoten[0] > 0.5) {
double exp0,exp1,d,e=0,g=0, a=0,b=0;
int i;
for( i=0; i < gradcount; i++ ) {
exp0 = exp(-pvlData->tec2->bValue * TEN_T3V_CONTR(twoten + 0,
grads + 3*i));
exp1 = exp(-pvlData->tec2->bValue * TEN_T3V_CONTR(twoten + 7,
grads + 3*i));
d = dwiAll[i+1] / dwiAll[0];
e = exp0 - exp1;
g = d - exp1;
a += .5*e*e;
b += e*g;
}
twoten[7] = AIR_CLAMP(0, 0.5*(b/a), 1);
}
}
if (GAGE_QUERY_ITEM_TEST(pvl->query, tenDwiGage2TensorQSegError)) {
const double *grads;
int gradcount;
double *twoten, d;
int i;
/* HEY: should switch to tenEstimate-based DWI simulation */
if (dwiAll[0] != 0) { /* S0 = 0 */
twoten = pvl->directAnswer[tenDwiGage2TensorQSeg];
gradcount = pvl->kind->valLen -1; /* Dont count b0 */
grads = ((const double*) kindData->ngrad->data) +3; /* Ignore b0 grad */
pvl->directAnswer[tenDwiGage2TensorQSegError][0] = 0;
for( i=0; i < gradcount; i++ ) {
d = twoten[7]*exp(-pvlData->tec2->bValue * TEN_T3V_CONTR(twoten + 0,
grads + 3*i));
d += (1 - twoten[7])*exp(-pvlData->tec2->bValue
*TEN_T3V_CONTR(twoten + 7, grads + 3*i));
d = dwiAll[i+1]/dwiAll[0] - d;
pvl->directAnswer[tenDwiGage2TensorQSegError][0] += d*d;
}
pvl->directAnswer[tenDwiGage2TensorQSegError][0] =
sqrt( pvl->directAnswer[tenDwiGage2TensorQSegError][0] );
} else {
/* HEY: COMPLETELY WRONG!! An error is not defined! */
pvl->directAnswer[tenDwiGage2TensorQSegError][0] = 0;
}
/* printf("%f\n",pvl->directAnswer[tenDwiGage2TensorQSegError][0]); */
}
if (GAGE_QUERY_ITEM_TEST(pvl->query, tenDwiGage2TensorQSegAndError)) {
double *twoten, *err, *twotenerr;
twoten = pvl->directAnswer[tenDwiGage2TensorQSeg];
err = pvl->directAnswer[tenDwiGage2TensorQSegError];
twotenerr = pvl->directAnswer[tenDwiGage2TensorQSegAndError];
TEN_T_COPY(twotenerr + 0, twoten + 0);
TEN_T_COPY(twotenerr + 7, twoten + 7);
twotenerr[14] = err[0];
}
if (GAGE_QUERY_ITEM_TEST(pvl->query, tenDwiGage2TensorPeled)) {
#if TEEM_LEVMAR
#define PARAMS 4
double *twoTen, Cp /* , residual, AICSingFit, AICTwoFit */;
/* Vars for the NLLS */
double guess[PARAMS], loBnd[PARAMS], upBnd[PARAMS],
opts[LM_OPTS_SZ], *grad, *egrad, tenA[7], tenB[7],
matA[9], matB[9], matTmp[9], rott[9];
unsigned int gi;
int lmret;
/* Pointer to the location where the two tensor will be written */
twoTen = pvl->directAnswer[tenDwiGage2TensorPeled];
/* Estimate the DWI error, error is given as standard deviation */
pvlData->tec2->recordErrorDwi = AIR_FALSE;
/* Estimate the single tensor */
tenEstimate1TensorSingle_d(pvlData->tec2, pvlData->ten1, dwiAll);
/* Get the eigenValues and eigen vectors for this tensor */
tenEigensolve_d(pvlData->ten1Eval, pvlData->ten1Evec, pvlData->ten1);
/* Get westins Cp */
Cp = tenAnisoEval_d(pvlData->ten1Eval, tenAniso_Cp1);
/* Calculate the residual, need the variance to sqr it */
/* residual = pvlData->tec2->errorDwi*pvlData->tec2->errorDwi; */
/* Calculate the AIC for single tensor fit */
/* AICSingFit = _tenComputeAIC(residual, pvlData->tec2->dwiNum, 6); */
/* the CP-based test is gone; caller's responsibility */
/* rotate DW gradients by inverse of eigenvector column matrix
and place into pvlData->nten1EigenGrads (which has been
allocated by _tenDwiGagePvlDataNew()) */
grad = AIR_CAST(double *, kindData->ngrad->data);
egrad = AIR_CAST(double *, pvlData->nten1EigenGrads->data);
for (gi=0; gi<kindData->ngrad->axis[1].size; gi++) {
/* yes, this is also transforming some zero-length (B0) gradients;
that's harmless */
ELL_3MV_MUL(egrad, pvlData->ten1Evec, grad);
grad += 3;
egrad += 3;
}
/* Lower and upper bounds for the NLLS routine */
loBnd[0] = 0.0;
loBnd[1] = 0.0;
loBnd[2] = -AIR_PI/2;
loBnd[3] = -AIR_PI/2;
upBnd[0] = pvlData->ten1Eval[0]*5;
upBnd[1] = 1.0;
upBnd[2] = AIR_PI/2;
upBnd[3] = AIR_PI/2;
/* Starting point for the NLLS */
guess[0] = pvlData->ten1Eval[0];
guess[1] = 0.5;
guess[2] = AIR_PI/4;
guess[3] = -AIR_PI/4;
/*
guess[2] = AIR_AFFINE(0, airDrandMT_r(pvlData->randState), 1,
AIR_PI/6, AIR_PI/3);
guess[3] = AIR_AFFINE(0, airDrandMT_r(pvlData->randState), 1,
-AIR_PI/6, -AIR_PI/3);
*/
/* Fill in the constraints for the LM optimization,
the threshold of error difference */
opts[0] = pvlData->levmarTau;
opts[1] = pvlData->levmarEps1;
opts[2] = pvlData->levmarEps2;
opts[3] = pvlData->levmarEps3;
/* Very imp to set this opt, note that only forward
differences are used to approx Jacobian */
opts[4] = pvlData->levmarDelta;
/* run NLLS, results are stored back into guess[] */
pvlData->levmarUseFastExp = AIR_FALSE;
lmret = dlevmar_bc_dif(_tenLevmarPeledCB, guess, pvlData->tec2->dwi,
PARAMS, pvlData->tec2->dwiNum, loBnd, upBnd,
pvlData->levmarMaxIter, opts,
pvlData->levmarInfo,
NULL, NULL, pvlData);
if (-1 == lmret) {
ctx->errNum = 1;
sprintf(ctx->errStr, "%s: dlevmar_bc_dif() failed!", me);
} else {
/* Get the AIC for the two tensor fit, use the levmarinfo
to get the residual */
/*
residual = pvlData->levmarInfo[1]/pvlData->tec2->dwiNum;
AICTwoFit = _tenComputeAIC(residual, pvlData->tec2->dwiNum, 12);
*/
/* Form the tensors using the estimated pp, returned in guess */
_tenPeledRotate2D(tenA, guess[0], pvlData->ten1Eval[2], guess[2]);
_tenPeledRotate2D(tenB, guess[0], pvlData->ten1Eval[2], guess[3]);
TEN_T2M(matA, tenA);
TEN_T2M(matB, tenB);
ELL_3M_TRANSPOSE(rott, pvlData->ten1Evec);
ELL_3M_MUL(matTmp, matA, pvlData->ten1Evec);
ELL_3M_MUL(matA, rott, matTmp);
ELL_3M_MUL(matTmp, matB, pvlData->ten1Evec);
ELL_3M_MUL(matB, rott, matTmp);
/* Copy two two tensors */
/* guess[1] is population fraction of first tensor */
if (guess[1] > 0.5) {
twoTen[7] = guess[1];
TEN_M2T(twoTen + 0, matA);
TEN_M2T(twoTen + 7, matB);
} else {
twoTen[7] = 1 - guess[1];
TEN_M2T(twoTen + 0, matB);
TEN_M2T(twoTen + 7, matA);
}
twoTen[0] = 1;
}
#undef PARAMS
#else
double *twoTen;
twoTen = pvl->directAnswer[tenDwiGage2TensorPeled];
TEN_T_SET(twoTen + 0, AIR_NAN, AIR_NAN, AIR_NAN, AIR_NAN,
AIR_NAN, AIR_NAN, AIR_NAN);
TEN_T_SET(twoTen + 7, AIR_NAN, AIR_NAN, AIR_NAN, AIR_NAN,
AIR_NAN, AIR_NAN, AIR_NAN);
fprintf(stderr, "%s: sorry, not compiled with TEEM_LEVMAR\n", me);
#endif
}
