1 /*
2   Teem: Tools to process and visualize scientific data and images             .
3   Copyright (C) 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 models from a set of DW images"
28 static const char *_tend_mfitInfoL =
29   (INFO
30    ". More docs here.");
31 
32 int
tend_mfitMain(int argc,const char ** argv,const char * me,hestParm * hparm)33 tend_mfitMain(int argc, const char **argv, const char *me,
34               hestParm *hparm) {
35   int pret;
36   hestOpt *hopt = NULL;
37   char *perr, *err;
38   airArray *mop;
39 
40   Nrrd *nin, *nout, *nterr, *nconv, *niter;
41   char *outS, *terrS, *convS, *iterS, *modS;
42   int knownB0, saveB0, verbose, mlfit, typeOut;
43   unsigned int maxIter, minIter, starts;
44   double sigma, eps;
45   const tenModel *model;
46   tenExperSpec *espec;
47 
48   hestOptAdd(&hopt, "v", "verbose", airTypeInt, 1, 1, &verbose, "0",
49              "verbosity level");
50   hestOptAdd(&hopt, "m", "model", airTypeString, 1, 1, &modS, NULL,
51              "which model to fit. Use optional \"b0+\" prefix to "
52              "indicate that the B0 image should also be saved "
53              "(independent of whether it was known or had to be "
54              "estimated, according to \"-knownB0\").");
55   hestOptAdd(&hopt, "ns", "# starts", airTypeUInt, 1, 1, &starts, "1",
56              "number of random starting points at which to initialize "
57              "fitting");
58   hestOptAdd(&hopt, "ml", NULL, airTypeInt, 0, 0, &mlfit, NULL,
59              "do ML fitting, rather than least-squares, which also "
60              "requires setting \"-sigma\"");
61   hestOptAdd(&hopt, "sigma", "sigma", airTypeDouble, 1, 1, &sigma, "nan",
62              "Gaussian/Rician noise parameter");
63   hestOptAdd(&hopt, "eps", "eps", airTypeDouble, 1, 1, &eps, "0.01",
64              "convergence epsilon");
65   hestOptAdd(&hopt, "mini", "min iters", airTypeUInt, 1, 1, &minIter, "3",
66              "minimum required # iterations for fitting.");
67   hestOptAdd(&hopt, "maxi", "max iters", airTypeUInt, 1, 1, &maxIter, "100",
68              "maximum allowable # iterations for fitting.");
69   hestOptAdd(&hopt, "knownB0", "bool", airTypeBool, 1, 1, &knownB0, NULL,
70              "Indicates if the B=0 non-diffusion-weighted reference image "
71              "is known (\"true\") because it appears one or more times "
72              "amongst the DWIs, or, if it has to be estimated along with "
73              "the other model parameters (\"false\")");
74   /* (this is now specified as part of the "-m" model description)
75   hestOptAdd(&hopt, "saveB0", "bool", airTypeBool, 1, 1, &saveB0, NULL,
76              "Indicates if the B=0 non-diffusion-weighted value "
77              "should be saved in output, regardless of whether it was "
78              "known or had to be esimated");
79   */
80   hestOptAdd(&hopt, "t", "type", airTypeEnum, 1, 1, &typeOut, "float",
81              "output type of model parameters",
82              NULL, nrrdType);
83   hestOptAdd(&hopt, "i", "dwi", airTypeOther, 1, 1, &nin, "-",
84              "all the diffusion-weighted images in one 4D nrrd",
85              NULL, NULL, nrrdHestNrrd);
86   hestOptAdd(&hopt, "o", "nout", airTypeString, 1, 1, &outS, "-",
87              "output parameter vector image");
88   hestOptAdd(&hopt, "eo", "filename", airTypeString, 1, 1, &terrS, "",
89              "Giving a filename here allows you to save out the per-sample "
90              "fitting error.  By default, no such error is saved.");
91   hestOptAdd(&hopt, "co", "filename", airTypeString, 1, 1, &convS, "",
92              "Giving a filename here allows you to save out the per-sample "
93              "convergence fraction.  By default, no such error is saved.");
94   hestOptAdd(&hopt, "io", "filename", airTypeString, 1, 1, &iterS, "",
95              "Giving a filename here allows you to save out the per-sample "
96              "number of iterations needed for fitting.  "
97              "By default, no such error is saved.");
98 
99   mop = airMopNew();
100   airMopAdd(mop, hopt, (airMopper)hestOptFree, airMopAlways);
101   USAGE(_tend_mfitInfoL);
102   JUSTPARSE();
103   airMopAdd(mop, hopt, (airMopper)hestParseFree, airMopAlways);
104 
105   nterr = NULL;
106   nconv = NULL;
107   niter = NULL;
108   espec = tenExperSpecNew();
109   airMopAdd(mop, espec, (airMopper)tenExperSpecNix, airMopAlways);
110   nout = nrrdNew();
111   airMopAdd(mop, nout, (airMopper)nrrdNuke, airMopAlways);
112   if (tenModelParse(&model, &saveB0, AIR_FALSE, modS)) {
113     airMopAdd(mop, err=biffGetDone(TEN), airFree, airMopAlways);
114     fprintf(stderr, "%s: trouble parsing model \"%s\":\n%s\n", me, modS, err);
115     airMopError(mop); return 1;
116   }
117   if (tenExperSpecFromKeyValueSet(espec, nin)) {
118     airMopAdd(mop, err=biffGetDone(TEN), airFree, airMopAlways);
119     fprintf(stderr, "%s: trouble getting exper from kvp:\n%s\n", me, err);
120     airMopError(mop); return 1;
121   }
122   if (tenModelSqeFit(nout,
123                      airStrlen(terrS) ? &nterr : NULL,
124                      airStrlen(convS) ? &nconv : NULL,
125                      airStrlen(iterS) ? &niter : NULL,
126                      model, espec, nin,
127                      knownB0, saveB0, typeOut,
128                      minIter, maxIter, starts, eps,
129                      NULL, verbose)) {
130     airMopAdd(mop, err=biffGetDone(TEN), airFree, airMopAlways);
131     fprintf(stderr, "%s: trouble fitting:\n%s\n", me, err);
132     airMopError(mop); return 1;
133   }
134 
135   if (nrrdSave(outS, nout, NULL)
136       || (airStrlen(terrS) && nrrdSave(terrS, nterr, NULL))
137       || (airStrlen(convS) && nrrdSave(convS, nconv, NULL))
138       || (airStrlen(iterS) && nrrdSave(iterS, niter, NULL))) {
139     airMopAdd(mop, err=biffGetDone(NRRD), airFree, airMopAlways);
140     fprintf(stderr, "%s: trouble writing output:\n%s\n", me, err);
141     airMopError(mop); return 1;
142   }
143 
144   airMopOkay(mop);
145   return 0;
146 }
147 /* TEND_CMD(mfit, INFO); */
148 unrrduCmd tend_mfitCmd = { "mfit", INFO, tend_mfitMain };
149