/
epi.py
1703 lines (1536 loc) · 56.5 KB
/
epi.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""The fsl module provides classes for interfacing with the `FSL
<http://www.fmrib.ox.ac.uk/fsl/index.html>`_ command line tools. This
was written to work with FSL version 5.0.4.
"""
import os
import numpy as np
import nibabel as nb
import warnings
from ...utils.filemanip import split_filename, fname_presuffix
from ..base import traits, TraitedSpec, InputMultiPath, File, isdefined
from .base import FSLCommand, FSLCommandInputSpec, Info
class PrepareFieldmapInputSpec(FSLCommandInputSpec):
scanner = traits.String(
"SIEMENS", argstr="%s", position=1, desc="must be SIEMENS", usedefault=True
)
in_phase = File(
exists=True,
argstr="%s",
position=2,
mandatory=True,
desc=("Phase difference map, in SIEMENS format range from 0-4096 or 0-8192)"),
)
in_magnitude = File(
exists=True,
argstr="%s",
position=3,
mandatory=True,
desc="Magnitude difference map, brain extracted",
)
delta_TE = traits.Float(
2.46,
usedefault=True,
mandatory=True,
argstr="%f",
position=-2,
desc=(
"echo time difference of the "
"fieldmap sequence in ms. (usually 2.46ms in"
" Siemens)"
),
)
nocheck = traits.Bool(
False,
position=-1,
argstr="--nocheck",
usedefault=True,
desc=("do not perform sanity checks for image size/range/dimensions"),
)
out_fieldmap = File(
argstr="%s", position=4, desc="output name for prepared fieldmap"
)
class PrepareFieldmapOutputSpec(TraitedSpec):
out_fieldmap = File(exists=True, desc="output name for prepared fieldmap")
class PrepareFieldmap(FSLCommand):
"""
Interface for the fsl_prepare_fieldmap script (FSL 5.0)
Prepares a fieldmap suitable for FEAT from SIEMENS data - saves output in
rad/s format (e.g. ```fsl_prepare_fieldmap SIEMENS
images_3_gre_field_mapping images_4_gre_field_mapping fmap_rads 2.65```).
Examples
--------
>>> from nipype.interfaces.fsl import PrepareFieldmap
>>> prepare = PrepareFieldmap()
>>> prepare.inputs.in_phase = "phase.nii"
>>> prepare.inputs.in_magnitude = "magnitude.nii"
>>> prepare.inputs.output_type = "NIFTI_GZ"
>>> prepare.cmdline # doctest: +ELLIPSIS
'fsl_prepare_fieldmap SIEMENS phase.nii magnitude.nii \
.../phase_fslprepared.nii.gz 2.460000'
>>> res = prepare.run() # doctest: +SKIP
"""
_cmd = "fsl_prepare_fieldmap"
input_spec = PrepareFieldmapInputSpec
output_spec = PrepareFieldmapOutputSpec
def _parse_inputs(self, skip=None):
if skip is None:
skip = []
if not isdefined(self.inputs.out_fieldmap):
self.inputs.out_fieldmap = self._gen_fname(
self.inputs.in_phase, suffix="_fslprepared"
)
if not isdefined(self.inputs.nocheck) or not self.inputs.nocheck:
skip += ["nocheck"]
return super()._parse_inputs(skip=skip)
def _list_outputs(self):
outputs = self.output_spec().get()
outputs["out_fieldmap"] = self.inputs.out_fieldmap
return outputs
def _run_interface(self, runtime):
runtime = super()._run_interface(runtime)
if runtime.returncode == 0:
out_file = self.inputs.out_fieldmap
im = nb.load(out_file)
dumb_img = nb.Nifti1Image(np.zeros(im.shape), im.affine, im.header)
out_nii = nb.funcs.concat_images((im, dumb_img))
nb.save(out_nii, out_file)
return runtime
class TOPUPInputSpec(FSLCommandInputSpec):
in_file = File(
exists=True,
mandatory=True,
desc="name of 4D file with images",
argstr="--imain=%s",
)
encoding_file = File(
exists=True,
mandatory=True,
xor=["encoding_direction"],
desc="name of text file with PE directions/times",
argstr="--datain=%s",
)
encoding_direction = traits.List(
traits.Enum("y", "x", "z", "x-", "y-", "z-"),
mandatory=True,
xor=["encoding_file"],
requires=["readout_times"],
argstr="--datain=%s",
desc=("encoding direction for automatic generation of encoding_file"),
)
readout_times = InputMultiPath(
traits.Float,
requires=["encoding_direction"],
xor=["encoding_file"],
mandatory=True,
desc=("readout times (dwell times by # phase-encode steps minus 1)"),
)
out_base = File(
desc=(
"base-name of output files (spline "
"coefficients (Hz) and movement parameters)"
),
name_source=["in_file"],
name_template="%s_base",
argstr="--out=%s",
hash_files=False,
)
out_field = File(
argstr="--fout=%s",
hash_files=False,
name_source=["in_file"],
name_template="%s_field",
desc="name of image file with field (Hz)",
)
out_warp_prefix = traits.Str(
"warpfield",
argstr="--dfout=%s",
hash_files=False,
desc="prefix for the warpfield images (in mm)",
usedefault=True,
)
out_mat_prefix = traits.Str(
"xfm",
argstr="--rbmout=%s",
hash_files=False,
desc="prefix for the realignment matrices",
usedefault=True,
)
out_jac_prefix = traits.Str(
"jac",
argstr="--jacout=%s",
hash_files=False,
desc="prefix for the warpfield images",
usedefault=True,
)
out_corrected = File(
argstr="--iout=%s",
hash_files=False,
name_source=["in_file"],
name_template="%s_corrected",
desc="name of 4D image file with unwarped images",
)
out_logfile = File(
argstr="--logout=%s",
desc="name of log-file",
name_source=["in_file"],
name_template="%s_topup.log",
keep_extension=True,
hash_files=False,
)
# TODO: the following traits admit values separated by commas, one value
# per registration level inside topup.
warp_res = traits.Float(
argstr="--warpres=%f",
desc=(
"(approximate) resolution (in mm) of warp "
"basis for the different sub-sampling levels"
),
)
subsamp = traits.Int(argstr="--subsamp=%d", desc="sub-sampling scheme")
fwhm = traits.Float(
argstr="--fwhm=%f", desc="FWHM (in mm) of gaussian smoothing kernel"
)
config = traits.String(
"b02b0.cnf",
argstr="--config=%s",
usedefault=True,
desc=("Name of config file specifying command line arguments"),
)
max_iter = traits.Int(argstr="--miter=%d", desc="max # of non-linear iterations")
reg_lambda = traits.Float(
argstr="--lambda=%0.f",
desc=(
"Weight of regularisation, default "
"depending on --ssqlambda and --regmod switches."
),
)
ssqlambda = traits.Enum(
1,
0,
argstr="--ssqlambda=%d",
desc=(
"Weight lambda by the current value of the "
"ssd. If used (=1), the effective weight of "
"regularisation term becomes higher for the "
"initial iterations, therefore initial steps"
" are a little smoother than they would "
"without weighting. This reduces the "
"risk of finding a local minimum."
),
)
regmod = traits.Enum(
"bending_energy",
"membrane_energy",
argstr="--regmod=%s",
desc=(
"Regularisation term implementation. Defaults "
"to bending_energy. Note that the two functions"
" have vastly different scales. The membrane "
"energy is based on the first derivatives and "
"the bending energy on the second derivatives. "
"The second derivatives will typically be much "
"smaller than the first derivatives, so input "
"lambda will have to be larger for "
"bending_energy to yield approximately the same"
" level of regularisation."
),
)
estmov = traits.Enum(1, 0, argstr="--estmov=%d", desc="estimate movements if set")
minmet = traits.Enum(
0,
1,
argstr="--minmet=%d",
desc=("Minimisation method 0=Levenberg-Marquardt, 1=Scaled Conjugate Gradient"),
)
splineorder = traits.Int(
argstr="--splineorder=%d",
desc=("order of spline, 2->Qadratic spline, 3->Cubic spline"),
)
numprec = traits.Enum(
"double",
"float",
argstr="--numprec=%s",
desc=("Precision for representing Hessian, double or float."),
)
interp = traits.Enum(
"spline",
"linear",
argstr="--interp=%s",
desc="Image interpolation model, linear or spline.",
)
scale = traits.Enum(
0,
1,
argstr="--scale=%d",
desc=("If set (=1), the images are individually scaled to a common mean"),
)
regrid = traits.Enum(
1,
0,
argstr="--regrid=%d",
desc=("If set (=1), the calculations are done in a different grid"),
)
class TOPUPOutputSpec(TraitedSpec):
out_fieldcoef = File(exists=True, desc="file containing the field coefficients")
out_movpar = File(exists=True, desc="movpar.txt output file")
out_enc_file = File(desc="encoding directions file output for applytopup")
out_field = File(desc="name of image file with field (Hz)")
out_warps = traits.List(File(exists=True), desc="warpfield images")
out_jacs = traits.List(File(exists=True), desc="Jacobian images")
out_mats = traits.List(File(exists=True), desc="realignment matrices")
out_corrected = File(desc="name of 4D image file with unwarped images")
out_logfile = File(desc="name of log-file")
class TOPUP(FSLCommand):
"""
Interface for FSL topup, a tool for estimating and correcting
susceptibility induced distortions. See FSL documentation for
`reference <http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/TOPUP>`_,
`usage examples
<http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/topup/ExampleTopupFollowedByApplytopup>`_,
and `exemplary config files
<https://github.com/ahheckel/FSL-scripts/blob/master/rsc/fsl/fsl4/topup/b02b0.cnf>`_.
Examples
--------
>>> from nipype.interfaces.fsl import TOPUP
>>> topup = TOPUP()
>>> topup.inputs.in_file = "b0_b0rev.nii"
>>> topup.inputs.encoding_file = "topup_encoding.txt"
>>> topup.inputs.output_type = "NIFTI_GZ"
>>> topup.cmdline # doctest: +ELLIPSIS
'topup --config=b02b0.cnf --datain=topup_encoding.txt \
--imain=b0_b0rev.nii --out=b0_b0rev_base --iout=b0_b0rev_corrected.nii.gz \
--fout=b0_b0rev_field.nii.gz --jacout=jac --logout=b0_b0rev_topup.log \
--rbmout=xfm --dfout=warpfield'
>>> res = topup.run() # doctest: +SKIP
"""
_cmd = "topup"
input_spec = TOPUPInputSpec
output_spec = TOPUPOutputSpec
def _format_arg(self, name, trait_spec, value):
if name == "encoding_direction":
return trait_spec.argstr % self._generate_encfile()
if name == "out_base":
path, name, ext = split_filename(value)
if path != "":
if not os.path.exists(path):
raise ValueError("out_base path must exist if provided")
return super()._format_arg(name, trait_spec, value)
def _list_outputs(self):
outputs = super()._list_outputs()
del outputs["out_base"]
base_path = None
if isdefined(self.inputs.out_base):
base_path, base, _ = split_filename(self.inputs.out_base)
if base_path == "":
base_path = None
else:
base = split_filename(self.inputs.in_file)[1] + "_base"
outputs["out_fieldcoef"] = self._gen_fname(
base, suffix="_fieldcoef", cwd=base_path
)
outputs["out_movpar"] = self._gen_fname(
base, suffix="_movpar", ext=".txt", cwd=base_path
)
n_vols = nb.load(self.inputs.in_file).shape[-1]
ext = Info.output_type_to_ext(self.inputs.output_type)
fmt = os.path.abspath("{prefix}_{i:02d}{ext}").format
outputs["out_warps"] = [
fmt(prefix=self.inputs.out_warp_prefix, i=i, ext=ext)
for i in range(1, n_vols + 1)
]
outputs["out_jacs"] = [
fmt(prefix=self.inputs.out_jac_prefix, i=i, ext=ext)
for i in range(1, n_vols + 1)
]
outputs["out_mats"] = [
fmt(prefix=self.inputs.out_mat_prefix, i=i, ext=".mat")
for i in range(1, n_vols + 1)
]
if isdefined(self.inputs.encoding_direction):
outputs["out_enc_file"] = self._get_encfilename()
return outputs
def _get_encfilename(self):
out_file = os.path.join(
os.getcwd(), ("%s_encfile.txt" % split_filename(self.inputs.in_file)[1])
)
return out_file
def _generate_encfile(self):
"""Generate a topup compatible encoding file based on given directions"""
out_file = self._get_encfilename()
durations = self.inputs.readout_times
if len(self.inputs.encoding_direction) != len(durations):
if len(self.inputs.readout_times) != 1:
raise ValueError(
"Readout time must be a float or match the"
"length of encoding directions"
)
durations = durations * len(self.inputs.encoding_direction)
lines = []
for idx, encdir in enumerate(self.inputs.encoding_direction):
direction = 1.0
if encdir.endswith("-"):
direction = -1.0
line = [
float(val[0] == encdir[0]) * direction for val in ["x", "y", "z"]
] + [durations[idx]]
lines.append(line)
np.savetxt(out_file, np.array(lines), fmt=b"%d %d %d %.8f")
return out_file
def _overload_extension(self, value, name=None):
if name == "out_base":
return value
return super()._overload_extension(value, name)
class ApplyTOPUPInputSpec(FSLCommandInputSpec):
in_files = InputMultiPath(
File(exists=True),
mandatory=True,
desc="name of file with images",
argstr="--imain=%s",
sep=",",
)
encoding_file = File(
exists=True,
mandatory=True,
desc="name of text file with PE directions/times",
argstr="--datain=%s",
)
in_index = traits.List(
traits.Int,
argstr="--inindex=%s",
sep=",",
desc="comma separated list of indices corresponding to --datain",
)
in_topup_fieldcoef = File(
exists=True,
argstr="--topup=%s",
copyfile=False,
requires=["in_topup_movpar"],
desc=("topup file containing the field coefficients"),
)
in_topup_movpar = File(
exists=True,
requires=["in_topup_fieldcoef"],
copyfile=False,
desc="topup movpar.txt file",
)
out_corrected = File(
desc="output (warped) image",
name_source=["in_files"],
name_template="%s_corrected",
argstr="--out=%s",
)
method = traits.Enum(
"jac",
"lsr",
argstr="--method=%s",
desc=("use jacobian modulation (jac) or least-squares resampling (lsr)"),
)
interp = traits.Enum(
"trilinear", "spline", argstr="--interp=%s", desc="interpolation method"
)
datatype = traits.Enum(
"char",
"short",
"int",
"float",
"double",
argstr="-d=%s",
desc="force output data type",
)
class ApplyTOPUPOutputSpec(TraitedSpec):
out_corrected = File(
exists=True, desc=("name of 4D image file with unwarped images")
)
class ApplyTOPUP(FSLCommand):
"""
Interface for FSL topup, a tool for estimating and correcting
susceptibility induced distortions.
`General reference
<http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/topup/ApplytopupUsersGuide>`_
and `use example
<http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/topup/ExampleTopupFollowedByApplytopup>`_.
Examples
--------
>>> from nipype.interfaces.fsl import ApplyTOPUP
>>> applytopup = ApplyTOPUP()
>>> applytopup.inputs.in_files = ["epi.nii", "epi_rev.nii"]
>>> applytopup.inputs.encoding_file = "topup_encoding.txt"
>>> applytopup.inputs.in_topup_fieldcoef = "topup_fieldcoef.nii.gz"
>>> applytopup.inputs.in_topup_movpar = "topup_movpar.txt"
>>> applytopup.inputs.output_type = "NIFTI_GZ"
>>> applytopup.cmdline # doctest: +ELLIPSIS
'applytopup --datain=topup_encoding.txt --imain=epi.nii,epi_rev.nii \
--inindex=1,2 --topup=topup --out=epi_corrected.nii.gz'
>>> res = applytopup.run() # doctest: +SKIP
"""
_cmd = "applytopup"
input_spec = ApplyTOPUPInputSpec
output_spec = ApplyTOPUPOutputSpec
def _parse_inputs(self, skip=None):
if skip is None:
skip = []
# If not defined, assume index are the first N entries in the
# parameters file, for N input images.
if not isdefined(self.inputs.in_index):
self.inputs.in_index = list(range(1, len(self.inputs.in_files) + 1))
return super()._parse_inputs(skip=skip)
def _format_arg(self, name, spec, value):
if name == "in_topup_fieldcoef":
return spec.argstr % value.split("_fieldcoef")[0]
return super()._format_arg(name, spec, value)
class EddyInputSpec(FSLCommandInputSpec):
in_file = File(
exists=True,
mandatory=True,
argstr="--imain=%s",
desc="File containing all the images to estimate distortions for",
)
in_mask = File(
exists=True, mandatory=True, argstr="--mask=%s", desc="Mask to indicate brain"
)
in_index = File(
exists=True,
mandatory=True,
argstr="--index=%s",
desc="File containing indices for all volumes in --imain "
"into --acqp and --topup",
)
in_acqp = File(
exists=True,
mandatory=True,
argstr="--acqp=%s",
desc="File containing acquisition parameters",
)
in_bvec = File(
exists=True,
mandatory=True,
argstr="--bvecs=%s",
desc="File containing the b-vectors for all volumes in --imain",
)
in_bval = File(
exists=True,
mandatory=True,
argstr="--bvals=%s",
desc="File containing the b-values for all volumes in --imain",
)
out_base = traits.Str(
default_value="eddy_corrected",
usedefault=True,
argstr="--out=%s",
desc="Basename for output image",
)
session = File(
exists=True,
argstr="--session=%s",
desc="File containing session indices for all volumes in --imain",
)
in_topup_fieldcoef = File(
exists=True,
argstr="--topup=%s",
requires=["in_topup_movpar"],
desc="Topup results file containing the field coefficients",
)
in_topup_movpar = File(
exists=True,
requires=["in_topup_fieldcoef"],
desc="Topup results file containing the movement parameters (movpar.txt)",
)
field = File(
exists=True, argstr="--field=%s", desc="Non-topup derived fieldmap scaled in Hz"
)
field_mat = File(
exists=True,
argstr="--field_mat=%s",
desc="Matrix specifying the relative positions of the fieldmap, "
"--field, and the first volume of the input file, --imain",
)
flm = traits.Enum(
"quadratic",
"linear",
"cubic",
usedefault=True,
argstr="--flm=%s",
desc="First level EC model",
)
slm = traits.Enum(
"none",
"linear",
"quadratic",
usedefault=True,
argstr="--slm=%s",
desc="Second level EC model",
)
fep = traits.Bool(
False, argstr="--fep", desc="Fill empty planes in x- or y-directions"
)
initrand = traits.Bool(
False,
argstr="--initrand",
desc="Resets rand for when selecting voxels",
min_ver="5.0.10",
)
interp = traits.Enum(
"spline",
"trilinear",
usedefault=True,
argstr="--interp=%s",
desc="Interpolation model for estimation step",
)
nvoxhp = traits.Int(
default_value=1000,
usedefault=True,
argstr="--nvoxhp=%s",
desc="# of voxels used to estimate the hyperparameters",
)
fudge_factor = traits.Float(
default_value=10.0,
usedefault=True,
argstr="--ff=%s",
desc="Fudge factor for hyperparameter error variance",
)
dont_sep_offs_move = traits.Bool(
False,
argstr="--dont_sep_offs_move",
desc="Do NOT attempt to separate field offset from subject movement",
)
dont_peas = traits.Bool(
False,
argstr="--dont_peas",
desc="Do NOT perform a post-eddy alignment of shells",
)
fwhm = traits.Float(
desc="FWHM for conditioning filter when estimating the parameters",
argstr="--fwhm=%s",
)
niter = traits.Int(
5, usedefault=True, argstr="--niter=%s", desc="Number of iterations"
)
method = traits.Enum(
"jac",
"lsr",
usedefault=True,
argstr="--resamp=%s",
desc="Final resampling method (jacobian/least squares)",
)
repol = traits.Bool(
False, argstr="--repol", desc="Detect and replace outlier slices"
)
outlier_nstd = traits.Int(
argstr="--ol_nstd",
desc="Number of std off to qualify as outlier",
requires=["repol"],
min_ver="5.0.10",
)
outlier_nvox = traits.Int(
argstr="--ol_nvox",
desc="Min # of voxels in a slice for inclusion in outlier detection",
requires=["repol"],
min_ver="5.0.10",
)
outlier_type = traits.Enum(
"sw",
"gw",
"both",
argstr="--ol_type",
desc="Type of outliers, slicewise (sw), groupwise (gw) or both (both)",
requires=["repol"],
min_ver="5.0.10",
)
outlier_pos = traits.Bool(
False,
argstr="--ol_pos",
desc="Consider both positive and negative outliers if set",
requires=["repol"],
min_ver="5.0.10",
)
outlier_sqr = traits.Bool(
False,
argstr="--ol_sqr",
desc="Consider outliers among sums-of-squared differences if set",
requires=["repol"],
min_ver="5.0.10",
)
multiband_factor = traits.Int(
argstr="--mb=%s", desc="Multi-band factor", min_ver="5.0.10"
)
multiband_offset = traits.Enum(
0,
1,
-1,
argstr="--mb_offs=%d",
desc="Multi-band offset (-1 if bottom slice removed, 1 if top slice removed",
requires=["multiband_factor"],
min_ver="5.0.10",
)
mporder = traits.Int(
argstr="--mporder=%s",
desc="Order of slice-to-vol movement model",
requires=["use_cuda"],
min_ver="5.0.11",
)
slice2vol_niter = traits.Int(
argstr="--s2v_niter=%d",
desc="Number of iterations for slice-to-vol",
requires=["mporder"],
min_ver="5.0.11",
)
slice2vol_lambda = traits.Int(
argstr="--s2v_lambda=%d",
desc="Regularisation weight for slice-to-vol movement (reasonable range 1-10)",
requires=["mporder"],
min_ver="5.0.11",
)
slice2vol_interp = traits.Enum(
"trilinear",
"spline",
argstr="--s2v_interp=%s",
desc="Slice-to-vol interpolation model for estimation step",
requires=["mporder"],
min_ver="5.0.11",
)
slice_order = traits.File(
exists=True,
argstr="--slspec=%s",
desc="Name of text file completely specifying slice/group acquisition",
requires=["mporder"],
xor=["json"],
min_ver="5.0.11",
)
json = traits.File(
exists=True,
argstr="--json=%s",
desc="Name of .json text file with information about slice timing",
requires=["mporder"],
xor=["slice_order"],
min_ver="6.0.1",
)
estimate_move_by_susceptibility = traits.Bool(
False,
argstr="--estimate_move_by_susceptibility",
desc="Estimate how susceptibility field changes with subject movement",
min_ver="6.0.1",
)
mbs_niter = traits.Int(
argstr="--mbs_niter=%s",
desc="Number of iterations for MBS estimation",
requires=["estimate_move_by_susceptibility"],
min_ver="6.0.1",
)
mbs_lambda = traits.Int(
argstr="--mbs_lambda=%s",
desc="Weighting of regularisation for MBS estimation",
requires=["estimate_move_by_susceptibility"],
min_ver="6.0.1",
)
mbs_ksp = traits.Int(
argstr="--mbs_ksp=%smm",
desc="Knot-spacing for MBS field estimation",
requires=["estimate_move_by_susceptibility"],
min_ver="6.0.1",
)
num_threads = traits.Int(
1, usedefault=True, nohash=True, desc="Number of openmp threads to use"
)
is_shelled = traits.Bool(
False,
argstr="--data_is_shelled",
desc="Override internal check to ensure that date are acquired "
"on a set of b-value shells",
)
use_cuda = traits.Bool(False, desc="Run eddy using cuda gpu")
cnr_maps = traits.Bool(
False, desc="Output CNR-Maps", argstr="--cnr_maps", min_ver="5.0.10"
)
residuals = traits.Bool(
False, desc="Output Residuals", argstr="--residuals", min_ver="5.0.10"
)
class EddyOutputSpec(TraitedSpec):
out_corrected = File(
exists=True, desc="4D image file containing all the corrected volumes"
)
out_parameter = File(
exists=True,
desc="Text file with parameters defining the field and movement for each scan",
)
out_rotated_bvecs = File(
exists=True, desc="File containing rotated b-values for all volumes"
)
out_movement_rms = File(
exists=True, desc="Summary of the 'total movement' in each volume"
)
out_restricted_movement_rms = File(
exists=True,
desc="Summary of the 'total movement' in each volume "
"disregarding translation in the PE direction",
)
out_shell_alignment_parameters = File(
exists=True,
desc="Text file containing rigid body movement parameters "
"between the different shells as estimated by a "
"post-hoc mutual information based registration",
)
out_shell_pe_translation_parameters = File(
exists=True,
desc="Text file containing translation along the PE-direction "
"between the different shells as estimated by a "
"post-hoc mutual information based registration",
)
out_outlier_map = File(
exists=True,
desc="Matrix where rows represent volumes and columns represent "
'slices. "0" indicates that scan-slice is not an outlier '
'and "1" indicates that it is',
)
out_outlier_n_stdev_map = File(
exists=True,
desc="Matrix where rows represent volumes and columns represent "
"slices. Values indicate number of standard deviations off the "
"mean difference between observation and prediction is",
)
out_outlier_n_sqr_stdev_map = File(
exists=True,
desc="Matrix where rows represent volumes and columns represent "
"slices. Values indicate number of standard deivations off the "
"square root of the mean squared difference between observation "
"and prediction is",
)
out_outlier_report = File(
exists=True,
desc="Text file with a plain language report on what "
"outlier slices eddy has found",
)
out_outlier_free = File(
exists=True,
desc="4D image file not corrected for susceptibility or eddy-"
"current distortions or subject movement but with outlier "
"slices replaced",
)
out_movement_over_time = File(
exists=True,
desc="Text file containing translations (mm) and rotations "
"(radians) for each excitation",
)
out_cnr_maps = File(exists=True, desc="path/name of file with the cnr_maps")
out_residuals = File(exists=True, desc="path/name of file with the residuals")
class Eddy(FSLCommand):
"""
Interface for FSL eddy, a tool for estimating and correcting eddy
currents induced distortions. `User guide
<https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/eddy/UsersGuide>`__ and
`more info regarding acqp file
<https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/eddy/Faq#How_do_I_know_what_to_put_into_my_--acqp_file.3F>`_.
Examples
--------
>>> from nipype.interfaces.fsl import Eddy
Running eddy on a CPU using OpenMP:
>>> eddy = Eddy()
>>> eddy.inputs.in_file = 'epi.nii'
>>> eddy.inputs.in_mask = 'epi_mask.nii'
>>> eddy.inputs.in_index = 'epi_index.txt'
>>> eddy.inputs.in_acqp = 'epi_acqp.txt'
>>> eddy.inputs.in_bvec = 'bvecs.scheme'
>>> eddy.inputs.in_bval = 'bvals.scheme'
>>> eddy.cmdline # doctest: +ELLIPSIS
'eddy_openmp --flm=quadratic --ff=10.0 \
--acqp=epi_acqp.txt --bvals=bvals.scheme --bvecs=bvecs.scheme \
--imain=epi.nii --index=epi_index.txt --mask=epi_mask.nii \
--interp=spline --resamp=jac --niter=5 --nvoxhp=1000 \
--out=.../eddy_corrected --slm=none'
Running eddy on an Nvidia GPU using cuda:
>>> eddy.inputs.use_cuda = True
>>> eddy.cmdline # doctest: +ELLIPSIS
'eddy_cuda --flm=quadratic --ff=10.0 \
--acqp=epi_acqp.txt --bvals=bvals.scheme --bvecs=bvecs.scheme \
--imain=epi.nii --index=epi_index.txt --mask=epi_mask.nii \
--interp=spline --resamp=jac --niter=5 --nvoxhp=1000 \
--out=.../eddy_corrected --slm=none'
Running eddy with slice-to-volume motion correction:
>>> eddy.inputs.mporder = 6
>>> eddy.inputs.slice2vol_niter = 5
>>> eddy.inputs.slice2vol_lambda = 1
>>> eddy.inputs.slice2vol_interp = 'trilinear'
>>> eddy.inputs.slice_order = 'epi_slspec.txt'
>>> eddy.cmdline # doctest: +ELLIPSIS
'eddy_cuda --flm=quadratic --ff=10.0 \
--acqp=epi_acqp.txt --bvals=bvals.scheme --bvecs=bvecs.scheme \
--imain=epi.nii --index=epi_index.txt --mask=epi_mask.nii \
--interp=spline --resamp=jac --mporder=6 --niter=5 --nvoxhp=1000 \
--out=.../eddy_corrected --s2v_interp=trilinear --s2v_lambda=1 \
--s2v_niter=5 --slspec=epi_slspec.txt --slm=none'
>>> res = eddy.run() # doctest: +SKIP
"""
_cmd = "eddy_openmp"
input_spec = EddyInputSpec
output_spec = EddyOutputSpec
_num_threads = 1
def __init__(self, **inputs):
super().__init__(**inputs)
self.inputs.on_trait_change(self._num_threads_update, "num_threads")
if not isdefined(self.inputs.num_threads):
self.inputs.num_threads = self._num_threads
else:
self._num_threads_update()
self.inputs.on_trait_change(self._use_cuda, "use_cuda")
if isdefined(self.inputs.use_cuda):
self._use_cuda()
def _num_threads_update(self):
self._num_threads = self.inputs.num_threads
if not isdefined(self.inputs.num_threads):
if "OMP_NUM_THREADS" in self.inputs.environ:
del self.inputs.environ["OMP_NUM_THREADS"]
else:
self.inputs.environ["OMP_NUM_THREADS"] = str(self.inputs.num_threads)
def _use_cuda(self):
self._cmd = "eddy_cuda" if self.inputs.use_cuda else "eddy_openmp"
def _run_interface(self, runtime):
# If 'eddy_openmp' is missing, use 'eddy'
FSLDIR = os.getenv("FSLDIR", "")
cmd = self._cmd
if all(
(
FSLDIR != "",
cmd == "eddy_openmp",
not os.path.exists(os.path.join(FSLDIR, "bin", cmd)),
)
):
self._cmd = "eddy"
runtime = super()._run_interface(runtime)
# Restore command to avoid side-effects
self._cmd = cmd
return runtime
def _format_arg(self, name, spec, value):
if name == "in_topup_fieldcoef":
return spec.argstr % value.split("_fieldcoef")[0]
if name == "field":
return spec.argstr % fname_presuffix(value, use_ext=False)
if name == "out_base":
return spec.argstr % os.path.abspath(value)
return super()._format_arg(name, spec, value)
def _list_outputs(self):
outputs = self.output_spec().get()
outputs["out_corrected"] = os.path.abspath("%s.nii.gz" % self.inputs.out_base)
outputs["out_parameter"] = os.path.abspath(
"%s.eddy_parameters" % self.inputs.out_base
)