cMRF example

[rkcatarina]

No description provided.

[github-actions]

Coverage Report

File	Stmts	Miss	Cover	Missing
src/mrpro
_version.py	6	2	67%	7–8
src/mrpro/algorithms/csm
inati.py	24	1	96%	44
walsh.py	16	1	94%	53
src/mrpro/algorithms/dcf
dcf_voronoi.py	55	4	93%	15, 55–56, 89
src/mrpro/algorithms/optimizers
adam.py	22	1	95%	104
pdhg.py	81	3	96%	178–179, 185
pgd.py	53	4	92%	107, 152–155
src/mrpro/algorithms/reconstruction
DirectReconstruction.py	28	16	43%	59–79, 93
IterativeSENSEReconstruction.py	13	1	92%	79
Reconstruction.py	50	22	56%	42, 54–56, 80–87, 108–117
RegularizedIterativeSENSEReconstruction.py	41	17	59%	97–101, 115–140
src/mrpro/data
AcqInfo.py	145	5	97%	42, 121–122, 124, 233
CheckDataMixin.py	344	40	88%	90, 96, 98, 115, 123, 125, 127, 160–161, 184, 193, 210, 238, 254, 302, 307, 313–318, 348, 355, 446, 448, 480, 482, 484, 488, 504, 545, 550, 627, 649–653, 685, 726–729, 754
CsmData.py	28	3	89%	13, 82–84
DcfData.py	44	8	82%	16, 64, 76–81
EncodingLimits.py	73	3	96%	33, 123, 126
IData.py	59	8	86%	118, 132, 159–167
IHeader.py	136	12	91%	69–72, 253, 257, 261, 265, 299–303
KData.py	201	29	86%	113–114, 129, 136, 147–157, 166, 174, 183, 217, 239–241, 277–278, 333–344, 473, 475, 545
KHeader.py	141	21	85%	23, 108–114, 141, 189, 196–197, 200, 207, 224–231, 239–250
KNoise.py	31	15	52%	39–52, 56–61
KTrajectory.py	108	3	97%	166, 168, 188
MoveDataMixin.py	140	18	87%	28, 126, 142, 156–158, 220, 336–338, 351, 430, 450–451, 453, 468–469, 471
QData.py	39	7	82%	42, 65–73
ReduceRepeatMixin.py	33	2	94%	51–52
Rotation.py	719	35	95%	101, 199, 336, 434, 478, 496, 583, 585, 594, 628, 630, 693, 770, 775, 778, 793, 810, 815, 891, 1079, 1084, 1087, 1111, 1115, 1243, 1245, 1253–1254, 1318, 1400, 1703, 1855, 1890, 1894, 2084
SpatialDimension.py	232	20	91%	34, 104, 148, 154, 274–276, 289–291, 325, 343, 356, 369, 382, 395, 404–405, 420, 429
acq_filters.py	12	1	92%	47
src/mrpro/data/traj_calculators
KTrajectoryCalculator.py	27	1	96%	84
KTrajectoryIsmrmrd.py	19	1	95%	57
src/mrpro/operators
AveragingOp.py	34	2	94%	53, 78
CartesianSamplingOp.py	96	4	96%	124, 163, 238, 305
ConstraintsOp.py	60	2	97%	46, 48
DictionaryMatchOp.py	52	6	88%	98–102, 122
EndomorphOperator.py	32	2	94%	52, 58
FiniteDifferenceOp.py	27	2	93%	40, 105
FourierOp.py	90	3	97%	185, 257, 262
Functional.py	77	9	88%	20–22, 117, 119, 226–228, 242
GridSamplingOp.py	136	9	93%	71–72, 81–82, 89–90, 93, 95, 97
LinearOperator.py	201	8	96%	107, 217, 244, 251, 292, 301, 309, 326
LinearOperatorMatrix.py	162	19	88%	82, 119, 152, 161, 166, 175–178, 191–194, 202–203, 208–209, 221, 310, 337, 364
MultiIdentityOp.py	13	2	85%	43, 48
NonUniformFastFourierOp.py	188	10	95%	69, 96, 206, 208, 241, 243, 319, 373, 423, 428
Operator.py	79	2	97%	32, 88
ProximableFunctionalSeparableSum.py	39	3	92%	50, 103, 110
SliceProjectionOp.py	174	8	95%	45, 62, 64, 70, 206, 227, 260, 300
WaveletOp.py	119	5	96%	151, 169, 204, 209, 232
ZeroPadOp.py	16	1	94%	30
src/mrpro/operators/models
EPG.py	205	38	81%	88–100, 148–152, 172–175, 195–199, 245, 250, 266–268, 284–285, 290, 310, 315, 335, 340, 553, 658
cMRF.py	33	1	97%	71
src/mrpro/phantoms
brainweb.py	275	31	89%	325–335, 371, 427–430, 452–453, 458–459, 461–462, 466, 474, 481–482, 520, 586–587, 606–609, 620, 622, 657–658, 671
src/mrpro/utils
filters.py	62	2	97%	44, 49
getnested.py	37	37	0%	3–76
indexing.py	170	1	99%	331
reshape.py	97	2	98%	112, 303
slice_profiles.py	47	6	87%	21, 37, 116–119, 152
sliding_window.py	34	1	97%	34
split_idx.py	10	2	80%	43, 47
summarize_tensorvalues.py	12	3	75%	23, 25, 27
typing.py	69	55	20%	9–235
unit_conversion.py	60	14	77%	32, 40, 42, 49, 51, 58, 60, 69, 80, 82, 101, 103, 124, 126
zero_pad_or_crop.py	31	6	81%	26, 30, 55, 58, 61, 64
TOTAL	6654	600	91%

Tests	Skipped	Failures	Errors	Time
2799	0 💤	0 ❌	0 🔥	1m 50s ⏱️

[github-actions]

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[fzimmermann89]

Please try to upload the data and mask from one scanner (unzipped) to zenodo.

Then, use the same code as in the other notebooks to download from zenodo.

Why is the reconstruction a function?

Do you think it is possible to add a bit more high level explanation between in code? Like how you would describe it in a thesis, for example. Less practical, programming, more "use extended phase graphs (reference).." if you know what I mean.

Could we add the results of the reference scans, like what the expected t1 and t2 values are for this phantom? I would hard-code the reference results. use the mask to obtain the values from the fingerprinting measurement. And then do a plot of fingerprinting over reference in the end.

This would also be a great opportunity to add a assertion, that the cmrf results match the pre-calculated reference values.
This way, if epg ever breaks, for example, this example would fail and we would notice.