Allow nufft for traj where nufft/fft splitting fails

[ckolbPTB]

For some trajectory (e.g. EPI) the FourierOp cannot be created as a combination of FFTOp and NUFFTOp. So far this led to an error and users had to create their own FourierOp. This PR defaults the FourierOp to NufftOp for all dimensions. Not optimal but at least we get an image.

[github-actions]

Coverage Report

File	Stmts	Miss	Cover	Missing
src/mrpro
_version.py	6	2	67%	7–8
src/mrpro/algorithms/csm
inati.py	25	1	96%	43
src/mrpro/algorithms/dcf
dcf_voronoi.py	55	4	93%	15, 55–56, 89
src/mrpro/algorithms/optimizers
adam.py	30	6	80%	108, 125–129
cg.py	52	1	98%	139
pdhg.py	81	3	96%	180–181, 187
pgd.py	53	4	92%	108, 153–156
src/mrpro/algorithms/reconstruction
DirectReconstruction.py	28	2	93%	62, 78
Reconstruction.py	47	4	91%	54–56, 122
RegularizedIterativeSENSEReconstruction.py	51	2	96%	123, 152
TotalVariationRegularizedReconstruction.py	52	4	92%	104, 110, 127, 130
src/mrpro/data
AcqInfo.py	165	7	96%	49, 56, 134–135, 137, 243, 367
Dataclass.py	310	26	92%	59, 320, 336, 402, 460–462, 475, 570, 590–591, 593, 608–609, 611, 658–659, 664–665, 850–851, 876, 883, 888–889, 891
DcfData.py	33	1	97%	62
EncodingLimits.py	97	3	97%	37, 127, 130
IData.py	178	12	93%	138, 155–156, 217–225, 258, 304, 322
IHeader.py	174	8	95%	49, 98–101, 284, 288, 292, 296
KData.py	230	24	90%	122–123, 138, 145, 156–167, 178, 186, 197, 236, 258–260, 307–308, 380, 546, 548, 620
KHeader.py	176	13	93%	115–121, 148, 196, 203–204, 231–238
KNoise.py	22	1	95%	44
KTrajectory.py	95	3	97%	163, 165, 185
QData.py	32	1	97%	43
Rotation.py	737	42	94%	104, 202, 339, 437, 481, 499, 586, 588, 597, 634, 636, 699, 776, 781, 784, 799, 816, 821, 897, 1085, 1090, 1093, 1117, 1121, 1145, 1265, 1267, 1275–1276, 1340, 1422, 1626, 1633–1635, 1694, 1790, 1942, 1977, 1981, 2156, 2177
SpatialDimension.py	150	19	87%	34, 103, 146, 158, 278–280, 293–295, 329, 347, 360, 373, 386, 399, 408–409, 437
src/mrpro/data/traj_calculators
KTrajectoryCalculator.py	26	2	92%	84, 95
KTrajectoryCartesian.py	31	4	87%	129–132, 136
KTrajectoryIsmrmrd.py	19	1	95%	57
KTrajectorySpiral2D.py	57	13	77%	63–66, 69, 71, 73, 75, 77, 105, 107, 134–136
src/mrpro/operators
AveragingOp.py	38	2	95%	73, 115
CartesianSamplingOp.py	112	4	96%	152, 191, 266, 387
ConjugateGradientOp.py	89	7	92%	62, 64, 100, 106, 228, 230, 233
ConstraintsOp.py	85	4	95%	78, 80, 250, 255
EndomorphOperator.py	28	2	93%	71, 77
FiniteDifferenceOp.py	29	2	93%	40, 126
FourierOp.py	105	9	91%	85–87, 192–193, 212, 257, 321, 326
Functional.py	70	2	97%	116, 118
GridSamplingOp.py	165	15	91%	72–73, 82–83, 90–91, 94, 96, 98, 282, 290–291, 306, 312–313
LinearOperator.py	202	6	97%	217, 255, 296, 305, 313, 330
LinearOperatorMatrix.py	174	19	89%	99, 136, 169, 178, 183, 192–195, 208–211, 219–220, 225–226, 238, 347, 377, 404
MultiIdentityOp.py	16	2	88%	58, 63
NonUniformFastFourierOp.py	198	10	95%	72, 99, 221, 223, 261, 263, 340, 397, 471, 476
Operator.py	88	3	97%	82, 115, 125
PatchOp.py	49	3	94%	93, 129, 144
ProximableFunctionalSeparableSum.py	44	3	93%	118, 213, 224
SliceProjectionOp.py	178	10	94%	45, 62, 64, 70, 154, 180, 216, 253, 290, 330
WaveletOp.py	119	3	97%	184, 228, 254
ZeroPadOp.py	18	1	94%	30
src/mrpro/operators/functionals
SSIM.py	73	7	90%	60–80, 82, 86, 114, 147
src/mrpro/operators/models
EPG.py	255	19	93%	31–32, 283, 288, 304–306, 326–327, 332, 356, 361, 386, 391, 546, 600, 739, 756, 783
src/mrpro/phantoms
EllipsePhantom.py	43	2	95%	66, 131
brainweb.py	297	40	87%	276, 290–294, 349–359, 398, 454–457, 479–480, 485–486, 488–489, 493, 501, 508–509, 550, 621, 624–625, 644, 653–656, 667, 669, 700–701, 715, 723
fastmri.py	106	10	91%	50–51, 59, 65, 162, 169–171, 174–175, 189
m4raw.py	74	4	95%	58–59, 74, 76
mdcnn.py	71	7	90%	58, 62–63, 70, 82, 88, 135
src/mrpro/utils
RandomGenerator.py	156	15	90%	23–24, 36, 38, 188, 212, 428, 446, 528, 799, 829–832, 895, 898
ema.py	42	4	90%	49, 74, 78, 82
filters.py	61	1	98%	46
indexing.py	177	1	99%	321
pad_or_crop.py	39	6	85%	40, 43, 46, 49, 66, 73
reshape.py	153	10	93%	176, 370, 482–484, 505, 508–509, 515, 530
slice_profiles.py	49	6	88%	21, 37, 119–122, 155
split_idx.py	10	2	80%	43, 47
summarize.py	57	6	89%	40–41, 70–73, 81
unit_conversion.py	72	15	79%	34, 44, 51, 53, 62, 69, 71, 78, 80, 89, 100, 121, 123, 144, 146
TOTAL	7910	475	94%

Tests	Skipped	Failures	Errors	Time
3050	0 💤	0 ❌	0 🔥	2m 16s ⏱️

[github-actions]

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[fzimmermann89]

I am a bit confused why epi does not work:
Is the full shot in k0 like a spiral or is each line+blip in k0?If the latter, can you cut by indexing the blip and get an FFT?

Or how does an epi trajectory look like? @schuenke

[schuenke]

For our implementation, each line is a separate acquisition and we either use a symmetric trajectory (every 2nd line is acquired in reverse order) or a flyback trajectory (each line is acquired from -k to +k like a typical cartesian acquisition. However, we often use ramp sampling, so that only the center points are on a cartesian grid and the first and last few samples are closer together (adc dwell is constant, but due to non-constant gradients we have samller steps in kx direction).

Note: Currently, we also use the pp.calculate_kspace() function to calculate the "nominell" trajectory we store in the mrd header file. It's been a while, but as far as I remember the calculated trajectories for the symmetric case where not perfectly matching and the default repeat detection did not recognize all kx points as identical for the different ky positions.

This is a typical trajectory for an 8x8 symmetric EPI with ramp sampling and readout oversampling:

In this case, the trajectories in non-reverse and reverse direction match quite well.

However, if we use the same bandwidth and nx=ny=64, they get shifted as we can see when we zoom in:

The reason is that the adc start time has to be aligned to the RF (!) raster time and not the ADC raster time and rf_raster_time >> adc_raster time:

[fzimmermann89]

I am confused why this does not work with the current code (might be #842 related?)

Do you have an mrd file for debugging?