Example for Basics and Cartesian reconstruction

[ckolbPTB]

This example shows some basics features of MRpro and then a step by step guide to reconstruct fully sampled Cartesian scan, Cartesian scan with partial echo and partial Fourier and Cartesian scan with regular undersampling and self-calibrated csm calculation.

probably supersedes #233
closes #518

[github-actions]

Coverage Report

File	Stmts	Miss	Cover	Missing
src/mrpro/algorithms/csm
inati.py	24	1	96%	44
walsh.py	16	1	94%	34
src/mrpro/algorithms/dcf
dcf_voronoi.py	53	4	92%	15, 48–49, 76
src/mrpro/algorithms/optimizers
adam.py	20	1	95%	69
src/mrpro/algorithms/reconstruction
DirectReconstruction.py	28	16	43%	51–71, 85
IterativeSENSEReconstruction.py	13	1	92%	76
Reconstruction.py	50	22	56%	42, 54–56, 80–87, 104–113
RegularizedIterativeSENSEReconstruction.py	41	17	59%	96–100, 114–139
src/mrpro/data
AcqInfo.py	128	3	98%	26, 169, 207
CsmData.py	29	3	90%	15, 82–84
DcfData.py	45	8	82%	18, 66, 78–83
IData.py	67	9	87%	119, 125, 129, 159–167
IHeader.py	75	7	91%	75, 109, 127–131
KHeader.py	153	17	89%	25, 119–123, 150, 199, 210, 217–218, 221, 228, 260–271
KNoise.py	31	15	52%	39–52, 56–61
KTrajectory.py	81	12	85%	108–113, 116–118, 203–207
MoveDataMixin.py	140	18	87%	15, 113, 129, 143–145, 207, 323–325, 338, 417, 437–438, 440, 455–456, 458
QData.py	39	7	82%	42, 65–73
Rotation.py	674	35	95%	100, 198, 335, 433, 477, 495, 581, 583, 592, 626, 628, 691, 768, 773, 776, 791, 808, 813, 889, 1077, 1082, 1085, 1109, 1113, 1240, 1242, 1250–1251, 1315, 1397, 1690, 1846, 1881, 1885, 1996
SpatialDimension.py	232	21	91%	34, 104, 141, 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/_kdata
KData.py	134	18	87%	109–110, 125, 132, 142, 150, 204–205, 243, 248–249, 268–279
KDataRemoveOsMixin.py	29	2	93%	44, 46
KDataSelectMixin.py	19	2	89%	48, 63
KDataSplitMixin.py	48	3	94%	53, 84, 93
src/mrpro/data/traj_calculators
KTrajectoryCalculator.py	25	2	92%	23, 45
KTrajectoryIsmrmrd.py	13	2	85%	41, 50
KTrajectoryPulseq.py	23	1	96%	55
src/mrpro/operators
CartesianSamplingOp.py	89	3	97%	118, 157, 280
ConstraintsOp.py	60	2	97%	46, 48
EndomorphOperator.py	65	2	97%	228, 234
FiniteDifferenceOp.py	27	2	93%	40, 105
FourierOp.py	158	3	98%	263, 381, 386
Functional.py	71	5	93%	20–22, 117, 119
GridSamplingOp.py	136	9	93%	72–73, 82–83, 90–91, 94, 96, 98
LinearOperator.py	168	10	94%	55, 91, 190, 220, 261, 270, 278, 287, 295, 320
LinearOperatorMatrix.py	158	16	90%	82, 119, 152, 161, 166, 175–178, 191–194, 203, 215, 304, 331, 359
MultiIdentityOp.py	13	2	85%	43, 48
Operator.py	78	2	97%	25, 74
ProximableFunctionalSeparableSum.py	39	3	92%	50, 103, 110
SliceProjectionOp.py	173	8	95%	44, 61, 63, 69, 206, 227, 260, 300
WaveletOp.py	120	5	96%	152, 170, 205, 210, 233
ZeroPadOp.py	16	1	94%	30
src/mrpro/utils
filters.py	62	2	97%	44, 49
reshape.py	60	1	98%	191
slice_profiles.py	46	6	87%	20, 36, 113–116, 149
sliding_window.py	34	1	97%	34
split_idx.py	10	2	80%	43, 47
summarize_tensorvalues.py	11	9	18%	20–29
typing.py	18	11	39%	8–23
zero_pad_or_crop.py	31	6	81%	26, 30, 54, 57, 60, 63
TOTAL	4933	360	93%

Tests	Skipped	Failures	Errors	Time
2262	0 💤	0 ❌	0 🔥	1m 9s ⏱️

[github-actions]

📚 Documentation

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[schuenke]

You might wanna have a look at it again @ckolbPTB , added quite a few changes.

[fzimmermann89]

Why does this use the fftop?

Shouldn't we push for using the FourierOp?
Fftop is an implementation detail.

Also, we should not demonstrate trajectory.as_tensor to get Kx.
Instead use trajectory.kx directly

[ckolbPTB]

This notebook is meant to provide some background information on what is happening in MRpro. The conclusion is that FourierOp or even better one of the algorithms should be used.

[fzimmermann89]

I would just not mention FastFourierOp and only use FourierOp. CartesianSampling Op and FastFourierOp are implementation details.

This notebook is now the "gold standard" recipe for manual reconstruction of cartesian data
And it only shows FastFourierOp (and the all-in-one reconstruction.)

I would never use FastFourierOp for any reconstruction.
I am willing to bet that their will be ppl using FastFourierOp in their code and not even knowing about FourierOp.
This was already an issue in the past -- fft is to "obvious" to not use it. And having this as the cartesian reco example is IMHO a bad idea.

I would have removed all the cartesian sampling op stuff, and directly used the fourierop...