Faster DCF based on pipe

[fzimmermann89]

Not quite pipe/jackson, but would work with any nufft implementation (finuft does not have separate spread kernel)

for a quick draft, I just added it to the direct recon example.

@schuenke, @JoHa0811, @ckolbPTB
do you have any spiral data you could easily test this on and post a result?

closes #82

[github-actions]

Coverage Report

File	Stmts	Miss	Cover	Missing
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	20	1	95%	101
pdhg.py	79	3	96%	177–178, 184
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	128	3	98%	26, 169, 207
CsmData.py	29	3	90%	15, 84–86
DcfData.py	45	8	82%	18, 66, 78–83
IData.py	67	9	87%	120, 126, 130, 160–168
IHeader.py	75	7	91%	75, 109, 127–131
KData.py	214	25	88%	111–112, 127, 134, 144, 152, 206–207, 245, 250–251, 270–281, 440, 442, 507, 522, 559, 590, 599
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%	112–117, 120–122, 207–211
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
Rotation.py	682	35	95%	100, 198, 335, 433, 477, 495, 582, 584, 593, 627, 629, 692, 769, 774, 777, 792, 809, 814, 890, 1078, 1083, 1086, 1110, 1114, 1242, 1244, 1252–1253, 1317, 1399, 1702, 1854, 1889, 1893, 2004
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/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	73	2	97%	241, 246
Functional.py	77	9	88%	20–22, 117, 119, 226–228, 242
GridSamplingOp.py	136	9	93%	72–73, 82–83, 90–91, 94, 96, 98
LinearOperator.py	200	10	95%	48, 107, 217, 244, 251, 292, 301, 309, 326, 361
LinearOperatorMatrix.py	162	19	88%	82, 119, 152, 161, 166, 175–178, 191–194, 202–203, 208–209, 221, 310, 337, 365
MultiIdentityOp.py	13	2	85%	43, 48
NonUniformFastFourierOp.py	171	4	98%	98, 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/utils
filters.py	62	2	97%	44, 49
reshape.py	65	1	98%	191
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	11	9	18%	20–29
typing.py	21	11	48%	8–23
zero_pad_or_crop.py	31	6	81%	26, 30, 55, 58, 61, 64
TOTAL	5126	373	93%

Tests	Skipped	Failures	Errors	Time
2399	0 💤	0 ❌	0 🔥	1m 20s ⏱️

[github-actions]

📚 Documentation

📁 Download as zip
🔍 View online

[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	20	1	95%	101
pdhg.py	79	3	96%	177–178, 184
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	128	3	98%	26, 169, 207
CsmData.py	29	3	90%	15, 84–86
DcfData.py	45	8	82%	18, 66, 78–83
IData.py	60	8	87%	129, 142, 171–179
IHeader.py	96	11	89%	103, 106, 116, 119, 122, 125, 162–166
KData.py	214	25	88%	111–112, 127, 134, 144, 152, 206–207, 245, 250–251, 270–281, 440, 442, 507, 522, 559, 590, 599
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%	112–117, 120–122, 207–211
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
Rotation.py	682	35	95%	100, 198, 335, 433, 477, 495, 582, 584, 593, 627, 629, 692, 769, 774, 777, 792, 809, 814, 890, 1078, 1083, 1086, 1110, 1114, 1242, 1244, 1252–1253, 1317, 1399, 1702, 1854, 1889, 1893, 2004
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/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	32	2	94%	52, 58
FiniteDifferenceOp.py	27	2	93%	40, 105
FourierOp.py	73	2	97%	234, 239
Functional.py	77	9	88%	20–22, 117, 119, 226–228, 242
GridSamplingOp.py	136	9	93%	72–73, 82–83, 90–91, 94, 96, 98
LinearOperator.py	200	10	95%	48, 107, 217, 244, 251, 292, 301, 309, 326, 361
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	177	10	94%	69, 96, 206, 208, 241, 243, 302, 356, 406, 411
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/utils
filters.py	62	2	97%	44, 49
reshape.py	75	1	99%	239
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	11	9	18%	20–29
typing.py	58	45	22%	9–217
zero_pad_or_crop.py	31	6	81%	26, 30, 55, 58, 61, 64
TOTAL	5168	418	92%

Tests	Skipped	Failures	Errors	Time
2413	0 💤	0 ❌	0 🔥	1m 11s ⏱️

[Stef-Martin]

For my data, it looks like this - way way faster but looks worse

[fzimmermann89]

This is with single iteration?
Does increasing the iterations help?

[Stef-Martin]

whoops sry didn't even see that default iter of the estimate was set to 0 - just ran the script real quick.
So the one shown above was Jackson, so 0 iter. With pipe it is much better with 1 iteration:

By increasing the iterations, it gets worse, this is with 3 iter:

[fzimmermann89]

Mhm. Maybe there is something wrong with the iterations then.

The one iteration case looks good though

[fzimmermann89]

Let's have a look tomorrow.
Maybe compare the dcfs calculated by 1 and 3 iterations -- are there some points that get zero/really high?
just a

plt.matshow(dcf_operator2.matrix[0,0],vmax=2) # change vmax
plt.colorbar()

might help to identify issues.

Can you share an example file?

[fzimmermann89]

... and let's check if the images are good enough to create CSMs from them.

[Stef-Martin]

Update: without .abs() in return weight.abs().squeeze(-4) it works