Add RegularizedIterativeSENSEReconstruction

[ckolbPTB]

closes #369 closes #321 closes #6

[github-actions[bot]]

Coverage Report

File	Stmts	Miss	Cover	Missing
src/mrpro/algorithms/csm
inati.py	24	1	96%	44
iterative_walsh.py	15	1	93%	37
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	23	54%	41, 53–55, 79–86, 103–113
RegularizedIterativeSENSEReconstruction.py	41	17	59%	93–97, 111–136
src/mrpro/data
AcqInfo.py	128	2	98%	174, 214
CsmData.py	28	3	89%	14, 84–86
DcfData.py	44	8	82%	17, 65, 77–82
IData.py	67	9	87%	119, 125, 129, 159–167
IHeader.py	75	7	91%	75, 109, 127–131
KHeader.py	164	17	90%	24, 126–130, 157, 207, 218, 225–226, 229, 236, 275–286
KNoise.py	31	15	52%	39–52, 56–61
KTrajectory.py	69	5	93%	178–182
MoveDataMixin.py	126	14	89%	14, 109, 125, 139–141, 202, 265, 279, 358, 378–379, 396–397
QData.py	39	7	82%	42, 65–73
SpatialDimension.py	46	2	96%	64, 103
TrajectoryDescription.py	14	1	93%	23
acq_filters.py	10	1	90%	47
src/mrpro/data/_kdata
KData.py	105	16	85%	107–108, 117, 125, 179–180, 215, 220–221, 240–251
KDataRemoveOsMixin.py	29	2	93%	43, 45
KDataSelectMixin.py	20	2	90%	46, 62
KDataSplitMixin.py	48	3	94%	49, 79, 88
src/mrpro/data/traj_calculators
KTrajectoryCalculator.py	25	2	92%	23, 45
KTrajectoryIsmrmrd.py	13	2	85%	41, 50
KTrajectoryPulseq.py	29	1	97%	54
src/mrpro/operators
CartesianSamplingOp.py	50	9	82%	49–50, 55–56, 61–62, 88, 91, 114
ConstraintsOp.py	60	2	97%	46, 48
EndomorphOperator.py	51	2	96%	209, 213
FiniteDifferenceOp.py	27	2	93%	48, 113
FourierOp.py	77	1	99%	131
GridSamplingOp.py	123	9	93%	60–61, 70–71, 78–79, 82, 84, 86
LinearOperator.py	80	3	96%	32, 131, 256
Operator.py	52	1	98%	21
SliceProjectionOp.py	166	8	95%	39, 46, 48, 54, 191, 212, 245, 285
WaveletOp.py	120	5	96%	152, 170, 205, 210, 233
ZeroPadOp.py	16	1	94%	30
src/mrpro/utils
Rotation.py	453	28	94%	58–66, 106, 283, 368, 370, 397, 452, 457, 460, 475, 492, 497, 640, 645, 648, 664, 668, 742, 744, 752–753, 993, 1075
filters.py	62	2	97%	44, 49
slice_profiles.py	45	6	87%	18, 34, 111–114, 147
sliding_window.py	34	1	97%	34
split_idx.py	10	2	80%	43, 47
summarize_tensorvalues.py	11	9	18%	20–29
zero_pad_or_crop.py	31	6	81%	26, 30, 54, 57, 60, 63
TOTAL	3628	280	92%

Tests	Skipped	Failures	Errors	Time
828	0 :zzz:	0 :x:	0 :fire:	1m 10s :stopwatch:

[fzimmermann89]

Allow for a linear operator in the regularization: ... + L*(Bx-x0)^2 ? With B defaulting to identity?

[ckolbPTB]

With B defaulting to identity?

Would we then still omit an implementation of the identity operator and realise it with setting B: LinearOperator | float = 1. and making use of the OperatorTensorSum?

[fzimmermann89]

B as float does not make sense IMHO.

I would make it LinearOperator | None with None being the identity (this also avoids creating an object as a default operator)

We can still have an explicit IdentityOperator, that can be used there (or used as a dummy linear operator)

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---

From: Christoph Kolbitsch @.> Sent: Thursday, September 5, 2024 9:25:23 AM To: PTB-MR/mrpro @.> Cc: Felix F Zimmermann @.>; Comment @.> Subject: Re: [PTB-MR/mrpro] Add RegularizedIterativeSENSEReconstruction (PR #388)

With B defaulting to identity?

Would we then still omit an implementation of the identity operator and realise it with setting B: LinearOperator | float = 1. and making use of the OperatorTensorSum?

— Reply to this email directly, view it on GitHubhttps://github.com/PTB-MR/mrpro/pull/388#issuecomment-2330798330, or unsubscribehttps://github.com/notifications/unsubscribe-auth/ABH3DV47JR4YBFURG2LKKP3ZVABOHAVCNFSM6AAAAABNU4MUICVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDGMZQG44TQMZTGA. You are receiving this because you commented.Message ID: @.***>

[fzimmermann89]

can we jusit add the regularization example to the existing iterative sense example? these two examples seem quite redundant.

[ckolbPTB]

can we jusit add the regularization example to the existing iterative sense example? these two examples seem quite redundant.

yes there is sume redundancy but from a teaching perspective I think it is easier to understand to build up the complexity of the reconstruction step by step.