fix: add missing D_and_Ds_swap() to 13 algorithms for consistent parameter labelling

[Devguru-codes]

Add self.D_and_Ds_swap(results) call before return in ivim_fit() for:

• IAR_LU_segmented_3step, IAR_LU_subtracted
• PV_MUMC_biexp
• OGC_AmsterdamUMC_biexp, OGC_AmsterdamUMC_biexp_segmented, OGC_AmsterdamUMC_Bayesian_biexp
• OJ_GU_seg, PvH_KB_NKI_IVIMfit, TF_reference_IVIMfit
• TCML_TechnionIIT_SLS, TCML_TechnionIIT_lsq_sls_lm/trf/BOBYQA

This corrects mislabelled D and D* when the optimizer swaps them. Previously only 9/22 algorithms had this correction.

No regressions: 1127 passed, 167 skipped, 22 xfailed, 6 xpassed.

Describe the changes you have made in this PR

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Link this PR to an issue [optional]

Fixes #146

Checklist

• Self-review of changed code
• Added automated tests where applicable
• Update Docs & Guides

[Devguru-codes]

This PR adds the missing D_and_Ds_swap() call to 13 standardized algorithm implementations that were missing it. This method corrects mislabelled D and D* (Dp) parameters when the optimizer assigns them to the wrong parameter.

Previously, only 9 out of 22 Python algorithms applied this correction, leading to inconsistent parameter labelling.

Changes Made

Added results = self.D_and_Ds_swap(results) before return results in the ivim_fit() method of these 13 files:

#	File
1	IAR_LU_segmented_3step.py
2	IAR_LU_subtracted.py
3	PV_MUMC_biexp.py
4	OGC_AmsterdamUMC_biexp.py
5	OGC_AmsterdamUMC_biexp_segmented.py
6	OGC_AmsterdamUMC_Bayesian_biexp.py
7	OJ_GU_seg.py
8	PvH_KB_NKI_IVIMfit.py
9	TF_reference_IVIMfit.py
10	TCML_TechnionIIT_SLS.py
11	TCML_TechnionIIT_lsq_sls_lm.py
12	TCML_TechnionIIT_lsq_sls_trf.py
13	TCML_TechnionIIT_lsq_sls_BOBYQA.py

Each change is a single +1 line addition. No other code was modified.

Testing/Verification

• Ran the full test suite: 1127 passed, 167 skipped, 22 xfailed, 6 xpassed — identical to main baseline
• Zero regressions. The D_and_Ds_swap() is a no-op when parameters are already correctly labelled, so existing test results are unchanged.

[oliverchampion]

Some discussion and history here:
If I remember correctly, in the past, I implemented the swap in the wrapper to make sure it hit all algorithms. However, this gave some errors for edge-cases (i.e. when f=0 or f=1, we get a mono-exponential signal, for which either D or D* become meaningless. This gave some weird results (ie f=0, D fits normally, D* becomes very very small --> D and D* get swapped, messing up everything). Therefore, we chose to only implement this for some algorithms that benefited.

@IvanARashid do I remember this correctly?

[Devguru-codes]

I didn't know about this. I think you are right about the edge case when f ≈ 0 (mono-exponential, no perfusion), D fits normally but D* becomes meaninglessly small (e.g. D* → 0.001). This triggers the swap condition (D > Dp AND Dp < 0.05), which incorrectly swaps D and D* and flips f, messing up an otherwise valid fit. I have 2 options here (I think and correct me if I am wrong) -
First is revert this PR or I can add a guard the swap with an additional check to avoid the edge case like this -

if results['D'] > results['Dp'] and results['Dp'] < 0.05 and results['f'] > 0.01:
    # only swap when there's a meaningful perfusion fraction

I will need your confirmation here like what to do.