add citation tag

README.md

@@ -37,6 +37,14 @@ The **test** folder contains the test files corresponding to the contributed cod
 
 The **utils** folder contains various helpful tools.
 
+## Citing
+If this repository was useful, please cite our [upcoming MRM paper](https://eur04.safelinks.protection.outlook.com/?url=https%3A%2F%2Furldefense.com%2Fv3%2F__https%3A%2Fdrive.google.com%2Fdrive%2Fu%2F0%2Ffolders%2F1Yh7cHuBIDQ0fmlWum9banDFgCc6uf3XH__%3B!!K-Hz7m0Vt54!gNxtgp_trP5qT7uJBD7zz5-Wwi7y83_56-qBO2I2Zd9ATILK5_BWzAKmxSah9-y8N8Wsgy67pn1LMeCd9xfaQGM%24&data=05%7C02%7Co.j.gurney-champion%40amsterdamumc.nl%7Cceeee39962fc4098e61708de2e90699a%7C68dfab1a11bb4cc6beb528d756984fb6%7C0%7C0%7C638999394967163935%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=mu6wjTAM1DlCgZjfM3mkKZEuSm1vNiteCxdh5kV29WY%3D&reserved=0). The repository contains a lot of code from other sites. Please cite the appropriate papers depending on the code you used. The citations can be found with the "cite" function from OsipiBase:
+```ruby
+fit=osipibase(algorithm="IAR_LU_biexp")
+fit.cite()
+```
+DOIs can also be found in [code_contributions_record](doc/code_contributions_record.csv).
+
 ## View Testing Reports
 [![Unit tests](https://github.com/OSIPI/TF2.4_IVIM-MRI_CodeCollection/actions/workflows/unit_test.yml/badge.svg?branch=main)](https://github.com/OSIPI/TF2.4_IVIM-MRI_CodeCollection/actions/workflows/unit_test.yml)
 [![Algorithm Analysis](https://github.com/OSIPI/TF2.4_IVIM-MRI_CodeCollection/actions/workflows/analysis.yml/badge.svg?branch=main)](https://github.com/OSIPI/TF2.4_IVIM-MRI_CodeCollection/actions/workflows/analysis.yml)

doc/code_contributions_record.csv

@@ -17,9 +17,9 @@ IVIM,Fitting,Variable projection,See referenced article. Supports units in mm2/s
 IVIM,Fitting,Variable projection,See referenced article. Supports units in mm2/s and µm2/ms,IAR_LundUniversity,TF2.4_IVIM-MRI_CodeCollection/src/original/IAR_LundUniversity/ivim_fit_method_modified_topopro.py,Fadnavis et al. Modified by Ivan A. Rashid,Lund University,IvimModelTopoPro,https://doi.org/10.3389/fnins.2021.779025,IAR_LU_modified_topopro
 IVIM,Fitting,Linear fit,Linear fit for D with extrapolation for f. Supports units in mm2/s and µm2/ms,IAR_LundUniversity,TF2.4_IVIM-MRI_CodeCollection/src/original/IAR_LundUniversity/ivim_fit_method_modified_linear.py,Modified by Ivan A. Rashid,Lund University,IvimModelLinear,tba,
 IVIM,Fitting,sIVIM fit,NLLS of the simplified IVIM model (sIVIM). Supports units in mm2/s and µm2/ms,IAR_LundUniversity,TF2.4_IVIM-MRI_CodeCollection/src/original/IAR_LundUniversity/ivim_fit_method_modified_sivim.py,Modified by Ivan A. Rashid,Lund University,IvimModelsIVIM,tba,
-IVIM,Fitting,Segmented NLLS fitting,MATLAB code,OJ_GU,TF2.4_IVIM-MRI_CodeCollection/src/original/OJ_GU/,Oscar Jalnefjord,University of Gothenburg,IVIM_seg,https://doi.org/10.1007/s10334-018-0697-5,OJ_GU_seg
-IVIM,Fitting,Bayesian,MATLAB code,OJ_GU,TF2.4_IVIM-MRI_CodeCollection/src/original/OJ_GU/,Oscar Jalnefjord,University of Gothenburg,IVIM_bayes,https://doi.org/10.1007/s10334-018-0697-5,OJ_GU_segMATLAB
-IVIM,Fitting,Segmented NLLS fitting,Specifically tailored algorithm for NLLS segmented fitting,OJ_GU,TF2.4_IVIM-MRI_CodeCollection/src/original/OJ_GU/,Oscar Jalnefjord,University of Gothenburg,seg,https://doi.org/10.1002/mrm.26783,OJ_GU_bayesMATLAB
+IVIM,Fitting,Segmented NLLS fitting,MATLAB code,OJ_GU,TF2.4_IVIM-MRI_CodeCollection/src/original/OJ_GU/,Oscar Jalnefjord,University of Gothenburg,IVIM_seg,https://doi.org/10.1007/s10334-018-0697-5,OJ_GU_segMATLAB
+IVIM,Fitting,Bayesian,MATLAB code,OJ_GU,TF2.4_IVIM-MRI_CodeCollection/src/original/OJ_GU/,Oscar Jalnefjord,University of Gothenburg,IVIM_bayes,https://doi.org/10.1002/mrm.26783,OJ_GU_bayesMATLAB
+IVIM,Fitting,Segmented NLLS fitting,Specifically tailored algorithm for NLLS segmented fitting,OJ_GU,TF2.4_IVIM-MRI_CodeCollection/src/original/OJ_GU/,Oscar Jalnefjord,University of Gothenburg,seg,https://doi.org/10.1007/s10334-018-0697-5,OJ_GU_seg
 IVIM,Fitting,Linear fit,Linear fit for D and D* and f. Intended to be extremely fast but not always accurate,ETP_SRI,TF2.4_IVIM-MRI_CodeCollection/src/original/ETP_SRI/LinearFitting.py,Eric Peterson,SRI International,LinearFit,tba,ETP_SRI_LinearFitting
 IVIM,Fitting,LSQ fitting,MATLAB code,ASD_MemorialSloanKettering,TF2.4_IVIM-MRI_CodeCollection/src/original/ASD_MemorialSloanKettering/MRI-QAMPER_IVIM,Eve LoCastro/Ramesh Paudyal/Amita Shukla-Dave,Memorial Sloan Kettering,IVIM_standard_bcin,https://doi.org/10.3390/tomography9060161,ASD_MemorialSloanKettering_QAMPER_IVIM
 IVIM,Fitting,Neural network,Part of the SUPER-IVIM-DC python package,TCML_TechnionIIT,TF2.4_IVIM-MRI_CodeCollection/src/original/TCML_TechnionIIT/SUPER-IVIM-DC,Noam Korngut/Elad Rotman/Onur Afacan Sila Kurugol/Yael Zaffrani-Reznikov/Shira Nemirovsky-Rotman/Simon Warfield/Moti Freiman,TCML Technion IIT,super_ivim_dc.infer.infer_from_signal,https://doi.org/10.1007/978-3-031-16434-7_71,Super_IVIM_DC

src/standardized/ASD_MemorialSloanKettering_QAMPER_IVIM.py

@@ -17,7 +17,7 @@ class ASD_MemorialSloanKettering_QAMPER_IVIM(OsipiBase):
     id_algorithm_type = "Bi-exponential fit"
     id_return_parameters = "f, D*, D, S0"
     id_units = "seconds per milli metre squared or milliseconds per micro metre squared"
-
+    id_ref = "https://doi.org/10.3390/tomography9060161"
     # Algorithm requirements
     required_bvalues = 4
     required_thresholds = [0,

src/standardized/IAR_LU_modified_mix.py

@@ -18,7 +18,8 @@ class IAR_LU_modified_mix(OsipiBase):
     id_algorithm_type = "Bi-exponential fit"
     id_return_parameters = "f, D*, D"
     id_units = "seconds per milli metre squared or milliseconds per micro metre squared"
-
+    id_ref = "https://doi.org/10.1038/srep38927"
+
     # Algorithm requirements
     required_bvalues = 4
     required_thresholds = [0,0] # Interval from "at least" to "at most", in case submissions allow a custom number of thresholds

src/standardized/IAR_LU_modified_topopro.py

@@ -18,7 +18,8 @@ class IAR_LU_modified_topopro(OsipiBase):
     id_algorithm_type = "Bi-exponential fit"
     id_return_parameters = "f, D*, D"
     id_units = "seconds per milli metre squared or milliseconds per micro metre squared"
-
+    id_ref = "https://doi.org/10.3389/fnins.2021.779025"
+
     # Algorithm requirements
     required_bvalues = 4
     required_thresholds = [0,0] # Interval from "at least" to "at most", in case submissions allow a custom number of thresholds

src/standardized/IVIM_NEToptim.py

@@ -19,7 +19,7 @@ class IVIM_NEToptim(OsipiBase):
     id_algorithm_type = "Deep learnt bi-exponential fit"
     id_return_parameters = "f, D*, D, S0"
     id_units = "seconds per milli metre squared or milliseconds per micro metre squared"
-
+    id_ref = "https://doi.org/10.1002/mrm.28852"
     # Algorithm requirements
     required_bvalues = 4
     required_thresholds = [0,

src/standardized/OGC_AmsterdamUMC_Bayesian_biexp.py

@@ -16,7 +16,7 @@ class OGC_AmsterdamUMC_Bayesian_biexp(OsipiBase):
     id_algorithm_type = "Bi-exponential fit"
     id_return_parameters = "f, D*, D, S0"
     id_units = "seconds per milli metre squared or milliseconds per micro metre squared"
-
+    id_ref = "reference method in https://doi.org/10.1002/mrm.28852"
     # Algorithm requirements
     required_bvalues = 4
     required_thresholds = [0,

src/standardized/OGC_AmsterdamUMC_biexp.py

@@ -16,6 +16,7 @@ class OGC_AmsterdamUMC_biexp(OsipiBase):
     id_algorithm_type = "Bi-exponential fit"
     id_return_parameters = "f, D*, D, S0"
     id_units = "seconds per milli metre squared or milliseconds per micro metre squared"
+    id_ref = "reference method in https://doi.org/10.1002/mrm.28852"
 
     # Algorithm requirements
     required_bvalues = 4

src/standardized/OJ_GU_bayesMATLAB.py

@@ -17,6 +17,7 @@ class OJ_GU_bayesMATLAB(OsipiBase):
     id_algorithm_type = "Bi-exponential fit"
     id_return_parameters = "f, D*, D, S0"
     id_units = "seconds per milli metre squared or milliseconds per micro metre squared"
+    id_ref = "https://doi.org/10.1002/mrm.26783"
 
     # Algorithm requirements
     required_bvalues = 4

src/standardized/OJ_GU_seg.py

@@ -16,7 +16,8 @@ class OJ_GU_seg(OsipiBase):
     id_algorithm_type = "Segmented bi-exponential fit"
     id_return_parameters = "f, D*, D"
     id_units = "mm2/s"
-
+    id_ref = "https://doi.org/10.1007/s10334-018-0697-5"
+
     # Algorithm requirements
     required_bvalues = 4
     required_thresholds = [0,0] # Interval from "at least" to "at most", in case submissions allow a custom number of thresholds

src/standardized/OJ_GU_segMATLAB.py

@@ -17,6 +17,7 @@ class OJ_GU_segMATLAB(OsipiBase):
     id_algorithm_type = "Bi-exponential fit"
     id_return_parameters = "f, D*, D, S0"
     id_units = "seconds per milli metre squared or milliseconds per micro metre squared"
+    id_ref = "https://doi.org/10.1007/s10334-018-0697-5"
 
     # Algorithm requirements
     required_bvalues = 4

src/standardized/PvH_KB_NKI_IVIMfit.py

@@ -18,6 +18,7 @@ class PvH_KB_NKI_IVIMfit(OsipiBase):
     id_algorithm_type = "Bi-exponential fit"
     id_return_parameters = "f, D*, D"
     id_units = "seconds per milli metre squared or milliseconds per micro metre squared"
+    id_ref = "https://doi.org/10.3389/fonc.2021.705964"
 
     # Algorithm requirements
     required_bvalues = 4

src/standardized/Super_IVIM_DC.py

@@ -21,6 +21,7 @@ class Super_IVIM_DC(OsipiBase):
     id_algorithm_type = "Supervised Deep learnt bi-exponential fit with data consistency"
     id_return_parameters = "f, D*, D, S0"
     id_units = "seconds per milli metre squared or milliseconds per micro metre squared"
+    id_ref = "https://doi.org/10.1007/978-3-031-16434-7_71"
 
     # Algorithm requirements
     required_bvalues = 4

src/standardized/TCML_TechnionIIT_SLS.py

@@ -16,6 +16,7 @@ class TCML_TechnionIIT_SLS(OsipiBase):
     id_algorithm_type = "Bi-exponential fit, segmented fitting"
     id_return_parameters = "f, D*, D, S0"
     id_units = "seconds per milli metre squared or milliseconds per micro metre squared"
+    id_ref = "same github as https://doi.org/10.1007/978-3-031-16434-7_71, but not the main code from the paper"
 
     # Algorithm requirements
     required_bvalues = 4

src/standardized/TCML_TechnionIIT_lsqBOBYQA.py

@@ -16,6 +16,7 @@ class TCML_TechnionIIT_lsqBOBYQA(OsipiBase):
     id_algorithm_type = "Bi-exponential fit, BOBYQO"
     id_return_parameters = "f, D*, D, S0"
     id_units = "seconds per milli metre squared or milliseconds per micro metre squared"
+    id_ref = "same github as https://doi.org/10.1007/978-3-031-16434-7_71, but not the main code from the paper"
 
     # Algorithm requirements
     required_bvalues = 4

src/standardized/TCML_TechnionIIT_lsq_sls_BOBYQA.py

@@ -16,6 +16,7 @@ class TCML_TechnionIIT_lsq_sls_BOBYQA(OsipiBase):
     id_algorithm_type = "Bi-exponential fit, SLS fit followed by Trust Region Reflective algorithm"
     id_return_parameters = "f, D*, D, S0"
     id_units = "seconds per milli metre squared or milliseconds per micro metre squared"
+    id_ref = "same github as https://doi.org/10.1007/978-3-031-16434-7_71, but not the main code from the paper"
 
     # Algorithm requirements
     required_bvalues = 4

src/standardized/TCML_TechnionIIT_lsq_sls_lm.py

@@ -16,6 +16,7 @@ class TCML_TechnionIIT_lsq_sls_lm(OsipiBase):
     id_algorithm_type = "Bi-exponential, segmented as initaition, followed by Levenberg-Marquardt algorithm"
     id_return_parameters = "f, D*, D, S0"
     id_units = "seconds per milli metre squared or milliseconds per micro metre squared"
+    id_ref = "same github as https://doi.org/10.1007/978-3-031-16434-7_71, but not the main code from the paper"
 
     # Algorithm requirements
     required_bvalues = 4

src/standardized/TCML_TechnionIIT_lsq_sls_trf.py

@@ -16,6 +16,7 @@ class TCML_TechnionIIT_lsq_sls_trf(OsipiBase):
     id_algorithm_type = "Bi-exponential fit, SLS fit followed by Trust Region Reflective algorithm"
     id_return_parameters = "f, D*, D, S0"
     id_units = "seconds per milli metre squared or milliseconds per micro metre squared"
+    id_ref = "same github as https://doi.org/10.1007/978-3-031-16434-7_71, but not the main code from the paper"
 
     # Algorithm requirements
     required_bvalues = 4

src/standardized/TCML_TechnionIIT_lsqlm.py

@@ -16,6 +16,7 @@ class TCML_TechnionIIT_lsqlm(OsipiBase):
     id_algorithm_type = "Bi-exponential fit with Levenberg-Marquardt algorithm"
     id_return_parameters = "f, D*, D, S0"
     id_units = "seconds per milli metre squared or milliseconds per micro metre squared"
+    id_ref = "same github as https://doi.org/10.1007/978-3-031-16434-7_71, but not the main code from the paper"
 
     # Algorithm requirements
     required_bvalues = 4

src/standardized/TCML_TechnionIIT_lsqtrf.py

@@ -16,6 +16,7 @@ class TCML_TechnionIIT_lsqtrf(OsipiBase):
     id_algorithm_type = "Bi-exponential fit, Trust Region Reflective algorithm"
     id_return_parameters = "f, D*, D, S0"
     id_units = "seconds per milli metre squared or milliseconds per micro metre squared"
+    id_ref = "same github as https://doi.org/10.1007/978-3-031-16434-7_71, but not the main code from the paper"
 
     # Algorithm requirements
     required_bvalues = 4

src/standardized/TF_reference_IVIMfit.py

@@ -16,6 +16,7 @@ class TF_reference_IVIMfit(OsipiBase):
     id_algorithm_type = "Bi-exponential fit"
     id_return_parameters = "f, D*, D"
     id_units = "seconds per milli metre squared or milliseconds per micro metre squared"
+    id_ref = "code specially written for this repository, based on the IVIM consensus paper (Eric Sigmund, Susi Rauh, et al. 2026) which will appear in JMRI"
 
     # Algorithm requirements
     required_bvalues = 4

src/wrappers/OsipiBase.py

@@ -103,6 +103,7 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
         self.deep_learning = False
         self.supervised = False
         self.stochastic = False
+        self.id_ref = None
         # If the user inputs an algorithm to OsipiBase, it is intereprete as initiating
         # an algorithm object with that name.
         if algorithm:
@@ -505,4 +506,11 @@ def D_and_Ds_swap(self,results):
             results['Dp']=results['D']
             results['D']=D
             results['f']=1-results['f']
-        return results
+        return results
+
+    def cite(self):
+        print("thank you for using our repository. If it was useful for your paper, please cite our upcoming paper in MRM.")
+        if self.id_ref is not None:
+            print("The current fit method you have loaded should be cited with: " + self.id_ref)
+        else:
+            print("The used fit code has not yet provided a code-speicific reference.")