diff --git a/requirements.txt b/requirements.txt
index 29742e3..cc94a5e 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -15,3 +15,4 @@ pandas
 sphinx
 sphinx_rtd_theme
 pytest-json-report
+ivimnet
\ No newline at end of file
diff --git a/src/original/OGC_AUMC_IVIMNET/Example_1_simple_map.py b/src/original/OGC_AUMC_IVIMNET/Example_1_simple_map.py
new file mode 100644
index 0000000..5f55818
--- /dev/null
+++ b/src/original/OGC_AUMC_IVIMNET/Example_1_simple_map.py
@@ -0,0 +1,63 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+September 2020 by Oliver Gurney-Champion & Misha Kaandorp
+oliver.gurney.champion@gmail.com / o.j.gurney-champion@amsterdamumc.nl
+https://www.github.com/ochampion
+
+Code is uploaded as part of our publication in MRM (Kaandorp et al. Improved unsupervised physics-informed deep learning for intravoxel-incoherent motion modeling and evaluation in pancreatic cancer patients. MRM 2021)
+
+requirements:
+numpy
+torch
+tqdm
+matplotlib
+scipy
+joblib
+"""
+import IVIMNET.simulations as sim
+from hyperparams import hyperparams as hp_example_1
+import IVIMNET.deep as deep
+import time
+import torch
+import IVIMNET.fitting_algorithms as fit
+
+# Import parameters
+arg = hp_example_1()
+arg = deep.checkarg(arg)
+print(arg.save_name)
+for SNR in arg.sim.SNR:
+    # this simulates the signal
+    IVIM_signal_noisy, D, f, Dp = sim.sim_signal(SNR, arg.sim.bvalues, sims=arg.sim.sims, Dmin=arg.sim.range[0][0],
+                                             Dmax=arg.sim.range[1][0], fmin=arg.sim.range[0][1],
+                                             fmax=arg.sim.range[1][1], Dsmin=arg.sim.range[0][2],
+                                             Dsmax=arg.sim.range[1][2], rician=arg.sim.rician)
+    
+    start_time = time.time()
+    # train network
+    net = deep.learn_IVIM(IVIM_signal_noisy, arg.sim.bvalues, arg)
+    elapsed_time = time.time() - start_time
+    print('\ntime elapsed for training: {}\n'.format(elapsed_time))
+
+    # simulate IVIM signal for prediction
+    [dwi_image_long, Dt_truth, Fp_truth, Dp_truth] = sim.sim_signal_predict(arg, SNR)
+
+    # predict
+    start_time = time.time()
+    paramsNN = deep.predict_IVIM(dwi_image_long, arg.sim.bvalues, net, arg)
+    elapsed_time = time.time() - start_time
+    print('\ntime elapsed for inference: {}\n'.format(elapsed_time))
+    # remove network to save memory
+    del net
+    if arg.train_pars.use_cuda:
+        torch.cuda.empty_cache()
+
+    start_time = time.time()
+    # all fitting is done in the fit.fit_dats for the other fitting algorithms (lsq, segmented and Baysesian)
+    paramsf = fit.fit_dats(arg.sim.bvalues, dwi_image_long, arg.fit)
+    elapsed_time = time.time() - start_time
+    print('\ntime elapsed for lsqfit: {}\n'.format(elapsed_time))
+    print('results for lsqfit')
+
+    # plot values predict and truth
+    sim.plot_example1(paramsNN, paramsf, Dt_truth, Fp_truth, Dp_truth, arg, SNR)
diff --git a/src/original/OGC_AUMC_IVIMNET/Example_2_simulations.py b/src/original/OGC_AUMC_IVIMNET/Example_2_simulations.py
new file mode 100644
index 0000000..815e4ba
--- /dev/null
+++ b/src/original/OGC_AUMC_IVIMNET/Example_2_simulations.py
@@ -0,0 +1,48 @@
+"""
+September 2020 by Oliver Gurney-Champion & Misha Kaandorp
+oliver.gurney.champion@gmail.com / o.j.gurney-champion@amsterdamumc.nl
+https://www.github.com/ochampion
+
+Code is uploaded as part of our publication in MRM (Kaandorp et al. Improved unsupervised physics-informed deep learning for intravoxel-incoherent motion modeling and evaluation in pancreatic cancer patients. MRM 2021)
+
+requirements:
+numpy
+torch
+tqdm
+matplotlib
+scipy
+joblib
+"""
+
+# import
+import numpy as np
+import IVIMNET.simulations as sim
+import IVIMNET.deep as deep
+from hyperparams import hyperparams as hp_example
+
+# load hyperparameter
+arg = hp_example()
+arg = deep.checkarg(arg)
+
+matlsq = np.zeros([len(arg.sim.SNR), 3, 3])
+matNN = np.zeros([len(arg.sim.SNR), 3, 3])
+stability = np.zeros([len(arg.sim.SNR), 3])
+a = 0
+
+for SNR in arg.sim.SNR:
+    print('\n simulation at SNR of {snr}\n'.format(snr=SNR))
+    if arg.fit.do_fit:
+        matlsq[a, :, :], matNN[a, :, :], stability[a, :] = sim.sim(SNR, arg)
+        print('\nresults from lsq:')
+        print(matlsq)
+    else:
+        matNN[a, :, :], stability[a, :] = sim.sim(SNR, arg)
+    a = a + 1
+    print('\nresults from NN: columns show themean, the RMSE/mean and the Spearman coef [DvDp,Dvf,fvDp] \n'
+          'the rows show D, f and D*\n'
+          'and the different matixes repressent the different SNR levels {}:'.format(arg.sim.SNR))
+    print(matNN)
+    # if repeat is higher than 1, then print stability (CVNET)
+    if arg.sim.repeats > 1:
+        print('\nstability of NN for D, f and D* at different SNR levels:')
+        print(stability)
diff --git a/src/original/OGC_AUMC_IVIMNET/Example_3_volunteer.py b/src/original/OGC_AUMC_IVIMNET/Example_3_volunteer.py
new file mode 100644
index 0000000..e2e49ea
--- /dev/null
+++ b/src/original/OGC_AUMC_IVIMNET/Example_3_volunteer.py
@@ -0,0 +1,108 @@
+"""
+September 2020 by Oliver Gurney-Champion & Misha Kaandorp
+oliver.gurney.champion@gmail.com / o.j.gurney-champion@amsterdamumc.nl
+https://www.github.com/ochampion
+
+Code is uploaded as part of our publication in MRM (Kaandorp et al. Improved unsupervised physics-informed deep learning for intravoxel-incoherent motion modeling and evaluation in pancreatic cancer patients. MRM 2021)
+
+requirements:
+numpy
+torch
+tqdm
+matplotlib
+scipy
+joblib
+"""
+
+# this loads all patient data and evaluates it all.
+import os
+import time
+import nibabel as nib
+import numpy as np
+import IVIMNET.deep as deep
+import torch
+from IVIMNET.fitting_algorithms import fit_dats
+from hyperparams import hyperparams as hp
+
+arg = hp()
+arg = deep.checkarg(arg)
+
+testdata = False
+
+### folder patient data
+folder = 'example_data'
+
+### load patient data
+print('Load patient data \n')
+# load and init b-values
+text_file = np.genfromtxt('{folder}/bvalues.bval'.format(folder=folder))
+bvalues = np.array(text_file)
+selsb = np.array(bvalues) == 0
+# load nifti
+data = nib.load('{folder}/data.nii.gz'.format(folder=folder))
+datas = data.get_fdata() 
+# reshape image for fitting
+sx, sy, sz, n_b_values = datas.shape 
+X_dw = np.reshape(datas, (sx * sy * sz, n_b_values))
+
+### select only relevant values, delete background and noise, and normalise data
+S0 = np.nanmean(X_dw[:, selsb], axis=1)
+S0[S0 != S0] = 0
+S0 = np.squeeze(S0)
+valid_id = (S0 > (0.5 * np.median(S0[S0 > 0]))) 
+datatot = X_dw[valid_id, :]
+# normalise data
+S0 = np.nanmean(datatot[:, selsb], axis=1).astype('<f')
+datatot = datatot / S0[:, None]
+print('Patient data loaded\n')
+
+### least square fitting
+if arg.fit.do_fit:
+    print('Conventional fitting\n')
+    start_time = time.time()
+    paramslsq = fit_dats(bvalues.copy(), datatot.copy()[:, :len(bvalues)], arg.fit)
+    elapsed_time1 = time.time() - start_time
+    print('\ntime elapsed for lsqfit: {}\n'.format(elapsed_time1))
+    # define names IVIM params
+    names_lsq = ['D_{}_{}'.format(arg.fit.method, 'fitS0' if not arg.fit.fitS0 else 'freeS0'),
+                 'f_{}_{}'.format(arg.fit.method, 'fitS0' if not arg.fit.fitS0 else 'freeS0'),
+                 'Dp_{}_{}'.format(arg.fit.method, 'fitS0' if not arg.fit.fitS0 else 'freeS0')]
+    
+    tot = 0
+    # fill image array
+    for k in range(len(names_lsq)):
+        img = np.zeros([sx * sy * sz])
+        img[valid_id] = paramslsq[k][tot:(tot + sum(valid_id))]
+        img = np.reshape(img, [sx, sy, sz])
+        nib.save(nib.Nifti1Image(img, data.affine, data.header),'{folder}/{name}.nii.gz'.format(folder=folder,name=names_lsq[k]))
+    print('Conventional fitting done\n')
+
+### NN network
+if not arg.train_pars.skip_net:
+    print('NN fitting\n')
+    res = [i for i, val in enumerate(datatot != datatot) if not val.any()] # Remove NaN data
+    start_time = time.time()
+    # train network
+    net = deep.learn_IVIM(datatot[res], bvalues, arg)
+    elapsed_time1net = time.time() - start_time
+    print('\ntime elapsed for Net: {}\n'.format(elapsed_time1net))
+    start_time = time.time()
+    # predict parameters
+    paramsNN = deep.predict_IVIM(datatot, bvalues, net, arg)
+    elapsed_time1netinf = time.time() - start_time
+    print('\ntime elapsed for Net inf: {}\n'.format(elapsed_time1netinf))
+    print('\ndata length: {}\n'.format(len(datatot)))
+    if arg.train_pars.use_cuda:
+        torch.cuda.empty_cache()
+    # define names IVIM params
+    names = ['D_NN_{nn}_lr_{lr}'.format(nn=arg.save_name, lr=arg.train_pars.lr),
+             'f_NN_{nn}_lr_{lr}'.format(nn=arg.save_name, lr=arg.train_pars.lr),
+             'Dp_NN_{nn}_lr_{lr}'.format(nn=arg.save_name, lr=arg.train_pars.lr),]
+    tot = 0
+    # fill image array and make nifti
+    for k in range(len(names)):
+        img = np.zeros([sx * sy * sz])
+        img[valid_id] = paramsNN[k][tot:(tot + sum(valid_id))]
+        img = np.reshape(img, [sx, sy, sz])
+        nib.save(nib.Nifti1Image(img, data.affine, data.header),'{folder}/{name}.nii.gz'.format(folder = folder,name=names[k])),
+    print('NN fitting done\n')
diff --git a/src/original/OGC_AUMC_IVIMNET/hyperparams.py b/src/original/OGC_AUMC_IVIMNET/hyperparams.py
new file mode 100644
index 0000000..1c020bf
--- /dev/null
+++ b/src/original/OGC_AUMC_IVIMNET/hyperparams.py
@@ -0,0 +1,145 @@
+"""
+September 2020 by Oliver Gurney-Champion
+oliver.gurney.champion@gmail.com / o.j.gurney-champion@amsterdamumc.nl
+https://www.github.com/ochampion
+
+Code is uploaded as part of our publication in MRM (Kaandorp et al. Improved unsupervised physics-informed deep learning for intravoxel-incoherent motion modeling and evaluation in pancreatic cancer patients. MRM 2021)
+
+requirements:
+numpy
+torch
+tqdm
+matplotlib
+scipy
+joblib
+"""
+import torch
+import numpy as np
+
+
+#most of these are options from the article and explained in the M&M.
+class train_pars:
+    def __init__(self,nets):
+        self.optim='adam' #these are the optimisers implementd. Choices are: 'sgd'; 'sgdr'; 'adagrad' adam
+        if nets == 'optim':
+            self.lr = 0.00003 # this is the learning rate.
+        elif nets == 'orig':
+            self.lr = 0.001  # this is the learning rate.
+        else:
+            self.lr = 0.00003 # this is the learning rate.
+        self.patience= 10 # this is the number of epochs without improvement that the network waits untill determining it found its optimum
+        self.batch_size= 128 # number of datasets taken along per iteration
+        self.maxit = 500 # max iterations per epoch
+        self.split = 0.9 # split of test and validation data
+        self.load_nn= False # load the neural network instead of retraining
+        self.loss_fun = 'rms' # what is the loss used for the model. rms is root mean square (linear regression-like); L1 is L1 normalisation (less focus on outliers)
+        self.skip_net = False # skip the network training and evaluation
+        self.scheduler = False # as discussed in the article, LR is important. This approach allows to reduce the LR itteratively when there is no improvement throughout an 5 consecutive epochs
+        # use GPU if available
+        self.use_cuda = torch.cuda.is_available()
+        self.device = torch.device("cuda:0" if self.use_cuda else "cpu")
+        self.select_best = False
+
+
+class net_pars:
+    def __init__(self,nets):
+        # select a network setting
+        if (nets == 'optim'):
+            # the optimized network settings
+            self.dropout = 0.1 #0.0/0.1 chose how much dropout one likes. 0=no dropout; internet says roughly 20% (0.20) is good, although it also states that smaller networks might desire smaller amount of dropout
+            self.batch_norm = True # False/True turns on batch normalistion
+            self.parallel = 'parallel' # defines whether the network exstimates each parameter seperately (each parameter has its own network) or whether 1 shared network is used instead
+            self.con = 'sigmoid' # defines the constraint function; 'sigmoid' gives a sigmoid function giving the max/min; 'abs' gives the absolute of the output, 'none' does not constrain the output
+            self.tri_exp = False
+            #### only if sigmoid constraint is used!
+            if self.tri_exp:
+                self.cons_min = [0., 0.0003, 0.0, 0.003, 0.0, 0.08] # F0', D0, F1', D1, F2', D2
+                self.cons_max = [2.5, 0.003, 1, 0.08, 1, 5]  # F0', D0, F1', D1, F2', D2
+            else:
+                self.cons_min = [0, 0, 0.005, 0]  # Dt, Fp, Ds, S0
+                self.cons_max = [0.005, 0.7, 0.2, 2.0]  # Dt, Fp, Ds, S0
+            ####
+            self.fitS0 = True # indicates whether to fit S0 (True) or fix it to 1 (for normalised signals); I prefer fitting S0 as it takes along the potential error is S0.
+            self.depth = 2 # number of layers
+            self.width = 0 # new option that determines network width. Putting to 0 makes it as wide as the number of b-values
+        elif nets == 'orig':
+            # as summarized in Table 1 from the main article for the original network
+            self.dropout = 0.0 #0.0/0.1 chose how much dropout one likes. 0=no dropout; internet says roughly 20% (0.20) is good, although it also states that smaller networks might desire smaller amount of dropout
+            self.batch_norm = False # False/True turns on batch normalistion
+            self.parallel = 'single'  # defines whether the network exstimates each parameter seperately (each parameter has its own network) or whether 1 shared network is used instead
+            self.con = 'abs' # defines the constraint function; 'sigmoid' gives a sigmoid function giving the max/min; 'abs' gives the absolute of the output, 'none' does not constrain the output
+            self.tri_exp = False
+            #### only if sigmoid constraint is used!
+            if self.tri_exp:
+                self.cons_min = [0., 0.0003, 0.0, 0.003, 0.0, 0.08] # F0', D0, F1', D1, F2', D2
+                self.cons_max = [2.5, 0.003, 1, 0.08, 1, 5]  # F0', D0, F1', D1, F2', D2
+            else:
+                self.cons_min = [0, 0, 0.005, 0]  # Dt, Fp, Ds, f0
+                self.cons_max = [0.005, 0.7, 0.2, 2.0]  # Dt, Fp, Ds, f0
+            ####
+            self.fitS0 = False # indicates whether to fit S0 (True) or fix it to 1 (for normalised signals); I prefer fitting S0 as it takes along the potential error is S0.
+            self.depth = 3 # number of layers
+            self.width = 0 # new option that determines network width. Putting to 0 makes it as wide as the number of b-values
+        else:
+            # chose wisely :)
+            self.dropout = 0.3 #0.0/0.1 chose how much dropout one likes. 0=no dropout; internet says roughly 20% (0.20) is good, although it also states that smaller networks might desire smaller amount of dropout
+            self.batch_norm = True # False/True turns on batch normalistion
+            self.parallel = 'parallel' # defines whether the network exstimates each parameter seperately (each parameter has its own network) or whether 1 shared network is used instead
+            self.con = 'sigmoid' # defines the constraint function; 'sigmoid' gives a sigmoid function giving the max/min; 'abs' gives the absolute of the output, 'none' does not constrain the output
+            self.tri_exp = True
+            #### only if sigmoid constraint is used!
+            if self.tri_exp:
+                self.cons_min = [0., 0.0003, 0.0, 0.003, 0.0, 0.08] # F0', D0, F1', D1, F2', D2
+                self.cons_max = [2.5, 0.003, 1, 0.08, 1, 5]  # F0', D0, F1', D1, F2', D2
+            else:
+                self.cons_min = [0, 0, 0.005, 0]  # Dt, Fp, Ds, f0
+                self.cons_max = [0.005, 0.7, 0.2, 2.0]  # Dt, Fp, Ds, f0
+            ####
+            self.fitS0 = False # indicates whether to fit S0 (True) or fix it to 1 (for normalised signals); I prefer fitting S0 as it takes along the potential error is S0.
+            self.depth = 4 # number of layers
+            self.width = 500 # new option that determines network width. Putting to 0 makes it as wide as the number of b-values
+        boundsrange = 0.3 * (np.array(self.cons_max)-np.array(self.cons_min)) # ensure that we are on the most lineair bit of the sigmoid function
+        self.cons_min = np.array(self.cons_min) - boundsrange
+        self.cons_max = np.array(self.cons_max) + boundsrange
+
+
+
+class lsqfit:
+    def __init__(self):
+        self.method = 'lsq' #seg, bayes or lsq
+        self.model = 'bi-exp' #bi-exp or tri-exp
+        self.do_fit = False # skip lsq fitting
+        self.load_lsq = False # load the last results for lsq fit
+        self.fitS0 = True # indicates whether to fit S0 (True) or fix it to 1 in the least squares fit.
+        self.jobs = 4 # number of parallel jobs. If set to 1, no parallel computing is used
+        self.bvalues_included=[50, 700] # for ADC fit
+        if self.model == 'tri-exp':
+            self.bounds = ([0, 0, 0, 0.008, 0, 0.06], [2.5, 0.008, 1, 0.08, 1, 5]) # F0', D0, F1', D1, F2', D2
+        elif self.model == 'ADC':
+            self.bounds = ([0, 0], [0.005, 2.5])  # Dt, Fp, Ds, S0
+        else:
+            self.bounds = ([0.0003, 0, 0.005, 0.5], [0.005, 0.7, 0.3, 2.5])  # Dt, Fp, Ds, S0
+
+class sim:
+    def __init__(self):
+        self.bvalues = np.array([0, 5, 10, 20, 30, 40, 60, 150, 300, 500, 700]) # array of b-values
+        self.SNR = [15, 20, 30, 50] # the SNRs to simulate at
+        self.sims = 1000000 # number of simulations to run
+        self.num_samples_eval = 10000 # number of simualtiosn te evaluate. This can be lower than the number run. Particularly to save time when fitting. More simulations help with generating sufficient data for the neural network
+        self.repeats = 1 # this is the number of repeats for simulations
+        self.rician = False # add rician noise to simulations; if false, gaussian noise is added instead
+        self.model = 'bi-exp'
+        if self.model == 'bi-exp':
+            self.range = ([0.0005, 0.05, 0.01], [0.003, 0.55, 0.1])
+        else:
+            self.range = ([0.0005, 0.05, 0.001, 0.05, 0.08], [0.003, 0.5, 0.05, 0.5, 2]) # D0, F1', D1, F2', D2
+
+
+class hyperparams:
+    def __init__(self):
+        self.fig = False # plot results and intermediate steps
+        self.save_name = 'optim' # orig or optim (or optim_adsig for in vivo)
+        self.net_pars = net_pars(self.save_name)
+        self.train_pars = train_pars(self.save_name)
+        self.fit = lsqfit()
+        self.sim = sim()