centralize b-value management at initialization (#155)

WrapImage/nifti_wrapper.py

@@ -100,7 +100,7 @@ def loop_over_first_n_minus_1_dimensions(arr):
 
         # Pass additional arguments to the algorithm
 
-        fit = OsipiBase(algorithm=args.algorithm)
+        fit = OsipiBase(algorithm=args.algorithm, bvalues=bvals)
         f_image = []
         Dp_image = []
         D_image = []
@@ -109,7 +109,7 @@ def loop_over_first_n_minus_1_dimensions(arr):
         n = data.ndim
         total_iteration = np.prod(data.shape[:n-1])
         for idx, view in tqdm(loop_over_first_n_minus_1_dimensions(data), desc=f"{args.algorithm} is fitting", dynamic_ncols=True, total=total_iteration):
-            fit_result = fit.osipi_fit(view, bvals)
+            fit_result = fit.osipi_fit(view)
             f_image.append(fit_result["f"])
             Dp_image.append(fit_result["Dp"])
             D_image.append(fit_result["D"])

WrapImage/nifti_wrapper_kaapana.py

@@ -127,7 +127,7 @@ def load_config():
         affine = np.array(affine_override).reshape(4, 4)
 
     # Initialize model
-    fit = OsipiBase(algorithm=algorithm)
+    fit = OsipiBase(algorithm=algorithm, bvalues=bvals)
 
     # Preallocate output arrays
     shape = data.shape[:data.ndim - 1]
@@ -142,7 +142,7 @@ def load_config():
         loop_over_first_n_minus_1_dimensions(data),
         desc="Fitting IVIM model", dynamic_ncols=True, total=total_iteration
     ):
-        fit_result = fit.osipi_fit(view, bvals)
+        fit_result = fit.osipi_fit(view)
         f_image[idx] = fit_result["f"]
         Dp_image[idx] = fit_result["Dp"]
         D_image[idx] = fit_result["D"]

src/standardized/ASD_MemorialSloanKettering_QAMPER_IVIM.py

@@ -66,12 +66,11 @@ def algorithm(self,dwi_arr, bval_arr, LB0, UB0, x0in):
         (f_arr, D_arr, Dx_arr, s0_arr, fitted_dwi_arr, RSS, rms_val, chi, AIC, BIC, R_sq) = results
         return D_arr/1000, f_arr, Dx_arr/1000, s0_arr
 
-    def ivim_fit(self, signals, bvalues, **kwargs):
+    def ivim_fit(self, signals, **kwargs):
         """Perform the IVIM fit
 
         Args:
             signals (array-like)
-            bvalues (array-like, optional): b-values for the signals. If None, self.bvalues will be used. Default is None.
 
         Returns:
             _type_: _description_
@@ -81,7 +80,7 @@ def ivim_fit(self, signals, bvalues, **kwargs):
 
         initial_guess = [self.initial_guess["D"], self.initial_guess["f"], self.initial_guess["Dp"], self.initial_guess["S0"]]
 
-        bvalues=np.array(bvalues)
+        bvalues=np.array(self.bvalues)
         LB = np.array(bounds[0])[[1,0,2,3]]
         UB = np.array(bounds[1])[[1,0,2,3]]
 

src/standardized/DT_IIITN_WLS.py

@@ -66,17 +66,16 @@ def __init__(self, bvalues=None, thresholds=None,
         )
         self.method = method.upper()
 
-    def ivim_fit(self, signals, bvalues, **kwargs):
+    def ivim_fit(self, signals, **kwargs):
         """Perform the IVIM fit using the selected method (WLS or RLM).
 
         Args:
             signals (array-like): Signal intensities at each b-value.
-            bvalues (array-like, optional): b-values for the signals.
 
         Returns:
             dict: Dictionary with keys "D", "f", "Dp".
         """
-        bvalues = np.array(bvalues)
+        bvalues = np.array(self.bvalues)
 
         # Use threshold as cutoff if available
         cutoff = 200

src/standardized/ETP_SRI_LinearFitting.py

@@ -59,20 +59,18 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
         # Check the inputs
 
 
-    def ivim_fit(self, signals, bvalues=None, linear_fit_option=False, **kwargs):
+    def ivim_fit(self, signals, linear_fit_option=False, **kwargs):
         """Perform the IVIM fit
 
         Args:
             signals (array-like)
-            bvalues (array-like, optional): b-values for the signals. If None, self.bvalues will be used. Default is None.
             linear_fit_option (bool, optional): This fit has an option to only run a linear fit. Defaults to False.
 
         Returns:
             _type_: _description_
         """
         signals[signals<0.0000001]=0.0000001
-        if bvalues is None:
-            bvalues = self.bvalues
+        bvalues = self.bvalues
 
         if self.thresholds is None:
             ETP_object = LinearFit()

src/standardized/IAR_LU_biexp.py

@@ -71,12 +71,11 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
             self.IAR_algorithm = None
 
 
-    def ivim_fit(self, signals, bvalues, **kwargs):
+    def ivim_fit(self, signals, **kwargs):
         """Perform the IVIM fit
 
         Args:
             signals (array-like)
-            bvalues (array-like, optional): b-values for the signals. If None, self.bvalues will be used. Default is None.
 
         Returns:
             _type_: _description_
@@ -88,10 +87,7 @@ def ivim_fit(self, signals, bvalues, **kwargs):
         initial_guess = [self.initial_guess["S0"], self.initial_guess["f"], self.initial_guess["Dp"], self.initial_guess["D"]]
 
         if self.IAR_algorithm is None:
-            if bvalues is None:
-                bvalues = self.bvalues
-            else:
-                bvalues = np.asarray(bvalues)
+            bvalues = np.asarray(self.bvalues)
 
             bvec = np.zeros((bvalues.size, 3))
             bvec[:,2] = 1
@@ -110,12 +106,11 @@ def ivim_fit(self, signals, bvalues, **kwargs):
         return results
 
 
-    def ivim_fit_full_volume(self, signals, bvalues, **kwargs):
+    def ivim_fit_full_volume(self, signals, **kwargs):
         """Perform the IVIM fit
 
         Args:
             signals (array-like)
-            bvalues (array-like, optional): b-values for the signals. If None, self.bvalues will be used. Default is None.
 
         Returns:
             _type_: _description_
@@ -125,16 +120,14 @@ def ivim_fit_full_volume(self, signals, bvalues, **kwargs):
                        [self.bounds["S0"][1], self.bounds["f"][1], self.bounds["Dp"][1], self.bounds["D"][1]]]
         initial_guess = [self.initial_guess["S0"], self.initial_guess["f"], self.initial_guess["Dp"], self.initial_guess["D"]]
         if self.IAR_algorithm is None:
-            if bvalues is None:
-                bvalues = self.bvalues
-            else:
-                bvalues = np.asarray(bvalues)
+            bvalues = np.asarray(self.bvalues)
 
             bvec = np.zeros((bvalues.size, 3))
             bvec[:,2] = 1
             gtab = gradient_table(bvalues, bvecs=bvec, b0_threshold=0)
 
             self.IAR_algorithm = IvimModelBiExp(gtab, bounds=bounds, initial_guess=initial_guess)
+        bvalues = np.asarray(self.bvalues)
         b0_index = np.where(bvalues == 0)[0][0]
         mask = signals[...,b0_index]>0
         fit_results = self.IAR_algorithm.fit(signals, mask=mask)

src/standardized/IAR_LU_modified_mix.py

@@ -68,12 +68,11 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
             self.IAR_algorithm = None
 
 
-    def ivim_fit(self, signals, bvalues, **kwargs):
+    def ivim_fit(self, signals, **kwargs):
         """Perform the IVIM fit
 
         Args:
             signals (array-like)
-            bvalues (array-like, optional): b-values for the signals. If None, self.bvalues will be used. Default is None.
 
         Returns:
             _type_: _description_
@@ -83,10 +82,7 @@ def ivim_fit(self, signals, bvalues, **kwargs):
                   [self.bounds["f"][1], self.bounds["Dp"][1]*1000, self.bounds["D"][1]*1000]]
 
         if self.IAR_algorithm is None:
-            if bvalues is None:
-                bvalues = self.bvalues
-            else:
-                bvalues = np.asarray(bvalues)
+            bvalues = np.asarray(self.bvalues)
 
             bvec = np.zeros((bvalues.size, 3))
             bvec[:,2] = 1

src/standardized/IAR_LU_modified_topopro.py

@@ -72,12 +72,11 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
             self.IAR_algorithm = None
 
 
-    def ivim_fit(self, signals, bvalues, **kwargs):
+    def ivim_fit(self, signals, **kwargs):
         """Perform the IVIM fit
 
         Args:
             signals (array-like)
-            bvalues (array-like, optional): b-values for the signals. If None, self.bvalues will be used. Default is None.
 
         Returns:
             _type_: _description_
@@ -86,10 +85,7 @@ def ivim_fit(self, signals, bvalues, **kwargs):
                   [self.bounds["f"][1], self.bounds["Dp"][1]*1000, self.bounds["D"][1]*1000]]
 
         if self.IAR_algorithm is None:
-            if bvalues is None:
-                bvalues = self.bvalues
-            else:
-                bvalues = np.asarray(bvalues)
+            bvalues = np.asarray(self.bvalues)
 
             bvec = np.zeros((bvalues.size, 3))
             bvec[:,2] = 1

src/standardized/IAR_LU_segmented_2step.py

@@ -72,12 +72,11 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
             self.IAR_algorithm = None
 
 
-    def ivim_fit(self, signals, bvalues, thresholds=None, **kwargs):
+    def ivim_fit(self, signals, **kwargs):
         """Perform the IVIM fit
 
         Args:
             signals (array-like)
-            bvalues (array-like, optional): b-values for the signals. If None, self.bvalues will be used. Default is None.
 
         Returns:
             _type_: _description_
@@ -90,10 +89,7 @@ def ivim_fit(self, signals, bvalues, thresholds=None, **kwargs):
         initial_guess = [self.initial_guess["S0"], self.initial_guess["f"], self.initial_guess["Dp"], self.initial_guess["D"]]
 
         if self.IAR_algorithm is None:
-            if bvalues is None:
-                bvalues = self.bvalues
-            else:
-                bvalues = np.asarray(bvalues)
+            bvalues = np.asarray(self.bvalues)
 
             bvec = np.zeros((bvalues.size, 3))
             bvec[:,2] = 1

src/standardized/IAR_LU_segmented_3step.py

@@ -75,12 +75,11 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
             self.IAR_algorithm = None
 
 
-    def ivim_fit(self, signals, bvalues, **kwargs):
+    def ivim_fit(self, signals, **kwargs):
         """Perform the IVIM fit
 
         Args:
             signals (array-like)
-            bvalues (array-like, optional): b-values for the signals. If None, self.bvalues will be used. Default is None.
 
         Returns:
             _type_: _description_
@@ -93,10 +92,7 @@ def ivim_fit(self, signals, bvalues, **kwargs):
         initial_guess = [self.initial_guess["S0"], self.initial_guess["f"], self.initial_guess["Dp"], self.initial_guess["D"]]
 
         if self.IAR_algorithm is None:
-            if bvalues is None:
-                bvalues = self.bvalues
-            else:
-                bvalues = np.asarray(bvalues)
+            bvalues = np.asarray(self.bvalues)
 
             bvec = np.zeros((bvalues.size, 3))
             bvec[:,2] = 1

src/standardized/IAR_LU_subtracted.py

@@ -72,12 +72,11 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
             self.IAR_algorithm = None
 
 
-    def ivim_fit(self, signals, bvalues, **kwargs):
+    def ivim_fit(self, signals, **kwargs):
         """Perform the IVIM fit
 
         Args:
             signals (array-like)
-            bvalues (array-like, optional): b-values for the signals. If None, self.bvalues will be used. Default is None.
 
         Returns:
             _type_: _description_
@@ -90,10 +89,7 @@ def ivim_fit(self, signals, bvalues, **kwargs):
         initial_guess = [self.initial_guess["S0"], self.initial_guess["f"], self.initial_guess["Dp"], self.initial_guess["D"]]
 
         if self.IAR_algorithm is None:
-            if bvalues is None:
-                bvalues = self.bvalues
-            else:
-                bvalues = np.asarray(bvalues)
+            bvalues = np.asarray(self.bvalues)
 
             bvec = np.zeros((bvalues.size, 3))
             bvec[:,2] = 1

src/standardized/IVIM_NEToptim.py

@@ -89,18 +89,15 @@ def initialize(self, bounds, initial_guess, fitS0, traindata, SNR, n):
         self.algorithm =lambda data: deep.predict_IVIM(data, self.bvalues, self.net, self.arg)
 
 
-    def ivim_fit(self, signals, bvalues, **kwargs):
+    def ivim_fit(self, signals, **kwargs):
         """Perform the IVIM fit
 
         Args:
             signals (array-like)
-            bvalues (array-like): b-values for the signals. If None, self.bvalues will be used. Default is None.
 
         Returns:
             _type_: _description_
         """
-        if not np.array_equal(bvalues, self.bvalues):
-            raise ValueError("bvalue list at fitting must be identical as the one at initiation, otherwise it will not run")
 
         paramsNN = deep.predict_IVIM(signals, self.bvalues, self.net, self.arg)
 
@@ -112,18 +109,15 @@ def ivim_fit(self, signals, bvalues, **kwargs):
         return results
 
 
-    def ivim_fit_full_volume(self, signals, bvalues, retrain_on_input_data=False, **kwargs):
+    def ivim_fit_full_volume(self, signals, retrain_on_input_data=False, **kwargs):
         """Perform the IVIM fit
 
         Args:
             signals (array-like)
-            bvalues (array-like): b-values for the signals. If None, self.bvalues will be used. Default is None.
 
         Returns:
             _type_: _description_
         """
-        if not np.array_equal(bvalues, self.bvalues):
-            raise ValueError("bvalue list at fitting must be identical as the one at initiation, otherwise it will not run")
         minimum_bvalue = np.min(self.bvalues) # We normalize the signal to the minimum bvalue. Should be 0 or very close to 0.
         b0_indices = np.where(self.bvalues == minimum_bvalue)[0]
         normalization_factor = np.mean(signals[..., b0_indices],axis=-1)

src/standardized/OGC_AmsterdamUMC_Bayesian_biexp.py

@@ -73,12 +73,11 @@ def initialize(self, bounds=None, initial_guess=None, fitS0=True, prior_in=None,
                 self.neg_log_prior = empirical_neg_log_prior(prior_in[0], prior_in[1], prior_in[2])
         self.fitS0=fitS0
 
-    def ivim_fit(self, signals, bvalues, initial_guess=None, **kwargs):
+    def ivim_fit(self, signals, initial_guess=None, **kwargs):
         """Perform the IVIM fit
 
         Args:
             signals (array-like)
-            bvalues (array-like, optional): b-values for the signals. If None, self.bvalues will be used. Default is None.
 
         Returns:
             _type_: _description_
@@ -88,7 +87,7 @@ def ivim_fit(self, signals, bvalues, initial_guess=None, **kwargs):
 
         initial_guess = [self.initial_guess["D"], self.initial_guess["f"], self.initial_guess["Dp"], self.initial_guess["S0"]]
 
-        bvalues=np.array(bvalues)
+        bvalues=np.array(self.bvalues)
 
         epsilon = 0.000001
         fit_results = fit_segmented(bvalues, signals, bounds=bounds, cutoff=self.thresholds, p0=initial_guess)
@@ -105,11 +104,10 @@ def ivim_fit(self, signals, bvalues, initial_guess=None, **kwargs):
 
         return results
 
-    def ivim_fit_full_volume(self, signals, bvalues, njobs=4, **kwargs):
+    def ivim_fit_full_volume(self, signals, njobs=4, **kwargs):
         """Perform the IVIM fit
         Args:
             signals (array-like)
-            bvalues (array-like, optional): b-values for the signals. If None, self.bvalues will be used. Default is None.
         Returns:
             _type_: _description_
         """
@@ -122,6 +120,7 @@ def ivim_fit_full_volume(self, signals, bvalues, njobs=4, **kwargs):
         # Get index of b=0
         shape=np.shape(signals)
 
+        bvalues=np.array(self.bvalues)
         b0_index = np.where(bvalues == 0)[0][0]
         # Mask of voxels where signal at b=0 >= 0.5
         valid_mask = signals[..., b0_index] >= 0
@@ -133,7 +132,7 @@ def ivim_fit_full_volume(self, signals, bvalues, njobs=4, **kwargs):
         normalization_factor = np.mean(signals[..., b0_indices],axis=-1)
         signals = signals / np.repeat(normalization_factor[...,np.newaxis],np.shape(signals)[-1],-1)
 
-        bvalues=np.array(bvalues)
+        bvalues=np.array(self.bvalues)
 
         epsilon = 0.000001
         fit_results = np.array(fit_segmented_array(bvalues, signals, bounds=bounds, cutoff=self.thresholds, p0=initial_guess))