Normalize the signal to the minimum b-value, not an explicit b0

Author: IvanARashid

src/wrappers/OsipiBase.py

@@ -146,7 +146,8 @@ def osipi_fit_full_volume(self, data, bvalues=None, **kwargs):
             for key in self.result_keys:
                 results[key] = np.empty(list(data.shape[:-1]))
 
-            b0_indices = np.where(use_bvalues == 0)[0]
+            minimum_bvalue = np.min(use_bvalues) # We normalize the signal to the minimum bvalue. Should be 0 or very close to 0.
+            b0_indices = np.where(use_bvalues == minimum_bvalue)[0]
             b0_mean = np.mean(data[..., b0_indices], axis=-1)
 
             normalization_factors = np.array([b0_mean for i in range(data.shape[-1])])