STY: Re-use the lower-bound b and S0 thresholds from dMRI data module

Re-use the lower-bound b and S0 thresholds from dMRI data module in the
dMRI model for the sake of consistency.

The data module minimum S0 value is 1e-5 as opposed to the 1e-6 value
that was being used in the model module.

Author: jhlegarreta

src/nifreeze/model/dmri.py

@@ -28,13 +28,10 @@
 from dipy.core.gradients import gradient_table_from_bvals_bvecs
 from joblib import Parallel, delayed
 
-from nifreeze.data.dmri import DTI_MIN_ORIENTATIONS, DWI
+from nifreeze.data.dmri import DEFAULT_LOWB_THRESHOLD, DEFAULT_MIN_S0, DTI_MIN_ORIENTATIONS, DWI
 from nifreeze.data.filtering import BVAL_ATOL, dwi_select_shells, grand_mean_normalization
 from nifreeze.model.base import BaseModel, ExpectationModel
 
-S0_EPSILON = 1e-6
-B_MIN = 50
-
 
 def _exec_fit(model, data, chunk=None, **kwargs):
     return model.fit(data, **kwargs), chunk
@@ -79,7 +76,7 @@ def __init__(self, dataset: DWI, max_b: float | int | None = None, **kwargs):
                 f"DWI dataset is too small ({dataset.gradients.shape[0]} directions)."
             )
 
-        if max_b is not None and max_b > B_MIN:
+        if max_b is not None and max_b > DEFAULT_LOWB_THRESHOLD:
             self._max_b = max_b
 
         self._data_mask = (
@@ -96,7 +93,7 @@ def __init__(self, dataset: DWI, max_b: float | int | None = None, **kwargs):
 
         # If b=0 is present and not to be ignored, update brain mask and set
         if not kwargs.pop("ignore_bzero", False) and dataset.bzero is not None:
-            self._data_mask[dataset.bzero < S0_EPSILON] = False
+            self._data_mask[dataset.bzero < DEFAULT_MIN_S0] = False
             self._S0 = dataset.bzero[self._data_mask]
 
         super().__init__(dataset, **kwargs)