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STY: Re-use and define global variables for dMRI model #208

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STY: Re-use and define global variables for dMRI model #208

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69f4c4d
STY: Re-use the lower-bound b and S0 thresholds from dMRI data module
jhlegarreta Aug 12, 2025
6a26bcb
STY: Define the S0 clipping percentile as a global variable
jhlegarreta Aug 12, 2025
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16 changes: 9 additions & 7 deletions src/nifreeze/model/dmri.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,12 +28,12 @@
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
DEFAULT_S0_CLIP_PERCENTILE = 98
"""Upper percentile threshold for non-diffusion-weighted signal estimation."""


def _exec_fit(model, data, chunk=None, **kwargs):
Expand Down Expand Up @@ -79,7 +79,7 @@ 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:
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@jhlegarreta jhlegarreta Aug 12, 2025 •
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Note that as explained in cd9f6ca, the b0 lower bound is changed from 1e-6 to 1e-5.

self._max_b = max_b

self._data_mask = (
Expand All @@ -88,15 +88,17 @@ def __init__(self, dataset: DWI, max_b: float | int | None = None, **kwargs):
else np.ones(dataset.dataobj.shape[:3], dtype=bool)
)

# By default, set S0 to the 98% percentile of the DWI data within mask
# By default, set S0 to the q-th percentile of the DWI data within mask
self._S0 = np.full(
self._data_mask.sum(),
np.round(np.percentile(dataset.dataobj[self._data_mask, ...], 98)),
np.round(
np.percentile(dataset.dataobj[self._data_mask, ...], DEFAULT_S0_CLIP_PERCENTILE)
),
)

# 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)
Expand Down