Merge pull request #107 from nipreps/enh/data-driven-b0-identification

ENH: Data-driven b0 identification tool

Author: oesteban

dmriprep/utils/tests/test_vectors.py

@@ -1,6 +1,7 @@
 """Test vector utilities."""
 import pytest
 import numpy as np
+import nibabel as nb
 from dmriprep.utils import vectors as v
 from collections import namedtuple
 
@@ -100,3 +101,25 @@ def mock_func(*args, **kwargs):
     # Miscellaneous tests
     with pytest.raises(ValueError):
         dgt.to_filename("path", filetype="mrtrix")
+
+
+def test_b0mask_from_data(tmp_path):
+    """Check the estimation of bzeros using the dwi data."""
+
+    highb = np.random.normal(100, 5, size=(40, 40, 40, 99))
+    mask_file = tmp_path / 'mask.nii.gz'
+
+    # Test 1: no lowb
+    dwi_file = tmp_path / 'only_highb.nii.gz'
+    nb.Nifti1Image(highb.astype(float), np.eye(4), None).to_filename(dwi_file)
+    nb.Nifti1Image(np.ones((40, 40, 40), dtype=np.uint8),
+                   np.eye(4), None).to_filename(mask_file)
+
+    assert v.b0mask_from_data(dwi_file, mask_file).sum() == 0
+
+    # Test 1: one lowb
+    lowb = np.random.normal(400, 50, size=(40, 40, 40, 1))
+    dwi_file = tmp_path / 'dwi.nii.gz'
+    nb.Nifti1Image(np.concatenate((lowb, highb), axis=3).astype(float),
+                   np.eye(4), None).to_filename(dwi_file)
+    assert v.b0mask_from_data(dwi_file, mask_file).sum() == 1

dmriprep/utils/vectors.py

@@ -483,3 +483,34 @@ def bvecs2ras(affine, bvecs, norm=True, bvec_norm_epsilon=0.2):
         rotated_bvecs[~b0s] /= norms_bvecs[~b0s, np.newaxis]
         rotated_bvecs[b0s] = np.zeros(3)
     return rotated_bvecs
+
+
+def rasb_dwi_length_check(dwi_file, rasb_file):
+    """Check the number of encoding vectors and number of orientations in the DWI file."""
+    return nb.load(dwi_file).shape[-1] == len(np.loadtxt(rasb_file, skiprows=1))
+
+
+def b0mask_from_data(dwi_file, mask_file, z_thres=3.0):
+    """
+    Evaluate B0 locations relative to mean signal variation.
+
+    Standardizes (z-score) the average DWI signal within mask and threshold.
+    This is a data-driven way of estimating which volumes in the DWI dataset are
+    really encoding *low-b* acquisitions.
+
+    Parameters
+    ----------
+    dwi_file : :obj:`str`
+        File path to the diffusion-weighted image series.
+    mask_file : :obj:`str`
+        File path to a mask corresponding to the DWI file.
+    z_thres : :obj:`float`
+        The z-value to consider a volume as a *low-b* orientation.
+
+    """
+    data = np.asanyarray(nb.load(dwi_file).dataobj)
+    mask = np.asanyarray(nb.load(mask_file).dataobj) > 0.5
+    signal_means = np.median(data[mask, np.newaxis], axis=0)
+    zscored_means = signal_means - np.median(signal_means)
+    zscored_means /= zscored_means.std()
+    return zscored_means > z_thres