ENH: Remove redundant DIPY function

Remove redundant DIPY `dipy.stats.qc.find_qspace_neighbors` function:
DIPY 1.10.0 was released on Dec 12, 2024, and includes the commit where
the function at issue was added:
https://docs.dipy.org/stable/release_notes/release1.10.html
dipy/dipy#3156

This patch set will additionally fix the DIPY keyword-only argument
warning:
```
mriqc/interfaces/diffusion.py::mriqc.interfaces.diffusion._find_qspace_neighbors
  /src/mriqc/mriqc/interfaces/diffusion.py:1435:
   UserWarning: Pass ['bvecs'] as keyword args. From version 2.0.0 passing
   these as positional arguments will result in an error.
    gtab = gradient_table(bvals, bvecs)
```

raised for example in:
https://app.circleci.com/pipelines/github/nipreps/mriqc/1651/workflows/01145976-37e9-4633-a00b-3ffe27053801/jobs/10057

mriqc/interfaces/diffusion.py

@@ -27,6 +27,8 @@
 import nibabel as nb
 import numpy as np
 import scipy.ndimage as nd
+from dipy.core.gradients import gradient_table
+from dipy.stats.qc import find_qspace_neighbors
 from nipype.interfaces.base import (
     BaseInterfaceInputSpec as _BaseInterfaceInputSpec,
 )
@@ -339,7 +341,9 @@ def _run_interface(self, runtime):
         bvecs = np.loadtxt(self._results['out_bvec_file']).T
         bvals = np.loadtxt(self._results['out_bval_file'])
 
-        self._results['qspace_neighbors'] = _find_qspace_neighbors(bvals, bvecs)
+        gtab = gradient_table(bvals, bvecs=bvecs)
+
+        self._results['qspace_neighbors'] = find_qspace_neighbors(gtab)
         self._results['out_bmatrix'] = np.hstack((bvecs, bvals[:, np.newaxis])).tolist()
 
         return runtime
@@ -1386,80 +1390,6 @@ def get_spike_mask(
     return spike_mask
 
 
-def _find_qspace_neighbors(bvals: np.ndarray, bvecs: np.ndarray) -> list[tuple[int, int]]:
-    """
-    Create a mapping of dwi volume index to its nearest neighbor in q-space.
-
-    This function implements an approximate nearest neighbor search in q-space
-    (excluding delta encoding). It calculates the Cartesian distance between
-    q-space representations of each diffusion-weighted imaging (DWI) volume
-    (represented by b-values and b-vectors) and identifies the closest one
-    (excluding the volume itself and b=0 volumes).
-
-    Parameters
-    ----------
-    bvals : :obj:`~numpy.ndarray`
-        List of b-values.
-    bvecs : :obj:`~numpy.ndarray`
-        Table of b-vectors.
-
-    Returns
-    -------
-    :obj:`list` of :obj:`tuple`
-        A list of 2-tuples indicating the nearest q-space neighbor
-        of each dwi volume.
-
-    Examples
-    --------
-    >>> _find_qspace_neighbors(
-    ...     np.array([0, 1000, 1000, 2000]),
-    ...     np.array([
-    ...         [1, 0, 0],
-    ...         [1, 0, 0],
-    ...         [0.99, 0.0001, 0.0001],
-    ...         [1, 0, 0]
-    ...     ]),
-    ... )
-    [(1, 2), (2, 1), (3, 1)]
-
-    Notes
-    -----
-    This is a copy of DIPY's code to be removed (and just imported) as soon as
-    a new release of DIPY is cut including
-    `dipy/dipy#3156 <https://github.com/dipy/dipy/pull/3156>`__.
-
-    """
-    from dipy.core.geometry import cart_distance
-    from dipy.core.gradients import gradient_table
-
-    gtab = gradient_table(bvals, bvecs)
-
-    dwi_neighbors: list[tuple[int, int]] = []
-
-    # Only correlate the b>0 images
-    dwi_indices = np.flatnonzero(~gtab.b0s_mask)
-
-    # Get a pseudo-qspace value for b>0s
-    qvecs = np.sqrt(gtab.bvals)[:, np.newaxis] * gtab.bvecs
-
-    for dwi_index in dwi_indices:
-        qvec = qvecs[dwi_index]
-
-        # Calculate distance in q-space, accounting for symmetry
-        pos_dist = cart_distance(qvec[np.newaxis, :], qvecs)
-        neg_dist = cart_distance(qvec[np.newaxis, :], -qvecs)
-        distances = np.min(np.column_stack([pos_dist, neg_dist]), axis=1)
-
-        # Be sure we don't select the image as its own neighbor
-        distances[dwi_index] = np.inf
-        # Or a b=0
-        distances[gtab.b0s_mask] = np.inf
-        neighbor_index = np.argmin(distances)
-        dwi_neighbors.append((dwi_index, neighbor_index))
-
-    return dwi_neighbors
-
-
 def noise_piesno(data: np.ndarray, n_channels: int = 4) -> (np.ndarray, np.ndarray):
     """
     Estimates noise in raw diffusion MRI (dMRI) data using the PIESNO algorithm.