docs/changes/newsfragments/302.enh

@@ -0,0 +1 @@
+Allow :class:`.BOLDWarper` to warp BOLD data to other MNI spaces by `Synchon Mandal`_

junifer/preprocess/bold_warper.py

@@ -13,8 +13,13 @@ from typing import (
     Union,
 )
 
+import nibabel as nib
+from templateflow import api as tflow
+
 from ..api.decorators import register_preprocessor
-from ..utils import logger, raise_error
+from ..data import get_template, get_xfm
+from ..pipeline import WorkDirManager
+from ..utils import logger, raise_error, run_ext_cmd
 from .ants.ants_apply_transforms_warper import _AntsApplyTransformsWarper
 from .base import BasePreprocessor
 from .fsl.apply_warper import _ApplyWarper
@@ -27,7 +32,13 @@ class BOLDWarper(BasePreprocessor):
     Parameters
     ----------
     reference : str
-        The data type to use as reference for warping.
+        The data type to use as reference for warping, can be either a data
+        type like "T1w" or a template space like "MNI152NLin2009cAsym".
+
+    Raises
+    ------
+    ValueError
+        If ``reference`` is invalid.
 
     """
 
@@ -49,9 +60,15 @@ class BOLDWarper(BasePreprocessor):
     def __init__(self, reference: str) -> None:
         """Initialize the class."""
         self.ref = reference
+        # Initialize superclass based on reference
+        if self.ref == "T1w":
             super().__init__(
                 on="BOLD", required_data_types=["BOLD", self.ref, "Warp"]
             )
+        elif self.ref in tflow.templates():
+            super().__init__(on="BOLD", required_data_types=["BOLD"])
+        else:
+            raise_error(f"Unknown reference: {self.ref}")
 
     def get_valid_inputs(self) -> List[str]:
         """Get valid data types for input.
@@ -97,7 +114,8 @@ class BOLDWarper(BasePreprocessor):
             The BOLD input from the Junifer Data object.
         extra_input : dict, optional
             The other fields in the Junifer Data object. Must include the
-            ``Warp`` and ``ref`` value's keys.
+            ``Warp`` and ``ref`` value's keys if native space transformation is
+            needed.
 
         Returns
         -------
@@ -110,17 +128,23 @@ class BOLDWarper(BasePreprocessor):
         Raises
         ------
         ValueError
-            If ``extra_input`` is None.
+            If ``extra_input`` is None when transforming to native space
+            i.e., using "T1w" as reference.
         RuntimeError
-            If warp / transformation file extension is not ".mat" or ".h5".
+            If warp / transformation file extension is not ".mat" or ".h5"
+            when transforming to native space or
+            if the BOLD data is in the correct space and does not require
+            warping.
 
         """
-        logger.debug("Warping BOLD using BOLDWarper")
+        logger.info(f"Warping BOLD to {self.ref} space using BOLDWarper")
+        # Transform to native space
+        if self.ref == "T1w":
             # Check for extra inputs
             if extra_input is None:
                 raise_error(
-                f"No extra input provided, requires `Warp` and `{self.ref}` "
+                    "No extra input provided, requires `Warp` and "
-                "data types in particular."
+                    f"`{self.ref}` data types in particular."
                 )
             # Check for warp file type to use correct tool
             warp_file_ext = extra_input["Warp"]["path"].suffix
@@ -152,4 +176,69 @@ class BOLDWarper(BasePreprocessor):
                     ),
                     klass=RuntimeError,
                 )
+        # Transform to template space
+        else:
+            # Check pre-requirements for space manipulation
+            if self.ref == input["space"]:
+                raise_error(
+                    (
+                        f"Skipped warping as the BOLD data is in {self.ref} "
+                        "space which would mean that you can remove the "
+                        "BOLDWarper from the preprocess step."
+                    ),
+                    klass=RuntimeError,
+                )
+            else:
+                # Get xfm file
+                xfm_file_path = get_xfm(src=input["space"], dst=self.ref)
+                # Get template space image
+                template_space_img = get_template(
+                    space=self.ref,
+                    target_data=input,
+                    extra_input=None,
+                )
+
+                # Create component-scoped tempdir
+                tempdir = WorkDirManager().get_tempdir(prefix="boldwarper")
+                # Create element-scoped tempdir so that warped BOLD is
+                # available later as nibabel stores file path reference for
+                # loading on computation
+                element_tempdir = WorkDirManager().get_element_tempdir(
+                    prefix="boldwarper"
+                )
+
+                # Save template
+                template_space_img_path = tempdir / f"{self.ref}_T1w.nii.gz"
+                nib.save(template_space_img, template_space_img_path)
+
+                # Create a tempfile for warped output
+                warped_bold_path = (
+                    element_tempdir
+                    / f"bold_warped_from_{input['space']}_to_{self.ref}.nii.gz"
+                )
+
+                logger.debug(
+                    f"Using ANTs to warp BOLD "
+                    f"from {input['space']} to {self.ref}"
+                )
+                # Set antsApplyTransforms command
+                apply_transforms_cmd = [
+                    "antsApplyTransforms",
+                    "-d 3",
+                    "-e 3",
+                    "-n LanczosWindowedSinc",
+                    f"-i {input['path'].resolve()}",
+                    f"-r {template_space_img_path.resolve()}",
+                    f"-t {xfm_file_path.resolve()}",
+                    f"-o {warped_bold_path.resolve()}",
+                ]
+                # Call antsApplyTransforms
+                run_ext_cmd(
+                    name="antsApplyTransforms", cmd=apply_transforms_cmd
+                )
+
+                # Modify target data
+                input["data"] = nib.load(warped_bold_path)
+                input["space"] = self.ref
+
         return "BOLD", input

junifer/preprocess/tests/test_bold_warper.py

@@ -7,6 +7,7 @@ import socket
 from typing import TYPE_CHECKING, List, Tuple
 
 import pytest
+from numpy.testing import assert_array_equal, assert_raises
 
 from junifer.datagrabber import DataladHCP1200, DMCC13Benchmark
 from junifer.datareader import DefaultDataReader
@@ -83,7 +84,7 @@ def test_BOLDWarper_get_output_type(input_: List[str]) -> None:
     socket.gethostname() != "juseless",
     reason="only for juseless",
 )
-def test_BOLDWarper_preprocess(
+def test_BOLDWarper_preprocess_to_native(
     datagrabber: "BaseDataGrabber", element: Tuple[str, ...]
 ) -> None:
     """Test BOLDWarper preprocess.
@@ -106,3 +107,70 @@ def test_BOLDWarper_preprocess(
         )
         assert data_type == "BOLD"
         assert isinstance(data, dict)
+
+
+@pytest.mark.parametrize(
+    "datagrabber, element, space",
+    [
+        [
+            DMCC13Benchmark(
+                types=["BOLD"],
+                sessions=["wave1bas"],
+                tasks=["Rest"],
+                phase_encodings=["AP"],
+                runs=["1"],
+                native_t1w=False,
+            ),
+            ("f9057kp", "wave1bas", "Rest", "AP", "1"),
+            "MNI152NLin2009aAsym",
+        ],
+        [
+            DMCC13Benchmark(
+                types=["BOLD"],
+                sessions=["wave1bas"],
+                tasks=["Rest"],
+                phase_encodings=["AP"],
+                runs=["1"],
+                native_t1w=False,
+            ),
+            ("f9057kp", "wave1bas", "Rest", "AP", "1"),
+            "MNI152NLin6Asym",
+        ],
+    ],
+)
+@pytest.mark.skipif(
+    _check_ants() is False, reason="requires ANTs to be in PATH"
+)
+@pytest.mark.skipif(
+    socket.gethostname() != "juseless",
+    reason="only for juseless",
+)
+def test_BOLDWarper_preprocess_to_multi_mni(
+    datagrabber: "BaseDataGrabber", element: Tuple[str, ...], space: str
+) -> None:
+    """Test BOLDWarper preprocess.
+
+    Parameters
+    ----------
+    datagrabber : DataGrabber-like object
+        The parametrized DataGrabber objects.
+    element : tuple of str
+        The parametrized elements.
+    space : str
+        The parametrized template space to transform to.
+
+    """
+    with datagrabber as dg:
+        # Read data
+        element_data = DefaultDataReader().fit_transform(dg[element])
+        pre_xfm_data = element_data["BOLD"]["data"].get_fdata().copy()
+        # Preprocess data
+        data_type, data = BOLDWarper(reference=space).preprocess(
+            input=element_data["BOLD"],
+            extra_input=element_data,
+        )
+        assert data_type == "BOLD"
+        assert isinstance(data, dict)
+        assert data["space"] == space
+        with assert_raises(AssertionError):
+            assert_array_equal(pre_xfm_data, data["data"])