[ENH] Add emc-related helper functions - images

dmriprep/interfaces/images.py

@@ -1,10 +1,9 @@
 """Image tools interfaces."""
-import numpy as np
-import nibabel as nb
-from nipype.utils.filemanip import fname_presuffix
+from dmriprep.utils.images import rescale_b0, median, match_transforms, extract_b0
 from nipype import logging
 from nipype.interfaces.base import (
-    traits, TraitedSpec, BaseInterfaceInputSpec, SimpleInterface, File
+    traits, TraitedSpec, BaseInterfaceInputSpec, SimpleInterface, File,
+    InputMultiObject, OutputMultiObject
 )
 
 LOGGER = logging.getLogger('nipype.interface')
@@ -45,24 +44,6 @@ def _run_interface(self, runtime):
         return runtime
 
 
-def extract_b0(in_file, b0_ixs, newpath=None):
-    """Extract the *b0* volumes from a DWI dataset."""
-    out_file = fname_presuffix(
-        in_file, suffix='_b0', newpath=newpath)
-
-    img = nb.load(in_file)
-    data = img.get_fdata(dtype='float32')
-
-    b0 = data[..., b0_ixs]
-
-    hdr = img.header.copy()
-    hdr.set_data_shape(b0.shape)
-    hdr.set_xyzt_units('mm')
-    hdr.set_data_dtype(np.float32)
-    nb.Nifti1Image(b0, img.affine, hdr).to_filename(out_file)
-    return out_file
-
-
 class _RescaleB0InputSpec(BaseInterfaceInputSpec):
     in_file = File(exists=True, mandatory=True, desc='b0s file')
     mask_file = File(exists=True, mandatory=True, desc='mask file')
@@ -103,43 +84,25 @@ def _run_interface(self, runtime):
         return runtime
 
 
-def rescale_b0(in_file, mask_file, newpath=None):
-    """Rescale the input volumes using the median signal intensity."""
-    out_file = fname_presuffix(
-        in_file, suffix='_rescaled_b0', newpath=newpath)
-
-    img = nb.load(in_file)
-    if img.dataobj.ndim == 3:
-        return in_file
-
-    data = img.get_fdata(dtype='float32')
-    mask_img = nb.load(mask_file)
-    mask_data = mask_img.get_fdata(dtype='float32')
+class _MatchTransformsInputSpec(BaseInterfaceInputSpec):
+    b0_indices = traits.List(mandatory=True)
+    dwi_files = InputMultiObject(File(exists=True), mandatory=True)
+    transforms = InputMultiObject(File(exists=True), mandatory=True)
 
-    median_signal = np.median(data[mask_data > 0, ...], axis=0)
-    rescaled_data = 1000 * data / median_signal
-    hdr = img.header.copy()
-    nb.Nifti1Image(rescaled_data, img.affine, hdr).to_filename(out_file)
-    return out_file
 
+class _MatchTransformsOutputSpec(TraitedSpec):
+    transforms = OutputMultiObject(File(exists=True), mandatory=True)
 
-def median(in_file, newpath=None):
-    """Average a 4D dataset across the last dimension using median."""
-    out_file = fname_presuffix(
-        in_file, suffix='_b0ref', newpath=newpath)
 
-    img = nb.load(in_file)
-    if img.dataobj.ndim == 3:
-        return in_file
-    if img.shape[-1] == 1:
-        nb.squeeze_image(img).to_filename(out_file)
-        return out_file
-
-    median_data = np.median(img.get_fdata(dtype='float32'),
-                            axis=-1)
+class MatchTransforms(SimpleInterface):
+    """
+    Interface for mapping the `match_transforms` function across lists of inputs.
+    """
+    input_spec = MatchTransformsInputSpec
+    output_spec = MatchTransformsOutputSpec
 
-    hdr = img.header.copy()
-    hdr.set_xyzt_units('mm')
-    hdr.set_data_dtype(np.float32)
-    nb.Nifti1Image(median_data, img.affine, hdr).to_filename(out_file)
-    return out_file
+    def _run_interface(self, runtime):
+        self._results["transforms"] = match_transforms(
+            self.inputs.dwi_files, self.inputs.transforms, self.inputs.b0_indices
+        )
+        return runtime

dmriprep/utils/images.py

@@ -0,0 +1,176 @@
+import numpy as np
+import nibabel as nb
+from nipype.utils.filemanip import fname_presuffix
+
+
+def extract_b0(in_file, b0_ixs, newpath=None):
+    """Extract the *b0* volumes from a DWI dataset."""
+    out_file = fname_presuffix(in_file, suffix="_b0", newpath=newpath)
+
+    img = nb.load(in_file)
+    data = img.get_fdata(dtype="float32")
+
+    b0 = data[..., b0_ixs]
+
+    hdr = img.header.copy()
+    hdr.set_data_shape(b0.shape)
+    hdr.set_xyzt_units("mm")
+    hdr.set_data_dtype(np.float32)
+    nb.Nifti1Image(b0, img.affine, hdr).to_filename(out_file)
+    return out_file
+
+
+def rescale_b0(in_file, mask_file, newpath=None):
+    """Rescale the input volumes using the median signal intensity."""
+    out_file = fname_presuffix(in_file, suffix="_rescaled_b0", newpath=newpath)
+
+    img = nb.load(in_file)
+    if img.dataobj.ndim == 3:
+        return in_file
+
+    data = img.get_fdata(dtype="float32")
+    mask_img = nb.load(mask_file)
+    mask_data = mask_img.get_fdata(dtype="float32")
+
+    median_signal = np.median(data[mask_data > 0, ...], axis=0)
+    rescaled_data = 1000 * data / median_signal
+    hdr = img.header.copy()
+    nb.Nifti1Image(rescaled_data, img.affine, hdr).to_filename(out_file)
+    return out_file
+
+
+def median(in_file, newpath=None):
+    """Average a 4D dataset across the last dimension using median."""
+    out_file = fname_presuffix(in_file, suffix="_b0ref", newpath=newpath)
+
+    img = nb.load(in_file)
+    if img.dataobj.ndim == 3:
+        return in_file
+    if img.shape[-1] == 1:
+        nb.squeeze_image(img).to_filename(out_file)
+        return out_file
+
+    median_data = np.median(img.get_fdata(), axis=-1)
+
+    hdr = img.header.copy()
+    hdr.set_xyzt_units("mm")
+    nb.Nifti1Image(median_data, img.affine, hdr).to_filename(out_file)
+    return out_file
+
+
+def average_images(images, out_path=None):
+    """Average a 4D dataset across the last dimension using mean."""
+    from nilearn.image import mean_img
+
+    average_img = mean_img([nb.load(img) for img in images])
+    if out_path is None:
+        out_path = fname_presuffix(
+            images[0], use_ext=False, suffix="_mean.nii.gz"
+        )
+    average_img.to_filename(out_path)
+    return out_path
+
+
+def quick_load_images(image_list, dtype=np.float32):
+    """Load 3D volumes from a list of file paths into a 4D array."""
+    example_img = nb.load(image_list[0])
+    num_images = len(image_list)
+    output_matrix = np.zeros(tuple(example_img.shape) + (num_images,), dtype=dtype)
+    for image_num, image_path in enumerate(image_list):
+        output_matrix[..., image_num] = nb.load(image_path).get_fdata(dtype=dtype)
+    return output_matrix
+
+
+def match_transforms(dwi_files, transforms, b0_indices):
+    """Arranges the order of a list of affine transforms to correspond with that of
+    each individual dwi volume file, accounting for the indices of B0s. A helper
+    function for EMC."""
+    num_dwis = len(dwi_files)
+    num_transforms = len(transforms)
+
+    if num_dwis == num_transforms:
+        return transforms
+
+    # Do sanity checks
+    if not len(transforms) == len(b0_indices):
+        raise Exception("number of transforms does not match number of b0 images")
+
+    # Create a list of which emc affines go with each of the split images
+    nearest_affines = []
+    for index in range(num_dwis):
+        nearest_b0_num = np.argmin(np.abs(index - np.array(b0_indices)))
+        this_transform = transforms[nearest_b0_num]
+        nearest_affines.append(this_transform)
+
+    return nearest_affines
+
+
+def save_4d_to_3d(in_file):
+    """Split a 4D dataset along the last dimension into multiple 3D volumes."""
+    files_3d = nb.four_to_three(nb.load(in_file))
+    out_files = []
+    for i, file_3d in enumerate(files_3d):
+        out_file = fname_presuffix(in_file, suffix="_tmp_{}".format(i))
+        file_3d.to_filename(out_file)
+        out_files.append(out_file)
+    del files_3d
+    return out_files
+
+
+def prune_b0s_from_dwis(in_files, b0_ixs):
+    """Remove *b0* volume files from a complete list of DWI volume files."""
+    if in_files[0].endswith("_warped.nii.gz"):
+        out_files = [
+            i
+            for j, i in enumerate(
+                sorted(
+                    in_files, key=lambda x: int(x.split("_")[-2].split(".nii.gz")[0])
+                )
+            )
+            if j not in b0_ixs
+        ]
+    else:
+        out_files = [
+            i
+            for j, i in enumerate(
+                sorted(
+                    in_files, key=lambda x: int(x.split("_")[-1].split(".nii.gz")[0])
+                )
+            )
+            if j not in b0_ixs
+        ]
+    return out_files
+
+
+def save_3d_to_4d(in_files):
+    """Concatenate a list of 3D volumes into a 4D output."""
+    img_4d = nb.funcs.concat_images([nb.load(img_3d) for img_3d in in_files])
+    out_file = fname_presuffix(in_files[0], suffix="_merged")
+    img_4d.to_filename(out_file)
+    return out_file
+
+
+def decompose_affine(affine):
+    """Takes an transformation affine matrix A and determines
+    rotations and translations."""
+
+    def rang(b):
+        a = min(max(b, -1), 1)
+        return a
+
+    Ry = np.arcsin(A[0, 2])
+    # Rx = np.arcsin(A[1, 2] / np.cos(Ry))
+    # Rz = np.arccos(A[0, 1] / np.sin(Ry))
+
+    if (abs(Ry) - np.pi / 2) ** 2 < 1e-9:
+        Rx = 0
+        Rz = np.arctan2(-rang(A[1, 0]), rang(-A[2, 0] / A[0, 2]))
+    else:
+        c = np.cos(Ry)
+        Rx = np.arctan2(rang(A[1, 2] / c), rang(A[2, 2] / c))
+        Rz = np.arctan2(rang(A[0, 1] / c), rang(A[0, 0] / c))
+
+    rotations = [Rx, Ry, Rz]
+    translations = [A[0, 3], A[1, 3], A[2, 3]]
+
+    return rotations, translations