Repository: Project-MONAI/MONAI

Issue:
loading multiple modalities from separate files and combining the data arrays
**Is your feature request related to a problem? Please describe.**
currently loading multiple files are not supported by` LoadNifti` transform

**Describe the solution you'd like**
to load multiple image files and combine the data arrays, if meta data exist, could consider merging the meta data


Discussion:

Resolving patch:
diff --git a/monai/transforms/composables.py b/monai/transforms/composables.py
--- a/monai/transforms/composables.py
+++ b/monai/transforms/composables.py
@@ -115,10 +115,15 @@ def __call__(self, data):
 @alias('LoadNiftiD', 'LoadNiftiDict')
 class LoadNiftid(MapTransform):
     """
-    dictionary-based wrapper of LoadNifti, must load image and metadata together.
+    Dictionary-based wrapper of LoadNifti, must load image and metadata
+    together. If loading a list of files in one key, stack them together and
+    add a new dimension as the first dimension, and use the meta data of the
+    first image to represent the stacked result. Note that the affine transform
+    of all the stacked images should be same.
     """
 
-    def __init__(self, keys, as_closest_canonical=False, dtype=np.float32, meta_key_format='{}.{}', overwriting_keys=False):
+    def __init__(self, keys, as_closest_canonical=False, dtype=np.float32,
+                 meta_key_format='{}.{}', overwriting_keys=False):
         """
         Args:
             keys (hashable items): keys of the corresponding items to be transformed.
@@ -794,6 +799,7 @@ class RandRotated(Randomizable, MapTransform):
         cval (scalar): Value to fill outside boundary. Default: 0.
         prefiter (bool): Apply spline_filter before interpolation. Default: True.
     """
+
     def __init__(self, keys, degrees, prob=0.1, spatial_axes=(0, 1), reshape=True, order=1,
                  mode='constant', cval=0, prefilter=True):
         MapTransform.__init__(self, keys)
diff --git a/monai/transforms/transforms.py b/monai/transforms/transforms.py
--- a/monai/transforms/transforms.py
+++ b/monai/transforms/transforms.py
@@ -139,7 +139,10 @@ def __call__(self, data_array, original_affine=None, original_axcodes=None):
 @export
 class LoadNifti:
     """
-    Load Nifti format file from provided path.
+    Load Nifti format file or files from provided path. If loading a list of
+    files, stack them together and add a new dimension as first dimension, and
+    use the meta data of the first image to represent the stacked result. Note
+    that the affine transform of all the images should be same if ``image_only=False``.
     """
 
     def __init__(self, as_closest_canonical=False, image_only=False, dtype=np.float32):
@@ -162,34 +165,45 @@ def __init__(self, as_closest_canonical=False, image_only=False, dtype=np.float3
     def __call__(self, filename):
         """
         Args:
-            filename (str or file): path to file or file-like object.
+            filename (str, list, tuple, file): path file or file-like object or a list of files.
         """
-        img = nib.load(filename)
-        img = correct_nifti_header_if_necessary(img)
+        filename = ensure_tuple(filename)
+        img_array = list()
+        compatible_meta = dict()
+        for name in filename:
+            img = nib.load(name)
+            img = correct_nifti_header_if_necessary(img)
+            header = dict(img.header)
+            header['filename_or_obj'] = name
+            header['original_affine'] = img.affine
+            header['affine'] = img.affine
+            header['as_closest_canonical'] = self.as_closest_canonical
 
-        header = dict(img.header)
-        header['filename_or_obj'] = filename
-        header['original_affine'] = img.affine
-        header['affine'] = img.affine
-        header['as_closest_canonical'] = self.as_closest_canonical
+            if self.as_closest_canonical:
+                img = nib.as_closest_canonical(img)
+                header['affine'] = img.affine
 
-        if self.as_closest_canonical:
-            img = nib.as_closest_canonical(img)
-            header['affine'] = img.affine
+            img_array.append(np.array(img.get_fdata(dtype=self.dtype)))
+            img.uncache()
+
+            if self.image_only:
+                continue
 
-        data = np.array(img.get_fdata(dtype=self.dtype))
-        img.uncache()
+            if not compatible_meta:
+                for meta_key in header:
+                    meta_datum = header[meta_key]
+                    if type(meta_datum).__name__ == 'ndarray' \
+                            and np_str_obj_array_pattern.search(meta_datum.dtype.str) is not None:
+                        continue
+                    compatible_meta[meta_key] = meta_datum
+            else:
+                assert np.allclose(header['affine'], compatible_meta['affine']), \
+                    'affine data of all images should be same.'
 
+        img_array = np.stack(img_array, axis=0) if len(img_array) > 1 else img_array[0]
         if self.image_only:
-            return data
-        compatible_meta = dict()
-        for meta_key in header:
-            meta_datum = header[meta_key]
-            if type(meta_datum).__name__ == 'ndarray' \
-                    and np_str_obj_array_pattern.search(meta_datum.dtype.str) is not None:
-                continue
-            compatible_meta[meta_key] = meta_datum
-        return data, compatible_meta
+            return img_array
+        return img_array, compatible_meta
 
 
 @export


Test patch:
diff --git a/tests/test_load_nifti.py b/tests/test_load_nifti.py
--- a/tests/test_load_nifti.py
+++ b/tests/test_load_nifti.py
@@ -11,40 +11,60 @@
 
 import unittest
 import os
-import shutil
 import numpy as np
 import tempfile
 import nibabel as nib
 from parameterized import parameterized
 from monai.transforms.transforms import LoadNifti
 
-TEST_CASE_IMAGE_ONLY = [
+TEST_CASE_1 = [
     {
         'as_closest_canonical': False,
         'image_only': True
     },
+    ['test_image.nii.gz'],
     (128, 128, 128)
 ]
 
-TEST_CASE_IMAGE_METADATA = [
+TEST_CASE_2 = [
     {
         'as_closest_canonical': False,
         'image_only': False
     },
+    ['test_image.nii.gz'],
     (128, 128, 128)
 ]
 
+TEST_CASE_3 = [
+    {
+        'as_closest_canonical': False,
+        'image_only': True
+    },
+    ['test_image1.nii.gz', 'test_image2.nii.gz', 'test_image3.nii.gz'],
+    (3, 128, 128, 128)
+]
+
+TEST_CASE_4 = [
+    {
+        'as_closest_canonical': False,
+        'image_only': False
+    },
+    ['test_image1.nii.gz', 'test_image2.nii.gz', 'test_image3.nii.gz'],
+    (3, 128, 128, 128)
+]
+
 
 class TestLoadNifti(unittest.TestCase):
 
-    @parameterized.expand([TEST_CASE_IMAGE_ONLY, TEST_CASE_IMAGE_METADATA])
-    def test_shape(self, input_param, expected_shape):
+    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4])
+    def test_shape(self, input_param, filenames, expected_shape):
         test_image = np.random.randint(0, 2, size=[128, 128, 128])
         tempdir = tempfile.mkdtemp()
-        nib.save(nib.Nifti1Image(test_image, np.eye(4)), os.path.join(tempdir, 'test_image.nii.gz'))
-        test_data = os.path.join(tempdir, 'test_image.nii.gz')
-        result = LoadNifti(**input_param)(test_data)
-        shutil.rmtree(tempdir)
+        with tempfile.TemporaryDirectory() as tempdir:
+            for i, name in enumerate(filenames):
+                filenames[i] = os.path.join(tempdir, name)
+                nib.save(nib.Nifti1Image(test_image, np.eye(4)), filenames[i])
+            result = LoadNifti(**input_param)(filenames)
         if isinstance(result, tuple):
             result = result[0]
         self.assertTupleEqual(result.shape, expected_shape)
diff --git a/tests/test_load_niftid.py b/tests/test_load_niftid.py
--- a/tests/test_load_niftid.py
+++ b/tests/test_load_niftid.py
@@ -11,7 +11,6 @@
 
 import unittest
 import os
-import shutil
 import numpy as np
 import tempfile
 import nibabel as nib
@@ -36,11 +35,11 @@ def test_shape(self, input_param, expected_shape):
         test_image = nib.Nifti1Image(np.random.randint(0, 2, size=[128, 128, 128]), np.eye(4))
         tempdir = tempfile.mkdtemp()
         test_data = dict()
-        for key in KEYS:
-            nib.save(test_image, os.path.join(tempdir, key + '.nii.gz'))
-            test_data.update({key: os.path.join(tempdir, key + '.nii.gz')})
-        result = LoadNiftid(**input_param)(test_data)
-        shutil.rmtree(tempdir)
+        with tempfile.TemporaryDirectory() as tempdir:
+            for key in KEYS:
+                nib.save(test_image, os.path.join(tempdir, key + '.nii.gz'))
+                test_data.update({key: os.path.join(tempdir, key + '.nii.gz')})
+            result = LoadNiftid(**input_param)(test_data)
         for key in KEYS:
             self.assertTupleEqual(result[key].shape, expected_shape)