diff --git a/modules/load_medical_images.ipynb b/modules/load_medical_images.ipynb
index 07e441ea79..74ea4f29a3 100644
--- a/modules/load_medical_images.ipynb
+++ b/modules/load_medical_images.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# Load medical images\n",
     "\n",
-    "This notebook introduces how to easily load different formats of medical images in MONAI and execute many additional operations.\n",
+    "This notebook describes how to easily load different formats of medical images in MONAI and execute additional image transforms.\n",
     "\n",
     "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/master/modules/load_medical_images.ipynb)"
    ]
@@ -59,25 +59,24 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "MONAI version: 0.6.0rc1+23.gc6793fd0\n",
-      "Numpy version: 1.20.3\n",
-      "Pytorch version: 1.9.0a0+c3d40fd\n",
-      "MONAI flags: HAS_EXT = True, USE_COMPILED = False\n",
-      "MONAI rev id: c6793fd0f316a448778d0047664aaf8c1895fe1c\n",
+      "MONAI version: 0.6.0+22.g027947bf.dirty\n",
+      "Numpy version: 1.21.0\n",
+      "Pytorch version: 1.9.0\n",
+      "MONAI flags: HAS_EXT = False, USE_COMPILED = False\n",
+      "MONAI rev id: 027947bf91ff0dfac94f472ed1855cd49e3feb8d\n",
       "\n",
       "Optional dependencies:\n",
       "Pytorch Ignite version: 0.4.5\n",
       "Nibabel version: 3.2.1\n",
-      "scikit-image version: 0.15.0\n",
-      "Pillow version: 7.0.0\n",
-      "Tensorboard version: 2.5.0\n",
+      "scikit-image version: 0.18.2\n",
+      "Pillow version: 8.2.0\n",
+      "Tensorboard version: 2.4.1\n",
       "gdown version: 3.13.0\n",
-      "TorchVision version: 0.10.0a0\n",
-      "ITK version: 5.1.2\n",
-      "tqdm version: 4.53.0\n",
+      "TorchVision version: 0.10.0\n",
+      "tqdm version: 4.61.1\n",
       "lmdb version: 1.2.1\n",
       "psutil version: 5.8.0\n",
-      "pandas version: 1.1.4\n",
+      "pandas version: 1.1.5\n",
       "einops version: 0.3.0\n",
       "\n",
       "For details about installing the optional dependencies, please visit:\n",
@@ -117,7 +116,7 @@
     "import tempfile\n",
     "from monai.data import ITKReader, PILReader\n",
     "from monai.transforms import (\n",
-    "    LoadImage, LoadImaged, AddChanneld,\n",
+    "    LoadImage, LoadImaged, EnsureChannelFirstd,\n",
     "    Resized, EnsureTyped, Compose\n",
     ")\n",
     "from monai.config import print_config\n",
@@ -137,9 +136,37 @@
     "  (npz, npy -> NumpyReader), (others -> ITKReader)."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate an example image"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# generate 3D test images\n",
+    "tempdir = tempfile.mkdtemp()\n",
+    "test_image = np.random.rand(64, 128, 96)\n",
+    "filename = os.path.join(tempdir, \"test_image.nii.gz\")\n",
+    "itk_np_view = itk.image_view_from_array(test_image)\n",
+    "itk.imwrite(itk_np_view, filename)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load the image file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2021-01-14T12:34:07.530773Z",
@@ -155,25 +182,18 @@
      "output_type": "stream",
      "text": [
       "image data shape:(96, 128, 64)\n",
-      "meta data:{'sizeof_hdr': array(348, dtype=int32), 'extents': array(0, dtype=int32), 'session_error': array(0, dtype=int16), 'dim_info': array(0, dtype=uint8), 'dim': array([  3,  96, 128,  64,   1,   1,   1,   1], dtype=int16), 'intent_p1': array(0., dtype=float32), 'intent_p2': array(0., dtype=float32), 'intent_p3': array(0., dtype=float32), 'intent_code': array(0, dtype=int16), 'datatype': array(64, dtype=int16), 'bitpix': array(64, dtype=int16), 'slice_start': array(0, dtype=int16), 'pixdim': array([1., 1., 1., 1., 0., 0., 0., 0.], dtype=float32), 'vox_offset': array(0., dtype=float32), 'scl_slope': array(nan, dtype=float32), 'scl_inter': array(nan, dtype=float32), 'slice_end': array(0, dtype=int16), 'slice_code': array(0, dtype=uint8), 'xyzt_units': array(2, dtype=uint8), 'cal_max': array(0., dtype=float32), 'cal_min': array(0., dtype=float32), 'slice_duration': array(0., dtype=float32), 'toffset': array(0., dtype=float32), 'glmax': array(0, dtype=int32), 'glmin': array(0, dtype=int32), 'qform_code': array(1, dtype=int16), 'sform_code': array(0, dtype=int16), 'quatern_b': array(0., dtype=float32), 'quatern_c': array(0., dtype=float32), 'quatern_d': array(1., dtype=float32), 'qoffset_x': array(-0., dtype=float32), 'qoffset_y': array(-0., dtype=float32), 'qoffset_z': array(0., dtype=float32), 'srow_x': array([0., 0., 0., 0.], dtype=float32), 'srow_y': array([0., 0., 0., 0.], dtype=float32), 'srow_z': array([0., 0., 0., 0.], dtype=float32), 'affine': array([[-1.,  0.,  0., -0.],\n",
+      "meta data:{'sizeof_hdr': array(348, dtype=int32), 'extents': array(0, dtype=int32), 'session_error': array(0, dtype=int16), 'dim_info': array(0, dtype=uint8), 'dim': array([  3,  96, 128,  64,   1,   1,   1,   1], dtype=int16), 'intent_p1': array(0., dtype=float32), 'intent_p2': array(0., dtype=float32), 'intent_p3': array(0., dtype=float32), 'intent_code': array(0, dtype=int16), 'datatype': array(64, dtype=int16), 'bitpix': array(64, dtype=int16), 'slice_start': array(0, dtype=int16), 'pixdim': array([1., 1., 1., 1., 0., 0., 0., 0.], dtype=float32), 'vox_offset': array(0., dtype=float32), 'scl_slope': array(nan, dtype=float32), 'scl_inter': array(nan, dtype=float32), 'slice_end': array(0, dtype=int16), 'slice_code': array(0, dtype=uint8), 'xyzt_units': array(2, dtype=uint8), 'cal_max': array(0., dtype=float32), 'cal_min': array(0., dtype=float32), 'slice_duration': array(0., dtype=float32), 'toffset': array(0., dtype=float32), 'glmax': array(0, dtype=int32), 'glmin': array(0, dtype=int32), 'qform_code': array(1, dtype=int16), 'sform_code': array(1, dtype=int16), 'quatern_b': array(0., dtype=float32), 'quatern_c': array(0., dtype=float32), 'quatern_d': array(1., dtype=float32), 'qoffset_x': array(-0., dtype=float32), 'qoffset_y': array(-0., dtype=float32), 'qoffset_z': array(0., dtype=float32), 'srow_x': array([-1.,  0.,  0., -0.], dtype=float32), 'srow_y': array([ 0., -1.,  0., -0.], dtype=float32), 'srow_z': array([0., 0., 1., 0.], dtype=float32), 'affine': array([[-1.,  0.,  0., -0.],\n",
       "       [ 0., -1.,  0., -0.],\n",
       "       [ 0.,  0.,  1.,  0.],\n",
       "       [ 0.,  0.,  0.,  1.]]), 'original_affine': array([[-1.,  0.,  0., -0.],\n",
       "       [ 0., -1.,  0., -0.],\n",
       "       [ 0.,  0.,  1.,  0.],\n",
-      "       [ 0.,  0.,  0.,  1.]]), 'as_closest_canonical': False, 'spatial_shape': array([ 96, 128,  64], dtype=int16), 'filename_or_obj': '/tmp/tmpg9t0nni3/test_image.nii.gz'}\n"
+      "       [ 0.,  0.,  0.,  1.]]), 'as_closest_canonical': False, 'spatial_shape': array([ 96, 128,  64], dtype=int16), 'original_channel_dim': 'no_channel', 'filename_or_obj': '/var/folders/6f/fdkl7m0x7sz3nj_t7p3ccgz00000gp/T/tmpq2arvyex/test_image.nii.gz'}\n"
      ]
     }
    ],
    "source": [
-    "# generate 3D test images\n",
-    "tempdir = tempfile.mkdtemp()\n",
-    "test_image = np.random.rand(64, 128, 96)\n",
-    "filename = os.path.join(tempdir, \"test_image.nii.gz\")\n",
-    "itk_np_view = itk.image_view_from_array(test_image)\n",
-    "itk.imwrite(itk_np_view, filename)\n",
     "data, meta = LoadImage()(filename)\n",
-    "\n",
     "print(f\"image data shape:{data.shape}\")\n",
     "print(f\"meta data:{meta}\")"
    ]
@@ -182,15 +202,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Load a list of Nifti images and stack as 1 training item\n",
+    "## Load a list of Nifti images and stack them as a multi-channel image\n",
     "Loading a list of files, stack them together and add a new dimension as first dimension.\n",
     "\n",
     "And use the meta data of the first image to represent the stacked result."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate some image examples"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2021-01-14T12:34:09.002685Z",
@@ -199,28 +226,44 @@
      "shell.execute_reply": "2021-01-14T12:34:09.184551Z"
     }
    },
+   "outputs": [],
+   "source": [
+    "filenames = [\"test_image.nii.gz\", \"test_image2.nii.gz\", \"test_image3.nii.gz\"]\n",
+    "for i, name in enumerate(filenames):\n",
+    "    filenames[i] = os.path.join(tempdir, name)\n",
+    "    itk_np_view = itk.image_view_from_array(test_image)\n",
+    "    itk.imwrite(itk_np_view, filenames[i])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load the images\n",
+    "Note that the output data shape is (3, 96, 128, 64), indicating a three-channel image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "image data shape:(3, 96, 128, 64)\n",
-      "meta data:{'sizeof_hdr': array(348, dtype=int32), 'extents': array(0, dtype=int32), 'session_error': array(0, dtype=int16), 'dim_info': array(0, dtype=uint8), 'dim': array([  3,  96, 128,  64,   1,   1,   1,   1], dtype=int16), 'intent_p1': array(0., dtype=float32), 'intent_p2': array(0., dtype=float32), 'intent_p3': array(0., dtype=float32), 'intent_code': array(0, dtype=int16), 'datatype': array(64, dtype=int16), 'bitpix': array(64, dtype=int16), 'slice_start': array(0, dtype=int16), 'pixdim': array([1., 1., 1., 1., 0., 0., 0., 0.], dtype=float32), 'vox_offset': array(0., dtype=float32), 'scl_slope': array(nan, dtype=float32), 'scl_inter': array(nan, dtype=float32), 'slice_end': array(0, dtype=int16), 'slice_code': array(0, dtype=uint8), 'xyzt_units': array(2, dtype=uint8), 'cal_max': array(0., dtype=float32), 'cal_min': array(0., dtype=float32), 'slice_duration': array(0., dtype=float32), 'toffset': array(0., dtype=float32), 'glmax': array(0, dtype=int32), 'glmin': array(0, dtype=int32), 'qform_code': array(1, dtype=int16), 'sform_code': array(0, dtype=int16), 'quatern_b': array(0., dtype=float32), 'quatern_c': array(0., dtype=float32), 'quatern_d': array(1., dtype=float32), 'qoffset_x': array(-0., dtype=float32), 'qoffset_y': array(-0., dtype=float32), 'qoffset_z': array(0., dtype=float32), 'srow_x': array([0., 0., 0., 0.], dtype=float32), 'srow_y': array([0., 0., 0., 0.], dtype=float32), 'srow_z': array([0., 0., 0., 0.], dtype=float32), 'affine': array([[-1.,  0.,  0., -0.],\n",
+      "meta data:{'sizeof_hdr': array(348, dtype=int32), 'extents': array(0, dtype=int32), 'session_error': array(0, dtype=int16), 'dim_info': array(0, dtype=uint8), 'dim': array([  3,  96, 128,  64,   1,   1,   1,   1], dtype=int16), 'intent_p1': array(0., dtype=float32), 'intent_p2': array(0., dtype=float32), 'intent_p3': array(0., dtype=float32), 'intent_code': array(0, dtype=int16), 'datatype': array(64, dtype=int16), 'bitpix': array(64, dtype=int16), 'slice_start': array(0, dtype=int16), 'pixdim': array([1., 1., 1., 1., 0., 0., 0., 0.], dtype=float32), 'vox_offset': array(0., dtype=float32), 'scl_slope': array(nan, dtype=float32), 'scl_inter': array(nan, dtype=float32), 'slice_end': array(0, dtype=int16), 'slice_code': array(0, dtype=uint8), 'xyzt_units': array(2, dtype=uint8), 'cal_max': array(0., dtype=float32), 'cal_min': array(0., dtype=float32), 'slice_duration': array(0., dtype=float32), 'toffset': array(0., dtype=float32), 'glmax': array(0, dtype=int32), 'glmin': array(0, dtype=int32), 'qform_code': array(1, dtype=int16), 'sform_code': array(1, dtype=int16), 'quatern_b': array(0., dtype=float32), 'quatern_c': array(0., dtype=float32), 'quatern_d': array(1., dtype=float32), 'qoffset_x': array(-0., dtype=float32), 'qoffset_y': array(-0., dtype=float32), 'qoffset_z': array(0., dtype=float32), 'srow_x': array([-1.,  0.,  0., -0.], dtype=float32), 'srow_y': array([ 0., -1.,  0., -0.], dtype=float32), 'srow_z': array([0., 0., 1., 0.], dtype=float32), 'affine': array([[-1.,  0.,  0., -0.],\n",
       "       [ 0., -1.,  0., -0.],\n",
       "       [ 0.,  0.,  1.,  0.],\n",
       "       [ 0.,  0.,  0.,  1.]]), 'original_affine': array([[-1.,  0.,  0., -0.],\n",
       "       [ 0., -1.,  0., -0.],\n",
       "       [ 0.,  0.,  1.,  0.],\n",
-      "       [ 0.,  0.,  0.,  1.]]), 'as_closest_canonical': False, 'spatial_shape': array([ 96, 128,  64], dtype=int16), 'filename_or_obj': '/tmp/tmpg9t0nni3/test_image.nii.gz'}\n"
+      "       [ 0.,  0.,  0.,  1.]]), 'as_closest_canonical': False, 'spatial_shape': array([ 96, 128,  64], dtype=int16), 'original_channel_dim': 0, 'filename_or_obj': '/var/folders/6f/fdkl7m0x7sz3nj_t7p3ccgz00000gp/T/tmpq2arvyex/test_image.nii.gz'}\n"
      ]
     }
    ],
    "source": [
-    "filenames = [\"test_image.nii.gz\", \"test_image2.nii.gz\", \"test_image3.nii.gz\"]\n",
-    "for i, name in enumerate(filenames):\n",
-    "    filenames[i] = os.path.join(tempdir, name)\n",
-    "    itk_np_view = itk.image_view_from_array(test_image)\n",
-    "    itk.imwrite(itk_np_view, filenames[i])\n",
     "data, meta = LoadImage()(filenames)\n",
     "\n",
     "print(f\"image data shape:{data.shape}\")\n",
@@ -234,9 +277,16 @@
     "## Load 3D image in DICOM format"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate an example image"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2021-01-14T12:34:09.188846Z",
@@ -245,29 +295,42 @@
      "shell.execute_reply": "2021-01-14T12:34:09.207174Z"
     }
    },
+   "outputs": [],
+   "source": [
+    "filename = os.path.join(tempdir, \"test_image.dcm\")\n",
+    "dcm_image = np.random.randint(256, size=(64, 128, 96)).astype(np.uint8)\n",
+    "itk_np_view = itk.image_view_from_array(dcm_image)\n",
+    "itk.imwrite(itk_np_view, filename)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load the image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "image data shape:(64, 128, 96)\n",
-      "meta data:{'0008|0016': '1.2.840.10008.5.1.4.1.1.7.2', '0008|0018': '1.2.826.0.1.3680043.2.1125.1.93614138108589045284680644628967496', '0008|0020': '20210114', '0008|0030': '123409.192726 ', '0008|0050': '', '0008|0060': 'OT', '0008|0090': '', '0010|0010': '', '0010|0020': '', '0010|0030': '', '0010|0040': '', '0020|000d': '1.2.826.0.1.3680043.2.1125.1.40125112463473974090659366113899120', '0020|000e': '1.2.826.0.1.3680043.2.1125.1.71875037911948695672421161054289584', '0020|0010': '', '0020|0011': '', '0020|0013': '', '0020|0052': '1.2.826.0.1.3680043.2.1125.1.71107643587930733955960565431144211', '0028|0002': '1', '0028|0004': 'MONOCHROME2 ', '0028|0008': '64', '0028|0009': '(5200,9230)', '0028|0010': '128', '0028|0011': '96', '0028|0100': '8', '0028|0101': '8', '0028|0102': '7', '0028|0103': '0', '0028|1052': '0 ', '0028|1053': '1 ', '0028|1054': 'US', 'origin': array([0., 0., 0.]), 'spacing': array([1., 1., 1.]), 'direction': array([[1., 0., 0.],\n",
-      "       [0., 1., 0.],\n",
-      "       [0., 0., 1.]]), 'original_affine': array([[1., 0., 0., 0.],\n",
-      "       [0., 1., 0., 0.],\n",
-      "       [0., 0., 1., 0.],\n",
-      "       [0., 0., 0., 1.]]), 'affine': array([[1., 0., 0., 0.],\n",
-      "       [0., 1., 0., 0.],\n",
-      "       [0., 0., 1., 0.],\n",
-      "       [0., 0., 0., 1.]]), 'spatial_shape': array([ 64, 128,  96]), 'filename_or_obj': '/tmp/tmpg9t0nni3/test_image.dcm'}\n"
+      "image data shape:(96, 128, 64)\n",
+      "meta data:{'0008|0016': '1.2.840.10008.5.1.4.1.1.7.2', '0008|0018': '1.2.826.0.1.3680043.2.1125.1.49144329026051408197861589261117151', '0008|0020': '20210722', '0008|0030': '170636.794924 ', '0008|0050': '', '0008|0060': 'OT', '0008|0090': '', '0010|0010': '', '0010|0020': '', '0010|0030': '', '0010|0040': '', '0020|000d': '1.2.826.0.1.3680043.2.1125.1.55082693700512159103862591206822067', '0020|000e': '1.2.826.0.1.3680043.2.1125.1.88286845929415088271370520342732963', '0020|0010': '', '0020|0011': '', '0020|0013': '', '0020|0052': '1.2.826.0.1.3680043.2.1125.1.54872246537810654209027218359324264', '0028|0002': '1', '0028|0004': 'MONOCHROME2 ', '0028|0008': '64', '0028|0009': '(5200,9230)', '0028|0010': '128', '0028|0011': '96', '0028|0100': '8', '0028|0101': '8', '0028|0102': '7', '0028|0103': '0', '0028|1052': '0 ', '0028|1053': '1 ', '0028|1054': 'US', 'spacing': array([1., 1., 1.]), 'original_affine': array([[-1.,  0.,  0.,  0.],\n",
+      "       [ 0., -1.,  0.,  0.],\n",
+      "       [ 0.,  0.,  1.,  0.],\n",
+      "       [ 0.,  0.,  0.,  1.]]), 'affine': array([[-1.,  0.,  0.,  0.],\n",
+      "       [ 0., -1.,  0.,  0.],\n",
+      "       [ 0.,  0.,  1.,  0.],\n",
+      "       [ 0.,  0.,  0.,  1.]]), 'spatial_shape': array([ 96, 128,  64]), 'original_channel_dim': 'no_channel', 'filename_or_obj': '/var/folders/6f/fdkl7m0x7sz3nj_t7p3ccgz00000gp/T/tmpq2arvyex/test_image.dcm'}\n"
      ]
     }
    ],
    "source": [
-    "filename = os.path.join(tempdir, \"test_image.dcm\")\n",
-    "dcm_image = np.random.randint(256, size=(64, 128, 96)).astype(np.uint8())\n",
-    "itk_np_view = itk.image_view_from_array(dcm_image)\n",
-    "itk.imwrite(itk_np_view, filename)\n",
     "data, meta = LoadImage()(filename)\n",
     "\n",
     "print(f\"image data shape:{data.shape}\")\n",
@@ -278,15 +341,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Load a list of DICOM images and stack as 1 training item\n",
+    "## Load a list of DICOM images and stack them as a multi-channel image\n",
     "Loading a list of files, stack them together and add a new dimension as first dimension.\n",
     "\n",
     "And use the meta data of the first image to represent the stacked result."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate some image examples"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2021-01-14T12:34:09.240561Z",
@@ -295,30 +365,44 @@
      "shell.execute_reply": "2021-01-14T12:34:09.249908Z"
     }
    },
+   "outputs": [],
+   "source": [
+    "filenames = [\"test_image-1.dcm\", \"test_image-2.dcm\", \"test_image-3.dcm\"]\n",
+    "for i, name in enumerate(filenames):\n",
+    "    filenames[i] = os.path.join(tempdir, name)\n",
+    "    dcm_image = np.random.randint(256, size=(64, 128, 96)).astype(np.uint8)\n",
+    "    itk_np_view = itk.image_view_from_array(dcm_image)\n",
+    "    itk.imwrite(itk_np_view, filenames[i])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load the images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "image data shape:(3, 64, 128, 96)\n",
-      "meta data:{'0008|0016': '1.2.840.10008.5.1.4.1.1.7.2', '0008|0018': '1.2.826.0.1.3680043.2.1125.1.68371057194748398879400050583354143', '0008|0020': '20210114', '0008|0030': '123409.211404 ', '0008|0050': '', '0008|0060': 'OT', '0008|0090': '', '0010|0010': '', '0010|0020': '', '0010|0030': '', '0010|0040': '', '0020|000d': '1.2.826.0.1.3680043.2.1125.1.94520649545863516453799141692905326', '0020|000e': '1.2.826.0.1.3680043.2.1125.1.38559700698094788931159255294312939', '0020|0010': '', '0020|0011': '', '0020|0013': '', '0020|0052': '1.2.826.0.1.3680043.2.1125.1.17178103647065544836893621101531571', '0028|0002': '1', '0028|0004': 'MONOCHROME2 ', '0028|0008': '64', '0028|0009': '(5200,9230)', '0028|0010': '128', '0028|0011': '96', '0028|0100': '8', '0028|0101': '8', '0028|0102': '7', '0028|0103': '0', '0028|1052': '0 ', '0028|1053': '1 ', '0028|1054': 'US', 'origin': array([0., 0., 0.]), 'spacing': array([1., 1., 1.]), 'direction': array([[1., 0., 0.],\n",
-      "       [0., 1., 0.],\n",
-      "       [0., 0., 1.]]), 'original_affine': array([[1., 0., 0., 0.],\n",
-      "       [0., 1., 0., 0.],\n",
-      "       [0., 0., 1., 0.],\n",
-      "       [0., 0., 0., 1.]]), 'affine': array([[1., 0., 0., 0.],\n",
-      "       [0., 1., 0., 0.],\n",
-      "       [0., 0., 1., 0.],\n",
-      "       [0., 0., 0., 1.]]), 'spatial_shape': array([ 64, 128,  96]), 'filename_or_obj': '/tmp/tmpg9t0nni3/test_image.dcm'}\n"
+      "image data shape:(3, 96, 128, 64)\n",
+      "meta data:{'0008|0016': '1.2.840.10008.5.1.4.1.1.7.2', '0008|0018': '1.2.826.0.1.3680043.2.1125.1.70459687821230247148357643462536357', '0008|0020': '20210722', '0008|0030': '170636.830318 ', '0008|0050': '', '0008|0060': 'OT', '0008|0090': '', '0010|0010': '', '0010|0020': '', '0010|0030': '', '0010|0040': '', '0020|000d': '1.2.826.0.1.3680043.2.1125.1.29026801319907695346385092515779097', '0020|000e': '1.2.826.0.1.3680043.2.1125.1.48166663359438709592920991059259631', '0020|0010': '', '0020|0011': '', '0020|0013': '', '0020|0052': '1.2.826.0.1.3680043.2.1125.1.52318595437965660783667062205792724', '0028|0002': '1', '0028|0004': 'MONOCHROME2 ', '0028|0008': '64', '0028|0009': '(5200,9230)', '0028|0010': '128', '0028|0011': '96', '0028|0100': '8', '0028|0101': '8', '0028|0102': '7', '0028|0103': '0', '0028|1052': '0 ', '0028|1053': '1 ', '0028|1054': 'US', 'spacing': array([1., 1., 1.]), 'original_affine': array([[-1.,  0.,  0.,  0.],\n",
+      "       [ 0., -1.,  0.,  0.],\n",
+      "       [ 0.,  0.,  1.,  0.],\n",
+      "       [ 0.,  0.,  0.,  1.]]), 'affine': array([[-1.,  0.,  0.,  0.],\n",
+      "       [ 0., -1.,  0.,  0.],\n",
+      "       [ 0.,  0.,  1.,  0.],\n",
+      "       [ 0.,  0.,  0.,  1.]]), 'spatial_shape': array([ 96, 128,  64]), 'original_channel_dim': 0, 'filename_or_obj': '/var/folders/6f/fdkl7m0x7sz3nj_t7p3ccgz00000gp/T/tmpq2arvyex/test_image-1.dcm'}\n"
      ]
     }
    ],
    "source": [
-    "filenames = [\"test_image.dcm\", \"test_image2.dcm\", \"test_image3.dcm\"]\n",
-    "for i, name in enumerate(filenames):\n",
-    "    filenames[i] = os.path.join(tempdir, name)\n",
-    "    itk_np_view = itk.image_view_from_array(dcm_image)\n",
-    "    itk.imwrite(itk_np_view, filenames[i])\n",
     "data, meta = LoadImage()(filenames)\n",
     "\n",
     "print(f\"image data shape:{data.shape}\")\n",
@@ -332,9 +416,16 @@
     "## Load 2D image in PNG format"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate an image"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2021-01-14T12:34:09.254871Z",
@@ -344,20 +435,35 @@
     },
     "tags": []
    },
+   "outputs": [],
+   "source": [
+    "test_image = np.random.randint(0, 256, size=[128, 256])\n",
+    "filename = os.path.join(tempdir, \"test_image.png\")\n",
+    "Image.fromarray(test_image.astype(\"uint8\")).save(filename)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load the image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "image data shape:(128, 256)\n",
-      "meta data:{'format': 'PNG', 'mode': 'L', 'width': 256, 'height': 128, 'spatial_shape': array([256, 128]), 'filename_or_obj': '/tmp/tmpg9t0nni3/test_image.png'}\n"
+      "image data shape:(256, 128)\n",
+      "meta data:{'format': 'PNG', 'mode': 'L', 'width': 256, 'height': 128, 'spatial_shape': array([256, 128]), 'original_channel_dim': 'no_channel', 'filename_or_obj': '/var/folders/6f/fdkl7m0x7sz3nj_t7p3ccgz00000gp/T/tmpq2arvyex/test_image.png'}\n"
      ]
     }
    ],
    "source": [
-    "test_image = np.random.randint(0, 256, size=[128, 256])\n",
-    "filename = os.path.join(tempdir, \"test_image.png\")\n",
-    "Image.fromarray(test_image.astype(\"uint8\")).save(filename)\n",
     "data, meta = LoadImage()(filename)\n",
     "\n",
     "print(f\"image data shape:{data.shape}\")\n",
@@ -374,7 +480,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 13,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2021-01-14T12:34:09.277760Z",
@@ -388,13 +494,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "image data shape:(128, 256)\n",
-      "meta data:{'origin': array([0., 0.]), 'spacing': array([1., 1.]), 'direction': array([[1., 0.],\n",
-      "       [0., 1.]]), 'original_affine': array([[1., 0., 0.],\n",
-      "       [0., 1., 0.],\n",
-      "       [0., 0., 1.]]), 'affine': array([[1., 0., 0.],\n",
-      "       [0., 1., 0.],\n",
-      "       [0., 0., 1.]]), 'spatial_shape': array([128, 256]), 'filename_or_obj': '/tmp/tmpg9t0nni3/test_image.png'}\n"
+      "image data shape:(256, 128)\n",
+      "meta data:{'spacing': array([1., 1.]), 'original_affine': array([[-1.,  0.,  0.],\n",
+      "       [ 0., -1.,  0.],\n",
+      "       [ 0.,  0.,  1.]]), 'affine': array([[-1.,  0.,  0.],\n",
+      "       [ 0., -1.,  0.],\n",
+      "       [ 0.,  0.,  1.]]), 'spatial_shape': array([256, 128]), 'original_channel_dim': 'no_channel', 'filename_or_obj': '/var/folders/6f/fdkl7m0x7sz3nj_t7p3ccgz00000gp/T/tmpq2arvyex/test_image.png'}\n"
      ]
     }
    ],
@@ -419,7 +524,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 14,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2021-01-14T12:34:09.285732Z",
@@ -433,8 +538,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "image data shape:(128, 256, 2)\n",
-      "meta data:{'format': None, 'mode': 'LA', 'width': 256, 'height': 128, 'spatial_shape': array([256, 128]), 'filename_or_obj': '/tmp/tmpg9t0nni3/test_image.png'}\n"
+      "image data shape:(256, 128, 2)\n",
+      "meta data:{'format': None, 'mode': 'LA', 'width': 256, 'height': 128, 'spatial_shape': array([256, 128]), 'original_channel_dim': -1, 'filename_or_obj': '/var/folders/6f/fdkl7m0x7sz3nj_t7p3ccgz00000gp/T/tmpq2arvyex/test_image.png'}\n"
      ]
     }
    ],
@@ -450,13 +555,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Connect `LoadImage` with other transforms\n",
+    "## Combine `LoadImage` with other transforms\n",
     "It's very easy to connect `LoadImage` transform with other transforms to construct a transform chain."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 15,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2021-01-14T12:34:09.298283Z",
@@ -464,6 +569,7 @@
      "iopub.status.idle": "2021-01-14T12:34:09.389726Z",
      "shell.execute_reply": "2021-01-14T12:34:09.390100Z"
     },
+    "scrolled": true,
     "tags": []
    },
    "outputs": [
@@ -472,14 +578,14 @@
      "output_type": "stream",
      "text": [
       "image data shape:torch.Size([1, 64, 64])\n",
-      "meta data:{'format': 'PNG', 'mode': 'L', 'width': 256, 'height': 128, 'spatial_shape': array([256, 128]), 'filename_or_obj': '/tmp/tmpg9t0nni3/test_image.png'}\n"
+      "meta data:{'format': 'PNG', 'mode': 'L', 'width': 256, 'height': 128, 'spatial_shape': array([256, 128]), 'original_channel_dim': 'no_channel', 'filename_or_obj': '/var/folders/6f/fdkl7m0x7sz3nj_t7p3ccgz00000gp/T/tmpq2arvyex/test_image.png'}\n"
      ]
     }
    ],
    "source": [
     "transform = Compose([\n",
     "    LoadImaged(keys=\"image\"),\n",
-    "    AddChanneld(keys=\"image\"),\n",
+    "    EnsureChannelFirstd(keys=\"image\"),\n",
     "    Resized(keys=\"image\", spatial_size=[64, 64]),\n",
     "    EnsureTyped(\"image\"),\n",
     "])\n",
@@ -500,7 +606,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 16,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2021-01-14T12:34:09.394901Z",
@@ -531,7 +637,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.7.10"
   }
  },
  "nbformat": 4,