diff --git a/2d_classification/mednist_tutorial.ipynb b/2d_classification/mednist_tutorial.ipynb
index 09224fddda..dd1556d8f9 100644
--- a/2d_classification/mednist_tutorial.ipynb
+++ b/2d_classification/mednist_tutorial.ipynb
@@ -118,6 +118,7 @@
     "    RandZoom,\n",
     "    ScaleIntensity,\n",
     "    EnsureType,\n",
+    "    ToNumpy,\n",
     ")\n",
     "from monai.utils import set_determinism\n",
     "\n",
@@ -361,7 +362,8 @@
    "source": [
     "train_transforms = Compose(\n",
     "    [\n",
-    "        LoadImage(image_only=True),\n",
+    "        LoadImage(),\n",
+    "        ToNumpy(),\n",
     "        AddChannel(),\n",
     "        ScaleIntensity(),\n",
     "        RandRotate(range_x=np.pi / 12, prob=0.5, keep_size=True),\n",
@@ -372,7 +374,7 @@
     ")\n",
     "\n",
     "val_transforms = Compose(\n",
-    "    [LoadImage(image_only=True), AddChannel(), ScaleIntensity(), EnsureType()])\n",
+    "    [LoadImage(), ToNumpy(), AddChannel(), ScaleIntensity(), EnsureType()])\n",
     "\n",
     "y_pred_trans = Compose([EnsureType(), Activations(softmax=True)])\n",
     "y_trans = Compose([EnsureType(), AsDiscrete(to_onehot=num_class)])"
diff --git a/2d_registration/registration_mednist.ipynb b/2d_registration/registration_mednist.ipynb
index f53a9e66a2..8a728970bf 100644
--- a/2d_registration/registration_mednist.ipynb
+++ b/2d_registration/registration_mednist.ipynb
@@ -103,6 +103,7 @@
     "from monai.transforms import (\n",
     "    EnsureChannelFirstD,\n",
     "    Compose,\n",
+    "    FromMetaTensord,\n",
     "    LoadImageD,\n",
     "    RandRotateD,\n",
     "    RandZoomD,\n",
@@ -232,6 +233,7 @@
     "    [\n",
     "        LoadImageD(keys=[\"fixed_hand\", \"moving_hand\"]),\n",
     "        EnsureChannelFirstD(keys=[\"fixed_hand\", \"moving_hand\"]),\n",
+    "        FromMetaTensord(keys=[\"fixed_hand\", \"moving_hand\"]),\n",
     "        ScaleIntensityRanged(keys=[\"fixed_hand\", \"moving_hand\"],\n",
     "                             a_min=0., a_max=255., b_min=0.0, b_max=1.0, clip=True,),\n",
     "        RandRotateD(keys=[\"moving_hand\"], range_x=np.pi/4, prob=1.0, keep_size=True, mode=\"bicubic\"),\n",
diff --git a/3d_segmentation/brats_segmentation_3d.ipynb b/3d_segmentation/brats_segmentation_3d.ipynb
index cde08c6cae..ec374b04f7 100644
--- a/3d_segmentation/brats_segmentation_3d.ipynb
+++ b/3d_segmentation/brats_segmentation_3d.ipynb
@@ -135,6 +135,7 @@
     "    AsDiscreted,\n",
     "    Compose,\n",
     "    Invertd,\n",
+    "    FromMetaTensord,\n",
     "    LoadImaged,\n",
     "    MapTransform,\n",
     "    NormalizeIntensityd,\n",
@@ -265,13 +266,14 @@
     "        # load 4 Nifti images and stack them together\n",
     "        LoadImaged(keys=[\"image\", \"label\"]),\n",
     "        EnsureChannelFirstd(keys=\"image\"),\n",
-    "        ConvertToMultiChannelBasedOnBratsClassesd(keys=\"label\"),\n",
     "        Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
     "        Spacingd(\n",
     "            keys=[\"image\", \"label\"],\n",
     "            pixdim=(1.0, 1.0, 1.0),\n",
     "            mode=(\"bilinear\", \"nearest\"),\n",
     "        ),\n",
+    "        FromMetaTensord(keys=[\"image\", \"label\"]),\n",
+    "        ConvertToMultiChannelBasedOnBratsClassesd(keys=\"label\"),\n",
     "        RandSpatialCropd(keys=[\"image\", \"label\"], roi_size=[224, 224, 144], random_size=False),\n",
     "        RandFlipd(keys=[\"image\", \"label\"], prob=0.5, spatial_axis=0),\n",
     "        RandFlipd(keys=[\"image\", \"label\"], prob=0.5, spatial_axis=1),\n",
@@ -286,13 +288,14 @@
     "    [\n",
     "        LoadImaged(keys=[\"image\", \"label\"]),\n",
     "        EnsureChannelFirstd(keys=\"image\"),\n",
-    "        ConvertToMultiChannelBasedOnBratsClassesd(keys=\"label\"),\n",
     "        Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
     "        Spacingd(\n",
     "            keys=[\"image\", \"label\"],\n",
     "            pixdim=(1.0, 1.0, 1.0),\n",
     "            mode=(\"bilinear\", \"nearest\"),\n",
     "        ),\n",
+    "        FromMetaTensord(keys=[\"image\", \"label\"]),\n",
+    "        ConvertToMultiChannelBasedOnBratsClassesd(keys=\"label\"),\n",
     "        NormalizeIntensityd(keys=\"image\", nonzero=True, channel_wise=True),\n",
     "        EnsureTyped(keys=[\"image\", \"label\"]),\n",
     "    ]\n",
@@ -784,9 +787,10 @@
     "    [\n",
     "        LoadImaged(keys=[\"image\", \"label\"]),\n",
     "        EnsureChannelFirstd(keys=[\"image\"]),\n",
-    "        ConvertToMultiChannelBasedOnBratsClassesd(keys=\"label\"),\n",
     "        Orientationd(keys=[\"image\"], axcodes=\"RAS\"),\n",
     "        Spacingd(keys=[\"image\"], pixdim=(1.0, 1.0, 1.0), mode=\"bilinear\"),\n",
+    "        FromMetaTensord(keys=[\"image\", \"label\"]),\n",
+    "        ConvertToMultiChannelBasedOnBratsClassesd(keys=\"label\"),\n",
     "        NormalizeIntensityd(keys=\"image\", nonzero=True, channel_wise=True),\n",
     "        EnsureTyped(keys=[\"image\", \"label\"]),\n",
     "    ]\n",
diff --git a/3d_segmentation/spleen_segmentation_3d.ipynb b/3d_segmentation/spleen_segmentation_3d.ipynb
index 6e3f5d46c3..8b1ece6a0d 100644
--- a/3d_segmentation/spleen_segmentation_3d.ipynb
+++ b/3d_segmentation/spleen_segmentation_3d.ipynb
@@ -63,6 +63,7 @@
     "    AsDiscrete,\n",
     "    AsDiscreted,\n",
     "    EnsureChannelFirstd,\n",
+    "    FromMetaTensord,\n",
     "    Compose,\n",
     "    CropForegroundd,\n",
     "    LoadImaged,\n",
@@ -283,6 +284,7 @@
     "        Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
     "        Spacingd(keys=[\"image\", \"label\"], pixdim=(\n",
     "            1.5, 1.5, 2.0), mode=(\"bilinear\", \"nearest\")),\n",
+    "        FromMetaTensord(keys=[\"image\", \"label\"]),\n",
     "        ScaleIntensityRanged(\n",
     "            keys=[\"image\"], a_min=-57, a_max=164,\n",
     "            b_min=0.0, b_max=1.0, clip=True,\n",
@@ -315,6 +317,7 @@
     "        Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
     "        Spacingd(keys=[\"image\", \"label\"], pixdim=(\n",
     "            1.5, 1.5, 2.0), mode=(\"bilinear\", \"nearest\")),\n",
+    "        FromMetaTensord(keys=[\"image\", \"label\"]),\n",
     "        ScaleIntensityRanged(\n",
     "            keys=[\"image\"], a_min=-57, a_max=164,\n",
     "            b_min=0.0, b_max=1.0, clip=True,\n",
@@ -696,6 +699,7 @@
     "        Orientationd(keys=[\"image\"], axcodes=\"RAS\"),\n",
     "        Spacingd(keys=[\"image\"], pixdim=(\n",
     "            1.5, 1.5, 2.0), mode=\"bilinear\"),\n",
+    "        FromMetaTensord(keys=[\"image\", \"label\"]),\n",
     "        ScaleIntensityRanged(\n",
     "            keys=[\"image\"], a_min=-57, a_max=164,\n",
     "            b_min=0.0, b_max=1.0, clip=True,\n",
@@ -790,6 +794,7 @@
     "        Orientationd(keys=[\"image\"], axcodes=\"RAS\"),\n",
     "        Spacingd(keys=[\"image\"], pixdim=(\n",
     "            1.5, 1.5, 2.0), mode=\"bilinear\"),\n",
+    "        FromMetaTensord(keys=\"image\"),\n",
     "        ScaleIntensityRanged(\n",
     "            keys=[\"image\"], a_min=-57, a_max=164,\n",
     "            b_min=0.0, b_max=1.0, clip=True,\n",
diff --git a/3d_segmentation/spleen_segmentation_3d_lightning.ipynb b/3d_segmentation/spleen_segmentation_3d_lightning.ipynb
index 491cc94745..7134738584 100644
--- a/3d_segmentation/spleen_segmentation_3d_lightning.ipynb
+++ b/3d_segmentation/spleen_segmentation_3d_lightning.ipynb
@@ -128,6 +128,7 @@
     "    AddChanneld,\n",
     "    Compose,\n",
     "    CropForegroundd,\n",
+    "    FromMetaTensord,\n",
     "    LoadImaged,\n",
     "    Orientationd,\n",
     "    RandCropByPosNegLabeld,\n",
@@ -276,6 +277,7 @@
     "                    pixdim=(1.5, 1.5, 2.0),\n",
     "                    mode=(\"bilinear\", \"nearest\"),\n",
     "                ),\n",
+    "                FromMetaTensord(keys=[\"image\", \"label\"]),\n",
     "                ScaleIntensityRanged(\n",
     "                    keys=[\"image\"], a_min=-57, a_max=164,\n",
     "                    b_min=0.0, b_max=1.0, clip=True,\n",
@@ -316,6 +318,7 @@
     "                    pixdim=(1.5, 1.5, 2.0),\n",
     "                    mode=(\"bilinear\", \"nearest\"),\n",
     "                ),\n",
+    "                FromMetaTensord(keys=[\"image\", \"label\"]),\n",
     "                ScaleIntensityRanged(\n",
     "                    keys=[\"image\"], a_min=-57, a_max=164,\n",
     "                    b_min=0.0, b_max=1.0, clip=True,\n",
diff --git a/3d_segmentation/unet_segmentation_3d_catalyst.ipynb b/3d_segmentation/unet_segmentation_3d_catalyst.ipynb
index 26eb763919..68b95343ca 100644
--- a/3d_segmentation/unet_segmentation_3d_catalyst.ipynb
+++ b/3d_segmentation/unet_segmentation_3d_catalyst.ipynb
@@ -137,6 +137,7 @@
     "    AsChannelFirstd,\n",
     "    AsDiscrete,\n",
     "    Compose,\n",
+    "    FromMetaTensord,\n",
     "    LoadImaged,\n",
     "    RandCropByPosNegLabeld,\n",
     "    RandRotate90d,\n",
@@ -293,6 +294,7 @@
     "    [\n",
     "        LoadImaged(keys=[\"img\", \"seg\"]),\n",
     "        AsChannelFirstd(keys=[\"img\", \"seg\"], channel_dim=-1),\n",
+    "        FromMetaTensord(keys=[\"img\", \"seg\"]),\n",
     "        ScaleIntensityd(keys=[\"img\", \"seg\"]),\n",
     "        RandCropByPosNegLabeld(\n",
     "            keys=[\"img\", \"seg\"],\n",
@@ -310,6 +312,7 @@
     "    [\n",
     "        LoadImaged(keys=[\"img\", \"seg\"]),\n",
     "        AsChannelFirstd(keys=[\"img\", \"seg\"], channel_dim=-1),\n",
+    "        FromMetaTensord(keys=[\"img\", \"seg\"]),\n",
     "        ScaleIntensityd(keys=[\"img\", \"seg\"]),\n",
     "        EnsureTyped(keys=[\"img\", \"seg\"]),\n",
     "    ]\n",
diff --git a/3d_segmentation/unet_segmentation_3d_ignite.ipynb b/3d_segmentation/unet_segmentation_3d_ignite.ipynb
index fa61045f8e..586de5ff4d 100644
--- a/3d_segmentation/unet_segmentation_3d_ignite.ipynb
+++ b/3d_segmentation/unet_segmentation_3d_ignite.ipynb
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {
     "tags": []
    },
@@ -84,6 +84,7 @@
     "    Resize,\n",
     "    ScaleIntensity,\n",
     "    EnsureType,\n",
+    "    Lambda,\n",
     ")\n",
     "from monai.utils import first\n",
     "\n",
@@ -185,7 +186,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "torch.Size([10, 1, 96, 96, 96]) torch.Size([10, 1, 96, 96, 96])\n"
+      "(10, 1, 96, 96, 96) (10, 1, 96, 96, 96)\n"
      ]
     }
    ],
@@ -193,17 +194,19 @@
     "# Define transforms for image and segmentation\n",
     "imtrans = Compose(\n",
     "    [\n",
-    "        LoadImage(image_only=True),\n",
-    "        ScaleIntensity(),\n",
+    "        LoadImage(),\n",
     "        AddChannel(),\n",
+    "        Lambda(lambda x: x.as_tensor()),\n",
+    "        ScaleIntensity(),\n",
     "        RandSpatialCrop((96, 96, 96), random_size=False),\n",
     "        EnsureType(),\n",
     "    ]\n",
     ")\n",
     "segtrans = Compose(\n",
     "    [\n",
-    "        LoadImage(image_only=True),\n",
+    "        LoadImage(),\n",
     "        AddChannel(),\n",
+    "        Lambda(lambda x: x.as_tensor()),\n",
     "        RandSpatialCrop((96, 96, 96), random_size=False),\n",
     "        EnsureType(),\n",
     "    ]\n",
@@ -350,17 +353,19 @@
     "# create a validation data loader\n",
     "val_imtrans = Compose(\n",
     "    [\n",
-    "        LoadImage(image_only=True),\n",
-    "        ScaleIntensity(),\n",
+    "        LoadImage(),\n",
     "        AddChannel(),\n",
+    "        Lambda(lambda x: x.as_tensor()),\n",
+    "        ScaleIntensity(),\n",
     "        Resize((96, 96, 96)),\n",
     "        EnsureType(),\n",
     "    ]\n",
     ")\n",
     "val_segtrans = Compose(\n",
     "    [\n",
-    "        LoadImage(image_only=True),\n",
+    "        LoadImage(),\n",
     "        AddChannel(),\n",
+    "        Lambda(lambda x: x.as_tensor()),\n",
     "        Resize((96, 96, 96)),\n",
     "        EnsureType(),\n",
     "    ]\n",
diff --git a/acceleration/automatic_mixed_precision.ipynb b/acceleration/automatic_mixed_precision.ipynb
index cd47c4051d..001ef29dea 100644
--- a/acceleration/automatic_mixed_precision.ipynb
+++ b/acceleration/automatic_mixed_precision.ipynb
@@ -122,6 +122,7 @@
     "    Compose,\n",
     "    CropForegroundd,\n",
     "    FgBgToIndicesd,\n",
+    "    FromMetaTensord,\n",
     "    LoadImaged,\n",
     "    Orientationd,\n",
     "    RandCropByPosNegLabeld,\n",
@@ -245,6 +246,7 @@
     "                pixdim=(1.5, 1.5, 2.0),\n",
     "                mode=(\"bilinear\", \"nearest\"),\n",
     "            ),\n",
+    "            FromMetaTensord(keys=[\"image\", \"label\"]),\n",
     "            ScaleIntensityRanged(\n",
     "                keys=[\"image\"],\n",
     "                a_min=-57,\n",
@@ -288,6 +290,7 @@
     "                pixdim=(1.5, 1.5, 2.0),\n",
     "                mode=(\"bilinear\", \"nearest\"),\n",
     "            ),\n",
+    "            FromMetaTensord(keys=[\"image\", \"label\"]),\n",
     "            ScaleIntensityRanged(\n",
     "                keys=[\"image\"],\n",
     "                a_min=-57,\n",
diff --git a/acceleration/dataset_type_performance.ipynb b/acceleration/dataset_type_performance.ipynb
index 4917cc78c4..71ea3b4950 100644
--- a/acceleration/dataset_type_performance.ipynb
+++ b/acceleration/dataset_type_performance.ipynb
@@ -125,6 +125,7 @@
     "    AsDiscrete,\n",
     "    Compose,\n",
     "    CropForegroundd,\n",
+    "    FromMetaTensord,\n",
     "    LoadImaged,\n",
     "    Orientationd,\n",
     "    RandCropByPosNegLabeld,\n",
@@ -404,6 +405,7 @@
     "                pixdim=(1.5, 1.5, 2.0),\n",
     "                mode=(\"bilinear\", \"nearest\"),\n",
     "            ),\n",
+    "            FromMetaTensord(keys=[\"image\", \"label\"]),\n",
     "            ScaleIntensityRanged(\n",
     "                keys=[\"image\"],\n",
     "                a_min=-57,\n",
@@ -443,6 +445,7 @@
     "                pixdim=(1.5, 1.5, 2.0),\n",
     "                mode=(\"bilinear\", \"nearest\"),\n",
     "            ),\n",
+    "            FromMetaTensord(keys=[\"image\", \"label\"]),\n",
     "            ScaleIntensityRanged(\n",
     "                keys=[\"image\"],\n",
     "                a_min=-57,\n",
diff --git a/acceleration/fast_training_tutorial.ipynb b/acceleration/fast_training_tutorial.ipynb
index 30c405581c..6b0988b45b 100644
--- a/acceleration/fast_training_tutorial.ipynb
+++ b/acceleration/fast_training_tutorial.ipynb
@@ -107,6 +107,7 @@
     "    Compose,\n",
     "    CropForegroundd,\n",
     "    FgBgToIndicesd,\n",
+    "    FromMetaTensord,\n",
     "    LoadImaged,\n",
     "    Orientationd,\n",
     "    RandCropByPosNegLabeld,\n",
@@ -224,6 +225,7 @@
     "            pixdim=(1.5, 1.5, 2.0),\n",
     "            mode=(\"bilinear\", \"nearest\"),\n",
     "        ),\n",
+    "        FromMetaTensord(keys=[\"image\", \"label\"]),\n",
     "        ScaleIntensityRanged(\n",
     "            keys=[\"image\"],\n",
     "            a_min=-57,\n",
@@ -275,6 +277,7 @@
     "            pixdim=(1.5, 1.5, 2.0),\n",
     "            mode=(\"bilinear\", \"nearest\"),\n",
     "        ),\n",
+    "        FromMetaTensord(keys=[\"image\", \"label\"]),\n",
     "        ScaleIntensityRanged(\n",
     "            keys=[\"image\"],\n",
     "            a_min=-57,\n",
diff --git a/deepedit/ignite/infoANDinference.ipynb b/deepedit/ignite/infoANDinference.ipynb
index 62635077ff..242029f518 100644
--- a/deepedit/ignite/infoANDinference.ipynb
+++ b/deepedit/ignite/infoANDinference.ipynb
@@ -97,6 +97,8 @@
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import torch\n",
+    "from torch import jit\n",
+    "import tempfile\n",
     "\n",
     "import monai\n",
     "from monai.config import print_config\n",
@@ -120,6 +122,7 @@
     "    SqueezeDimd,\n",
     "    ToNumpyd,\n",
     "    ToTensord,\n",
+    "    FromMetaTensord,\n",
     ")\n",
     "\n",
     "from monai.networks.nets import DynUNet\n",
@@ -261,19 +264,16 @@
    ],
    "source": [
     "# Download data and model\n",
+    "data_dir = tempfile.TemporaryDirectory().name\n",
     "\n",
     "resource = \"https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/_image.nii.gz\"\n",
-    "dst = \"_image.nii.gz\"\n",
-    "\n",
-    "if not os.path.exists(dst):\n",
-    "    monai.apps.download_url(resource, dst)\n",
+    "image_path = os.path.join(data_dir, \"_image.nii.gz\")\n",
+    "monai.apps.download_url(resource, image_path)\n",
     "\n",
     "resource = \"https://github.com/Project-MONAI/MONAI-extra-test-data/releases/\\\n",
-    "download/0.8.1/pretrained_deepedit_dynunet-final.pt\"\n",
-    "dst = \"pretrained_deepedit_dynunet-final.pt\"\n",
-    "\n",
-    "if not os.path.exists(dst):\n",
-    "    monai.apps.download_url(resource, dst)"
+    "download/0.8.1/pretrained_deepedit_dynunet-final.ts\"\n",
+    "model_path = os.path.join(data_dir, \"pretrained_deepedit_dynunet-final.ts\")\n",
+    "monai.apps.download_url(resource, model_path)"
    ]
   },
   {
@@ -313,11 +313,12 @@
      "output_type": "stream",
      "text": [
       "EnsureChannelFirstd => image shape: (1, 392, 392, 210)\n",
-      "Orientationd => image shape: (1, 392, 392, 210)\n",
-      "ScaleIntensityRanged => image shape: (1, 392, 392, 210)\n",
-      "AddGuidanceFromPointsDeepEditd => image shape: (1, 392, 392, 210)\n",
-      "Resized => image shape: (1, 128, 128, 128)\n",
-      "ResizeGuidanceMultipleLabelDeepEditd => image shape: (1, 128, 128, 128)\n",
+      "FromMetaTensord => image shape: torch.Size([1, 392, 392, 210])\n",
+      "Orientationd => image shape: torch.Size([1, 392, 392, 210])\n",
+      "ScaleIntensityRanged => image shape: torch.Size([1, 392, 392, 210])\n",
+      "AddGuidanceFromPointsDeepEditd => image shape: torch.Size([1, 392, 392, 210])\n",
+      "Resized => image shape: torch.Size([1, 128, 128, 128])\n",
+      "ResizeGuidanceMultipleLabelDeepEditd => image shape: torch.Size([1, 128, 128, 128])\n",
       "AddGuidanceSignalDeepEditd => image shape: (3, 128, 128, 128)\n",
       "ToTensord => image shape: torch.Size([3, 128, 128, 128])\n"
      ]
@@ -325,7 +326,7 @@
    ],
    "source": [
     "data = {\n",
-    "    'image': '_image.nii.gz',\n",
+    "    'image': image_path,\n",
     "    'guidance': {'spleen': [[66, 180, 105], [66, 180, 145]], 'background': []},\n",
     "}\n",
     "\n",
@@ -338,6 +339,8 @@
     "    LoadImaged(keys=\"image\", reader=\"ITKReader\"),\n",
     "    # Ensure channel first\n",
     "    EnsureChannelFirstd(keys=\"image\"),\n",
+    "    # Convert away from MetaTensor\n",
+    "    FromMetaTensord(keys=\"image\"),\n",
     "    # Change image orientation\n",
     "    Orientationd(keys=\"image\", axcodes=\"RAS\"),\n",
     "    # Scaling image intensity - works well for CT images\n",
@@ -505,8 +508,7 @@
    ],
    "source": [
     "# Evaluation\n",
-    "model_path = 'pretrained_deepedit_dynunet-final.pt'\n",
-    "model.load_state_dict(torch.load(model_path))\n",
+    "model = jit.load(model_path)\n",
     "model.cuda()\n",
     "model.eval()\n",
     "\n",
@@ -542,17 +544,6 @@
     "        i, image.shape, label.shape, np.min(label), np.max(label), np.sum(label)))\n",
     "    show_image(image, label)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# remove downloaded files\n",
-    "os.remove('_image.nii.gz')\n",
-    "os.remove('pretrained_deepedit_dynunet-final.pt')"
-   ]
   }
  ],
  "metadata": {
diff --git a/deepgrow/ignite/inference.ipynb b/deepgrow/ignite/inference.ipynb
index 077ac69db5..6aaa98a269 100644
--- a/deepgrow/ignite/inference.ipynb
+++ b/deepgrow/ignite/inference.ipynb
@@ -59,7 +59,8 @@
     "    ToNumpyd,\n",
     "    Activationsd,\n",
     "    AsDiscreted,\n",
-    "    Resized\n",
+    "    Resized,\n",
+    "    FromMetaTensord,\n",
     ")\n",
     "\n",
     "max_epochs = 1\n",
@@ -421,7 +422,7 @@
     "    LoadImaged(keys='image'),\n",
     "    AsChannelFirstd(keys='image'),\n",
     "    Spacingd(keys='image', pixdim=pixdim, mode='bilinear'),\n",
-    "\n",
+    "    FromMetaTensord(keys='image'),\n",
     "    AddGuidanceFromPointsd(ref_image='image', guidance='guidance', foreground='foreground', background='background',\n",
     "                           dimensions=dimensions),\n",
     "    Fetch2DSliced(keys='image', guidance='guidance'),\n",
diff --git a/deepgrow/ignite/inference_3d.ipynb b/deepgrow/ignite/inference_3d.ipynb
index 0cf2db6199..fdd91f2bd7 100644
--- a/deepgrow/ignite/inference_3d.ipynb
+++ b/deepgrow/ignite/inference_3d.ipynb
@@ -39,7 +39,8 @@
     "    ToNumpyd,\n",
     "    Activationsd,\n",
     "    AsDiscreted,\n",
-    "    Resized\n",
+    "    Resized,\n",
+    "    FromMetaTensord,\n",
     ")\n",
     "\n",
     "max_epochs = 1\n",
@@ -140,6 +141,8 @@
     "    LoadImaged(keys='image'),\n",
     "    AsChannelFirstd(keys='image'),\n",
     "    Spacingd(keys='image', pixdim=pixdim, mode='bilinear'),\n",
+    "    FromMetaTensord(keys='image'),\n",
+    "    ToNumpyd(keys=('image', 'image_meta_dict')),\n",
     "    AddGuidanceFromPointsd(ref_image='image', guidance='guidance', foreground='foreground', background='background',\n",
     "                           dimensions=dimensions),\n",
     "    AddChanneld(keys='image'),\n",
@@ -164,7 +167,7 @@
     "    guidance = guidance if guidance else [np.roll(data['foreground'], 1).tolist(), []]\n",
     "    slice_idx = guidance[0][0][0] if guidance else slice_idx\n",
     "    print('Guidance: {}; Slice Idx: {}'.format(guidance, slice_idx))\n",
-    "    if tname == 'Resized':\n",
+    "    if tname in ('Resized', 'FromMetaTensord', 'ToNumpyd'):\n",
     "        continue\n",
     "\n",
     "    image = image[:, :, slice_idx] if tname in ('LoadImaged') else image[slice_idx] if tname in (\n",
diff --git a/deployment/bentoml/mednist_classifier_bentoml.ipynb b/deployment/bentoml/mednist_classifier_bentoml.ipynb
index 860e0915e2..f1737385de 100644
--- a/deployment/bentoml/mednist_classifier_bentoml.ipynb
+++ b/deployment/bentoml/mednist_classifier_bentoml.ipynb
@@ -104,6 +104,7 @@
     "    RandZoom,\n",
     "    ScaleIntensity,\n",
     "    EnsureType,\n",
+    "    ToNumpy,\n",
     ")\n",
     "from monai.utils import set_determinism\n",
     "\n",
@@ -230,7 +231,8 @@
    "source": [
     "train_transforms = Compose(\n",
     "    [\n",
-    "        LoadImage(image_only=True),\n",
+    "        LoadImage(),\n",
+    "        ToNumpy(),\n",
     "        AddChannel(),\n",
     "        ScaleIntensity(),\n",
     "        RandRotate(range_x=np.pi / 12, prob=0.5, keep_size=True),\n",
diff --git a/modules/3d_image_transforms.ipynb b/modules/3d_image_transforms.ipynb
index f407fd3838..ef06ff1d3a 100644
--- a/modules/3d_image_transforms.ipynb
+++ b/modules/3d_image_transforms.ipynb
@@ -44,6 +44,7 @@
     "    Rand3DElasticd,\n",
     "    RandAffined,\n",
     "    Spacingd,\n",
+    "    FromMetaTensord,\n",
     ")\n",
     "from monai.config import print_config\n",
     "from monai.apps import download_and_extract\n",
@@ -279,11 +280,11 @@
     }
    ],
    "source": [
-    "image, metadata = loader(train_data_dicts[0][\"image\"])\n",
+    "image = loader(train_data_dicts[0][\"image\"])\n",
+    "metadata = image.meta\n",
     "# print(f\"input: {train_data_dicts[0]['image']}\")\n",
     "print(f\"image shape: {image.shape}\")\n",
-    "print(f\"image affine:\\n{metadata['affine']}\")\n",
-    "print(f\"image pixdim:\\n{metadata['pixdim']}\")"
+    "print(f\"image affine:\\n{metadata['affine']}\")"
    ]
   },
   {
@@ -330,8 +331,7 @@
     "data_dict = loader(train_data_dicts[0])\n",
     "# print(f\"input:, {train_data_dicts[0]}\")\n",
     "print(f\"image shape: {data_dict['image'].shape}\")\n",
-    "print(f\"label shape: {data_dict['label'].shape}\")\n",
-    "print(f\"image pixdim:\\n{data_dict['image_meta_dict']['pixdim']}\")"
+    "print(f\"label shape: {data_dict['label'].shape}\")"
    ]
   },
   {
@@ -436,18 +436,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "image shape: (1, 512, 512, 55)\n",
-      "label shape: (1, 512, 512, 55)\n",
-      "image affine after Spacing:\n",
-      "[[ 0.0000000e+00 -9.7656202e-01  0.0000000e+00  4.7683716e-07]\n",
-      " [-9.7656202e-01  0.0000000e+00  0.0000000e+00  4.7683716e-07]\n",
-      " [ 0.0000000e+00  0.0000000e+00 -5.0000000e+00  2.7000000e+02]\n",
-      " [ 0.0000000e+00  0.0000000e+00  0.0000000e+00  1.0000000e+00]]\n",
-      "label affine after Spacing:\n",
-      "[[ 0.0000000e+00 -9.7656202e-01  0.0000000e+00  4.7683716e-07]\n",
-      " [-9.7656202e-01  0.0000000e+00  0.0000000e+00  4.7683716e-07]\n",
-      " [ 0.0000000e+00  0.0000000e+00 -5.0000000e+00  2.7000000e+02]\n",
-      " [ 0.0000000e+00  0.0000000e+00  0.0000000e+00  1.0000000e+00]]\n"
+      "image shape: torch.Size([1, 512, 512, 55])\n",
+      "label shape: torch.Size([1, 512, 512, 55])\n",
+      "image affine after Orientation:\n",
+      "tensor([[ 0.0000e+00, -9.7656e-01,  0.0000e+00,  4.7684e-07],\n",
+      "        [-9.7656e-01,  0.0000e+00,  0.0000e+00,  4.7684e-07],\n",
+      "        [ 0.0000e+00,  0.0000e+00, -5.0000e+00,  2.7000e+02],\n",
+      "        [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]])\n",
+      "label affine after Orientation:\n",
+      "tensor([[ 0.0000e+00, -9.7656e-01,  0.0000e+00,  4.7684e-07],\n",
+      "        [-9.7656e-01,  0.0000e+00,  0.0000e+00,  4.7684e-07],\n",
+      "        [ 0.0000e+00,  0.0000e+00, -5.0000e+00,  2.7000e+02],\n",
+      "        [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]])\n"
      ]
     }
    ],
@@ -455,8 +455,8 @@
     "data_dict = orientation(datac_dict)\n",
     "print(f\"image shape: {data_dict['image'].shape}\")\n",
     "print(f\"label shape: {data_dict['label'].shape}\")\n",
-    "print(f\"image affine after Spacing:\\n{data_dict['image_meta_dict']['affine']}\")\n",
-    "print(f\"label affine after Spacing:\\n{data_dict['label_meta_dict']['affine']}\")"
+    "print(f\"image affine after Orientation:\\n{data_dict['image'].affine}\")\n",
+    "print(f\"label affine after Orientation:\\n{data_dict['label'].affine}\")"
    ]
   },
   {
@@ -522,18 +522,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "image shape: (1, 334, 334, 55)\n",
-      "label shape: (1, 334, 334, 55)\n",
+      "image shape: torch.Size([1, 334, 334, 55])\n",
+      "label shape: torch.Size([1, 334, 334, 55])\n",
       "image affine after Spacing:\n",
-      "[[ 0.0000000e+00 -1.5000000e+00  0.0000000e+00  4.7683716e-07]\n",
-      " [-1.5000000e+00  0.0000000e+00  0.0000000e+00  4.7683716e-07]\n",
-      " [ 0.0000000e+00  0.0000000e+00 -5.0000000e+00  2.7000000e+02]\n",
-      " [ 0.0000000e+00  0.0000000e+00  0.0000000e+00  1.0000000e+00]]\n",
+      "tensor([[ 0.0000e+00, -1.5000e+00,  0.0000e+00,  4.7684e-07],\n",
+      "        [-1.5000e+00,  0.0000e+00,  0.0000e+00,  4.7684e-07],\n",
+      "        [ 0.0000e+00,  0.0000e+00, -5.0000e+00,  2.7000e+02],\n",
+      "        [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]])\n",
       "label affine after Spacing:\n",
-      "[[ 0.0000000e+00 -1.5000000e+00  0.0000000e+00  4.7683716e-07]\n",
-      " [-1.5000000e+00  0.0000000e+00  0.0000000e+00  4.7683716e-07]\n",
-      " [ 0.0000000e+00  0.0000000e+00 -5.0000000e+00  2.7000000e+02]\n",
-      " [ 0.0000000e+00  0.0000000e+00  0.0000000e+00  1.0000000e+00]]\n"
+      "tensor([[ 0.0000e+00, -1.5000e+00,  0.0000e+00,  4.7684e-07],\n",
+      "        [-1.5000e+00,  0.0000e+00,  0.0000e+00,  4.7684e-07],\n",
+      "        [ 0.0000e+00,  0.0000e+00, -5.0000e+00,  2.7000e+02],\n",
+      "        [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  1.0000e+00]])\n"
      ]
     }
    ],
@@ -541,8 +541,8 @@
     "data_dict = spacing(data_dict)\n",
     "print(f\"image shape: {data_dict['image'].shape}\")\n",
     "print(f\"label shape: {data_dict['label'].shape}\")\n",
-    "print(f\"image affine after Spacing:\\n{data_dict['image_meta_dict']['affine']}\")\n",
-    "print(f\"label affine after Spacing:\\n{data_dict['label_meta_dict']['affine']}\")"
+    "print(f\"image affine after Spacing:\\n{data_dict['image'].affine}\")\n",
+    "print(f\"label affine after Spacing:\\n{data_dict['label'].affine}\")"
    ]
   },
   {
@@ -621,6 +621,7 @@
     }
    ],
    "source": [
+    "data_dict = FromMetaTensord(keys=[\"image\", \"label\"])(data_dict)\n",
     "rand_affine = RandAffined(\n",
     "    keys=[\"image\", \"label\"],\n",
     "    mode=(\"bilinear\", \"nearest\"),\n",
diff --git a/modules/autoencoder_mednist.ipynb b/modules/autoencoder_mednist.ipynb
index a0b6a2df5a..e7be6e7a07 100644
--- a/modules/autoencoder_mednist.ipynb
+++ b/modules/autoencoder_mednist.ipynb
@@ -105,6 +105,7 @@
     "    ScaleIntensityD,\n",
     "    EnsureTypeD,\n",
     "    Lambda,\n",
+    "    FromMetaTensord,\n",
     ")\n",
     "from monai.utils import set_determinism\n",
     "\n",
@@ -278,6 +279,7 @@
     "train_transforms = Compose(\n",
     "    [\n",
     "        LoadImageD(keys=[\"im\"]),\n",
+    "        FromMetaTensord(keys=[\"im\"]),\n",
     "        AddChannelD(keys=[\"im\"]),\n",
     "        ScaleIntensityD(keys=[\"im\"]),\n",
     "        RandRotateD(keys=[\"im\"], range_x=np.pi / 12, prob=0.5, keep_size=True),\n",
@@ -291,6 +293,7 @@
     "test_transforms = Compose(\n",
     "    [\n",
     "        LoadImageD(keys=[\"im\"]),\n",
+    "        FromMetaTensord(keys=[\"im\"]),\n",
     "        AddChannelD(keys=[\"im\"]),\n",
     "        ScaleIntensityD(keys=[\"im\"]),\n",
     "        EnsureTypeD(keys=[\"im\"]),\n",
diff --git a/modules/batch_output_transform.ipynb b/modules/batch_output_transform.ipynb
index d4cc49f1d0..2be8d3ba4f 100644
--- a/modules/batch_output_transform.ipynb
+++ b/modules/batch_output_transform.ipynb
@@ -102,6 +102,7 @@
     "    RandCropByPosNegLabeld,\n",
     "    ScaleIntensityd,\n",
     "    EnsureTyped,\n",
+    "    FromMetaTensord,\n",
     ")\n",
     "from monai.utils import get_torch_version_tuple\n",
     "\n",
@@ -244,6 +245,7 @@
     "train_transforms = Compose(\n",
     "    [\n",
     "        LoadImaged(keys=[\"image\", \"label\"]),\n",
+    "        FromMetaTensord(keys=[\"image\", \"label\"]),\n",
     "        AsChannelFirstd(keys=[\"image\", \"label\"], channel_dim=-1),\n",
     "        ScaleIntensityd(keys=\"image\"),\n",
     "        RandCropByPosNegLabeld(\n",
@@ -255,6 +257,7 @@
     "val_transforms = Compose(\n",
     "    [\n",
     "        LoadImaged(keys=[\"image\", \"label\"]),\n",
+    "        FromMetaTensord(keys=[\"image\", \"label\"]),\n",
     "        AsChannelFirstd(keys=[\"image\", \"label\"], channel_dim=-1),\n",
     "        ScaleIntensityd(keys=\"image\"),\n",
     "        EnsureTyped(keys=[\"image\", \"label\"]),\n",
diff --git a/modules/cross_validation_models_ensemble.ipynb b/modules/cross_validation_models_ensemble.ipynb
index 2c40707b68..c5a6448d6d 100644
--- a/modules/cross_validation_models_ensemble.ipynb
+++ b/modules/cross_validation_models_ensemble.ipynb
@@ -104,6 +104,7 @@
     "    ScaleIntensityd,\n",
     "    EnsureTyped,\n",
     "    VoteEnsembled,\n",
+    "    FromMetaTensord,\n",
     ")\n",
     "from monai.utils import set_determinism\n",
     "\n",
@@ -250,6 +251,7 @@
     "    [\n",
     "        LoadImaged(keys=[\"image\", \"label\"]),\n",
     "        AsChannelFirstd(keys=[\"image\", \"label\"], channel_dim=-1),\n",
+    "        FromMetaTensord(keys=[\"image\", \"label\"]),\n",
     "        ScaleIntensityd(keys=[\"image\", \"label\"]),\n",
     "        RandCropByPosNegLabeld(\n",
     "            keys=[\"image\", \"label\"],\n",
@@ -267,6 +269,7 @@
     "    [\n",
     "        LoadImaged(keys=[\"image\", \"label\"]),\n",
     "        AsChannelFirstd(keys=[\"image\", \"label\"], channel_dim=-1),\n",
+    "        FromMetaTensord(keys=[\"image\", \"label\"]),\n",
     "        ScaleIntensityd(keys=[\"image\", \"label\"]),\n",
     "        EnsureTyped(keys=[\"image\", \"label\"]),\n",
     "    ]\n",
diff --git a/modules/decollate_batch.ipynb b/modules/decollate_batch.ipynb
index c281e6b739..700e44f295 100644
--- a/modules/decollate_batch.ipynb
+++ b/modules/decollate_batch.ipynb
@@ -143,6 +143,7 @@
     "    Resized,\n",
     "    SaveImaged,\n",
     "    ScaleIntensityd,\n",
+    "    FromMetaTensord,\n",
     ")\n",
     "from monai.utils import set_determinism\n",
     "\n",
@@ -246,6 +247,7 @@
     "        LoadImaged(keys=[\"img\", \"seg\"]),\n",
     "        EnsureChannelFirstd(keys=[\"img\", \"seg\"]),\n",
     "        Orientationd(keys=\"img\", axcodes=\"RAS\"),\n",
+    "        FromMetaTensord(keys=[\"img\", \"seg\"]),\n",
     "        Resized(keys=\"img\", spatial_size=(96, 96, 96), mode=\"trilinear\", align_corners=True),\n",
     "        ScaleIntensityd(keys=\"img\"),\n",
     "        EnsureTyped(keys=[\"img\", \"seg\"]),\n",
@@ -288,7 +290,10 @@
     "            device=device,\n",
     "        ),\n",
     "        AsDiscreted(keys=\"pred\", threshold=0.5),\n",
-    "        SaveImaged(keys=\"pred\", meta_keys=\"pred_meta_dict\", output_dir=root_dir, resample=False),\n",
+    "        SaveImaged(\n",
+    "            keys=\"pred\", meta_keys=\"pred_meta_dict\", resample=False,\n",
+    "            output_dir=tempfile.TemporaryDirectory().name\n",
+    "        ),\n",
     "    ]\n",
     ")\n",
     "# will compute mean dice on the decollated `predictions` and `labels`, which are list of `channel-first` tensors\n",
diff --git a/modules/image_dataset.ipynb b/modules/image_dataset.ipynb
index 45a803b112..9da53da161 100644
--- a/modules/image_dataset.ipynb
+++ b/modules/image_dataset.ipynb
@@ -118,6 +118,7 @@
     "from monai.data import ImageDataset\n",
     "from monai.transforms import Compose, EnsureChannelFirst, RandAdjustContrast, Spacing\n",
     "from monai.config import print_config\n",
+    "from monai.data import MetaTensor\n",
     "\n",
     "print_config()"
    ]
@@ -173,11 +174,12 @@
    "source": [
     "class TestCompose(Compose):\n",
     "    def __call__(self, data, meta):\n",
-    "        data = self.transforms[0](data, meta)  # ensure channel first\n",
-    "        data, _, meta[\"affine\"] = self.transforms[1](data, meta[\"affine\"])  # spacing\n",
+    "        data = MetaTensor(data, meta=meta)  # convert to MetaTensor\n",
+    "        data = self.transforms[0](data)  # ensure channel first\n",
+    "        data = self.transforms[1](data)  # spacing\n",
     "        if len(self.transforms) == 3:\n",
-    "            return self.transforms[2](data), meta  # image contrast\n",
-    "        return data, meta\n",
+    "            return self.transforms[2](data), data.meta  # image contrast\n",
+    "        return data, data.meta\n",
     "\n",
     "\n",
     "img_xform = TestCompose([EnsureChannelFirst(), Spacing(pixdim=(1.5, 1.5, 3.0)), RandAdjustContrast()])\n",
diff --git a/modules/integrate_3rd_party_transforms.ipynb b/modules/integrate_3rd_party_transforms.ipynb
index ff301c9bf5..9b6ab2cbac 100644
--- a/modules/integrate_3rd_party_transforms.ipynb
+++ b/modules/integrate_3rd_party_transforms.ipynb
@@ -59,12 +59,14 @@
     "    AddChanneld,\n",
     "    Compose,\n",
     "    CropForegroundd,\n",
+    "    FromMetaTensord,\n",
     "    LoadImaged,\n",
     "    Orientationd,\n",
     "    ScaleIntensityRanged,\n",
     "    Spacingd,\n",
     "    SqueezeDimd,\n",
     "    EnsureTyped,\n",
+    "    ToNumpyd,\n",
     "    adaptor,\n",
     ")\n",
     "from monai.data import DataLoader, Dataset\n",
@@ -269,6 +271,8 @@
     "    Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
     "    Spacingd(keys=[\"image\", \"label\"], pixdim=(\n",
     "        1.5, 1.5, 2.0), mode=(\"bilinear\", \"nearest\")),\n",
+    "    FromMetaTensord(keys=[\"image\", \"label\"]),\n",
+    "    ToNumpyd(keys=[\"image\", \"label\", \"image_meta_dict\", \"label_meta_dict\"]),\n",
     "    ScaleIntensityRanged(keys=[\"image\"], a_min=-57,\n",
     "                         a_max=164, b_min=0.0, b_max=1.0, clip=True),\n",
     "    CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\"),\n",
diff --git a/modules/interpretability/cats_and_dogs.ipynb b/modules/interpretability/cats_and_dogs.ipynb
index c8d6974e80..f189d9085c 100644
--- a/modules/interpretability/cats_and_dogs.ipynb
+++ b/modules/interpretability/cats_and_dogs.ipynb
@@ -40,6 +40,7 @@
     "    Resized,\n",
     "    Rotate90d,\n",
     "    ScaleIntensityd,\n",
+    "    FromMetaTensord,\n",
     ")\n",
     "from monai.networks.utils import eval_mode\n",
     "from contextlib import nullcontext\n",
@@ -165,6 +166,7 @@
     "transforms = Compose([\n",
     "    LoadImaged(\"image\"),\n",
     "    AsChannelFirstd(\"image\"),\n",
+    "    FromMetaTensord(\"image\"),\n",
     "    ScaleIntensityd(\"image\"),\n",
     "    Rotate90d(\"image\", k=3),\n",
     "    DivisiblePadd(\"image\", k=divisible_factor),\n",
@@ -200,7 +202,7 @@
     "    axes = np.asarray(axes) if nims == 1 else axes\n",
     "    for d, ax in zip(data, axes.ravel()):\n",
     "        # channel last for matplotlib\n",
-    "        im = np.moveaxis(d[\"image\"], 0, -1)\n",
+    "        im = np.moveaxis(d[\"image\"].cpu().numpy(), 0, -1)\n",
     "        ax.imshow(im, cmap='gray')\n",
     "        ax.set_title(Animals(d['label']).name, fontsize=25)\n",
     "        ax.axis(\"off\")\n",
diff --git a/modules/interpretability/class_lung_lesion.ipynb b/modules/interpretability/class_lung_lesion.ipynb
index 12ca96dd47..edcfac4ef6 100644
--- a/modules/interpretability/class_lung_lesion.ipynb
+++ b/modules/interpretability/class_lung_lesion.ipynb
@@ -85,6 +85,7 @@
     "    AddChanneld,\n",
     "    Compose,\n",
     "    LoadImaged,\n",
+    "    FromMetaTensord,\n",
     "    RandFlipd,\n",
     "    RandRotate90d,\n",
     "    RandSpatialCropd,\n",
@@ -247,6 +248,7 @@
     "train_transforms = Compose(\n",
     "    [\n",
     "        LoadImaged(\"image\"),\n",
+    "        FromMetaTensord(\"image\"),\n",
     "        AddChanneld(\"image\"),\n",
     "        ScaleIntensityRanged(\n",
     "            \"image\",\n",
@@ -268,6 +270,7 @@
     "val_transforms = Compose(\n",
     "    [\n",
     "        LoadImaged(\"image\"),\n",
+    "        FromMetaTensord(\"image\"),\n",
     "        AddChanneld(\"image\"),\n",
     "        ScaleIntensityRanged(\n",
     "            \"image\",\n",
diff --git a/modules/interpretability/covid_classification.ipynb b/modules/interpretability/covid_classification.ipynb
index 6ec9184d57..26c98a310d 100644
--- a/modules/interpretability/covid_classification.ipynb
+++ b/modules/interpretability/covid_classification.ipynb
@@ -88,6 +88,7 @@
     "    Compose, LoadImage, Lambda, AddChannel,\n",
     "    ScaleIntensity, EnsureType, RandRotate,\n",
     "    RandFlip, Rand2DElastic, RandZoom, Resize,\n",
+    "    ToNumpy,\n",
     ")\n",
     "from monai.apps import download_and_extract\n",
     "\n",
@@ -220,7 +221,8 @@
     "\n",
     "\n",
     "train_transforms = Compose([\n",
-    "    LoadImage(image_only=True),\n",
+    "    LoadImage(),\n",
+    "    ToNumpy(),\n",
     "    Lambda(lambda im: im if im.ndim == 2 else im[..., 0]),\n",
     "    AddChannel(),\n",
     "    Resize(spatial_size=crop_size, mode=\"area\"),\n",
@@ -234,7 +236,8 @@
     "])\n",
     "\n",
     "val_transforms = Compose([\n",
-    "    LoadImage(image_only=True),\n",
+    "    LoadImage(),\n",
+    "    ToNumpy(),\n",
     "    Lambda(lambda im: im if im.ndim == 2 else im[..., 0]),\n",
     "    AddChannel(),\n",
     "    Resize(spatial_size=crop_size, mode=\"area\"),\n",
diff --git a/modules/inverse_transforms_and_test_time_augmentations.ipynb b/modules/inverse_transforms_and_test_time_augmentations.ipynb
index f1050e2e01..544befb4c2 100644
--- a/modules/inverse_transforms_and_test_time_augmentations.ipynb
+++ b/modules/inverse_transforms_and_test_time_augmentations.ipynb
@@ -11,7 +11,7 @@
     "\n",
     "### What are transforms?\n",
     "\n",
-    "- We use transforms to modify data. In MONAI, we use them to (for exampl) load images from file, add a channel component, normalise the intensities and reshape the image.\n",
+    "- We use transforms to modify data. In MONAI, we use them to (for example) load images from file, add a channel component, normalise the intensities and reshape the image.\n",
     "- We can also use transforms as a method of data augmentation – we have a finite amount of data so to avoid overfitting, we can apply random transforms to modify our data each epoch.\n",
     "- Examples of random transformations might be randomly flipping, rotating, cropping, padding, zooming, as well as applying non-rigid deformations.\n",
     "\n",
@@ -145,6 +145,7 @@
     "    EnsureTyped,\n",
     "    EnsureType,\n",
     "    BatchInverseTransform,\n",
+    "    FromMetaTensord,\n",
     ")\n",
     "from monai.transforms.utils import allow_missing_keys_mode\n",
     "from monai.utils import first, set_determinism\n",
@@ -228,8 +229,8 @@
     "    def __call__(self, data):\n",
     "        d = dict(data)\n",
     "        im = d[self.label_key]\n",
-    "        q = np.sum((im > 0).reshape(-1, im.shape[-1]), axis=0)\n",
-    "        _slice = np.where(q == np.max(q))[0][0]\n",
+    "        q = (im > 0).reshape(-1, im.shape[-1]).sum(dim=0)\n",
+    "        _slice = q.argmax(dim=0)\n",
     "        for key in self.keys:\n",
     "            d[key] = d[key][..., _slice]\n",
     "        return d\n",
@@ -243,10 +244,9 @@
     "    def __call__(self, data):\n",
     "        d = {}\n",
     "        for key in self.keys:\n",
-    "            fname = os.path.basename(\n",
-    "                data[key + \"_meta_dict\"][\"filename_or_obj\"])\n",
+    "            fname = os.path.basename(data[key].meta[\"filename_or_obj\"])\n",
     "            path = os.path.join(self.path, key, fname)\n",
-    "            nib.save(nib.Nifti1Image(data[key], np.eye(4)), path)\n",
+    "            nib.save(nib.Nifti1Image(data[key].numpy(), np.eye(4)), path)\n",
     "            d[key] = path\n",
     "        return d\n",
     "\n",
@@ -262,6 +262,7 @@
     "    SliceWithMaxNumLabelsd(keys, \"label\"),\n",
     "    SaveSliced(keys, data_dir),\n",
     "])\n",
+    "\n",
     "# Running the whole way through the dataset will create the 2D slices and save to file\n",
     "ds_2d = Dataset(data_dicts, transform_2d_slice)\n",
     "dl_2d = DataLoader(ds_2d, batch_size=1, num_workers=10)\n",
@@ -303,8 +304,9 @@
     "train_transforms = Compose(\n",
     "    [\n",
     "        LoadImaged(keys),\n",
-    "        Lambdad(\"label\", lambda x: (x > 0).astype(\n",
-    "            np.float64)),  # make label binary\n",
+    "        FromMetaTensord(keys),\n",
+    "        Lambdad(\"label\", lambda x: (x > 0).to(\n",
+    "            torch.float32)),  # make label binary\n",
     "        RandAffined(\n",
     "            keys,\n",
     "            prob=1.0,\n",
@@ -1623,8 +1625,9 @@
     "# Need minimal transforms just to be able to show the unmodified originals\n",
     "minimal_transforms = Compose([\n",
     "    LoadImaged(keys),\n",
-    "    Lambdad(\"label\", lambda x: (x > 0).astype(\n",
-    "        np.float64)),  # make label binary\n",
+    "    FromMetaTensord(keys),\n",
+    "    Lambdad(\"label\", lambda x: (x > 0).to(\n",
+    "        torch.float32)),  # make label binary\n",
     "    ScaleIntensityd(\"image\"),\n",
     "    EnsureTyped(keys),\n",
     "])\n",
diff --git a/modules/learning_rate.ipynb b/modules/learning_rate.ipynb
index 31afc6f1f5..04655ee496 100644
--- a/modules/learning_rate.ipynb
+++ b/modules/learning_rate.ipynb
@@ -113,6 +113,7 @@
     "    ScaleIntensityd,\n",
     "    EnsureTyped,\n",
     "    EnsureType,\n",
+    "    FromMetaTensord,\n",
     ")\n",
     "from monai.utils import set_determinism\n",
     "from torch.utils.data import DataLoader\n",
@@ -177,6 +178,7 @@
     "transforms = Compose(\n",
     "    [\n",
     "        LoadImaged(keys=\"image\"),\n",
+    "        FromMetaTensord(keys=\"image\"),\n",
     "        AddChanneld(keys=\"image\"),\n",
     "        ScaleIntensityd(keys=\"image\"),\n",
     "        CenterSpatialCropd(keys=\"image\", roi_size=(20, 20)),\n",
diff --git a/modules/load_medical_images.ipynb b/modules/load_medical_images.ipynb
index c697db36bf..def7c3e953 100644
--- a/modules/load_medical_images.ipynb
+++ b/modules/load_medical_images.ipynb
@@ -122,7 +122,7 @@
     "from monai.data import ITKReader, PILReader\n",
     "from monai.transforms import (\n",
     "    LoadImage, LoadImaged, EnsureChannelFirstd,\n",
-    "    Resized, EnsureTyped, Compose\n",
+    "    Resized, EnsureTyped, Compose, FromMetaTensord,\n",
     ")\n",
     "from monai.config import print_config\n",
     "\n",
@@ -198,9 +198,9 @@
     }
    ],
    "source": [
-    "data, meta = LoadImage()(filename)\n",
+    "data = LoadImage()(filename)\n",
     "print(f\"image data shape:{data.shape}\")\n",
-    "print(f\"meta data:{meta}\")"
+    "print(f\"meta data:{data.meta}\")"
    ]
   },
   {
@@ -269,10 +269,10 @@
     }
    ],
    "source": [
-    "data, meta = LoadImage()(filenames)\n",
+    "data = LoadImage()(filenames)\n",
     "\n",
     "print(f\"image data shape:{data.shape}\")\n",
-    "print(f\"meta data:{meta}\")"
+    "print(f\"meta data:{data.meta}\")"
    ]
   },
   {
@@ -336,10 +336,10 @@
     }
    ],
    "source": [
-    "data, meta = LoadImage()(filename)\n",
+    "data = LoadImage()(filename)\n",
     "\n",
     "print(f\"image data shape:{data.shape}\")\n",
-    "print(f\"meta data:{meta}\")"
+    "print(f\"meta data:{data.meta}\")"
    ]
   },
   {
@@ -408,10 +408,10 @@
     }
    ],
    "source": [
-    "data, meta = LoadImage()(filenames)\n",
+    "data = LoadImage()(filenames)\n",
     "\n",
     "print(f\"image data shape:{data.shape}\")\n",
-    "print(f\"meta data:{meta}\")"
+    "print(f\"meta data:{data.meta}\")"
    ]
   },
   {
@@ -481,10 +481,10 @@
     }
    ],
    "source": [
-    "data, meta = LoadImage()(sub_folder_path)\n",
+    "data = LoadImage()(sub_folder_path)\n",
     "\n",
     "print(f\"image data shape:{data.shape}\")\n",
-    "print(f\"meta data:{meta}\")"
+    "print(f\"meta data:{data.meta}\")"
    ]
   },
   {
@@ -542,10 +542,10 @@
     }
    ],
    "source": [
-    "data, meta = LoadImage()(filename)\n",
+    "data = LoadImage()(filename)\n",
     "\n",
     "print(f\"image data shape:{data.shape}\")\n",
-    "print(f\"meta data:{meta}\")"
+    "print(f\"meta data:{data.meta}\")"
    ]
   },
   {
@@ -584,10 +584,10 @@
    "source": [
     "loader = LoadImage()\n",
     "loader.register(ITKReader())\n",
-    "data, meta = loader(filename)\n",
+    "data = loader(filename)\n",
     "\n",
     "print(f\"image data shape:{data.shape}\")\n",
-    "print(f\"meta data:{meta}\")"
+    "print(f\"meta data:{data.meta}\")"
    ]
   },
   {
@@ -623,10 +623,10 @@
    ],
    "source": [
     "loader = LoadImage(PILReader(converter=lambda image: image.convert(\"LA\")))\n",
-    "data, meta = loader(filename)\n",
+    "data = loader(filename)\n",
     "\n",
     "print(f\"image data shape:{data.shape}\")\n",
-    "print(f\"meta data:{meta}\")"
+    "print(f\"meta data:{data.meta}\")"
    ]
   },
   {
@@ -664,6 +664,7 @@
     "transform = Compose([\n",
     "    LoadImaged(keys=\"image\"),\n",
     "    EnsureChannelFirstd(keys=\"image\"),\n",
+    "    FromMetaTensord(keys=\"image\"),\n",
     "    Resized(keys=\"image\", spatial_size=[64, 64]),\n",
     "    EnsureTyped(\"image\"),\n",
     "])\n",
diff --git a/modules/mednist_GAN_workflow_dict.ipynb b/modules/mednist_GAN_workflow_dict.ipynb
index 7c81c438f6..9596a638a6 100644
--- a/modules/mednist_GAN_workflow_dict.ipynb
+++ b/modules/mednist_GAN_workflow_dict.ipynb
@@ -64,6 +64,7 @@
     "    RandZoomD,\n",
     "    ScaleIntensityD,\n",
     "    EnsureTypeD,\n",
+    "    FromMetaTensord,\n",
     ")\n",
     "from monai.networks.nets import Discriminator, Generator\n",
     "from monai.networks import normal_init\n",
@@ -270,6 +271,7 @@
     "train_transforms = Compose(\n",
     "    [\n",
     "        LoadImageD(keys=[\"hand\"]),\n",
+    "        FromMetaTensord(keys=[\"hand\"]),\n",
     "        AddChannelD(keys=[\"hand\"]),\n",
     "        ScaleIntensityD(keys=[\"hand\"]),\n",
     "        RandRotateD(keys=[\"hand\"], range_x=np.pi /\n",
diff --git a/modules/nifti_read_example.ipynb b/modules/nifti_read_example.ipynb
index 5b19124dde..d79382be33 100644
--- a/modules/nifti_read_example.ipynb
+++ b/modules/nifti_read_example.ipynb
@@ -89,7 +89,6 @@
     "\n",
     "import glob\n",
     "import os\n",
-    "import shutil\n",
     "import tempfile\n",
     "\n",
     "import nibabel as nib\n",
@@ -106,36 +105,13 @@
     "    RandSpatialCrop,\n",
     "    ScaleIntensity,\n",
     "    EnsureType,\n",
+    "    ToNumpy,\n",
     ")\n",
     "from monai.utils import first\n",
     "\n",
     "print_config()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Setup data directory\n",
-    "\n",
-    "You can specify a directory with the `MONAI_DATA_DIRECTORY` environment variable.  \n",
-    "This allows you to save results and reuse downloads.  \n",
-    "If not specified a temporary directory will be used."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
-    "root_dir = tempfile.mkdtemp() if directory is None else directory\n",
-    "print(root_dir)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -149,6 +125,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "root_dir = tempfile.mkdtemp()\n",
     "for i in range(5):\n",
     "    im, seg = create_test_image_3d(128, 128, 128)\n",
     "\n",
@@ -187,9 +164,10 @@
     "\n",
     "imtrans = Compose(\n",
     "    [\n",
-    "        LoadImage(image_only=True),\n",
-    "        ScaleIntensity(),\n",
+    "        LoadImage(),\n",
     "        AddChannel(),\n",
+    "        ToNumpy(),\n",
+    "        ScaleIntensity(),\n",
     "        RandSpatialCrop((64, 64, 64), random_size=False),\n",
     "        EnsureType(),\n",
     "    ]\n",
@@ -197,8 +175,9 @@
     "\n",
     "segtrans = Compose(\n",
     "    [\n",
-    "        LoadImage(image_only=True),\n",
+    "        LoadImage(),\n",
     "        AddChannel(),\n",
+    "        ToNumpy(),\n",
     "        RandSpatialCrop((64, 64, 64), random_size=False),\n",
     "        EnsureType(),\n",
     "    ]\n",
@@ -236,10 +215,10 @@
     }
    ],
    "source": [
-    "imtrans = Compose([LoadImage(image_only=True),\n",
+    "imtrans = Compose([LoadImage(), ToNumpy(),\n",
     "                   ScaleIntensity(), AddChannel(), EnsureType()])\n",
     "\n",
-    "segtrans = Compose([LoadImage(image_only=True), AddChannel(), EnsureType()])\n",
+    "segtrans = Compose([LoadImage(), ToNumpy(), AddChannel(), EnsureType()])\n",
     "\n",
     "ds = ArrayDataset(images, imtrans, segs, segtrans)\n",
     "patch_iter = PatchIter(patch_size=(64, 64, 64), start_pos=(0, 0, 0))\n",
@@ -260,25 +239,6 @@
     "im, seg = first(loader)\n",
     "print(\"image shapes:\", im.shape, seg.shape)"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Cleanup data directory\n",
-    "\n",
-    "Remove directory if a temporary was used."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if directory is None:\n",
-    "    shutil.rmtree(root_dir)"
-   ]
   }
  ],
  "metadata": {
diff --git a/modules/postprocessing_transforms.ipynb b/modules/postprocessing_transforms.ipynb
index 2dc5092eb3..2ed8a75d55 100644
--- a/modules/postprocessing_transforms.ipynb
+++ b/modules/postprocessing_transforms.ipynb
@@ -66,6 +66,7 @@
     "    Spacingd,\n",
     "    EnsureTyped,\n",
     "    EnsureType,\n",
+    "    FromMetaTensord,\n",
     ")\n",
     "from monai.networks.nets import UNet\n",
     "from monai.networks.layers import Norm\n",
@@ -264,6 +265,7 @@
     "        Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
     "        Spacingd(keys=[\"image\", \"label\"], pixdim=(\n",
     "            1.5, 1.5, 2.0), mode=(\"bilinear\", \"nearest\")),\n",
+    "        FromMetaTensord(keys=[\"image\", \"label\"]),\n",
     "        ScaleIntensityRanged(\n",
     "            keys=[\"image\"], a_min=-57, a_max=164,\n",
     "            b_min=0.0, b_max=1.0, clip=True,\n",
@@ -292,6 +294,7 @@
     "        Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
     "        Spacingd(keys=[\"image\", \"label\"], pixdim=(\n",
     "            1.5, 1.5, 2.0), mode=(\"bilinear\", \"nearest\")),\n",
+    "        FromMetaTensord(keys=[\"image\", \"label\"]),\n",
     "        ScaleIntensityRanged(\n",
     "            keys=[\"image\"], a_min=-57, a_max=164,\n",
     "            b_min=0.0, b_max=1.0, clip=True,\n",
diff --git a/modules/resample_benchmark.ipynb b/modules/resample_benchmark.ipynb
index 15c258eabc..064be7d3f9 100644
--- a/modules/resample_benchmark.ipynb
+++ b/modules/resample_benchmark.ipynb
@@ -449,7 +449,7 @@
    "source": [
     "img = monai.transforms.LoadImaged(keys=\"img\")({\"img\": f\"{root_dir}/mri.nii\"})[\"img\"]\n",
     "# W, H, D -> D, H, W\n",
-    "img = img.transpose((2, 1, 0))"
+    "img = img.numpy().transpose((2, 1, 0))"
    ]
   },
   {
diff --git a/modules/tcia_csv_processing.ipynb b/modules/tcia_csv_processing.ipynb
index 1568a97523..55cac70572 100644
--- a/modules/tcia_csv_processing.ipynb
+++ b/modules/tcia_csv_processing.ipynb
@@ -63,7 +63,7 @@
     "\n",
     "from monai.data import CSVDataset\n",
     "from monai.apps import download_url, download_and_extract\n",
-    "from monai.transforms import LoadImaged\n",
+    "from monai.transforms import Compose, FromMetaTensord, LoadImaged\n",
     "from monai.config import print_config\n",
     "from monai.utils import ensure_tuple\n",
     "\n",
@@ -248,7 +248,10 @@
     "    filename=os.path.join(root_dir, \"ISPY1_Combined.csv\"),\n",
     "    img_dir=os.path.join(root_dir, \"tcia_images\"),\n",
     "    row_indices=[[0, 8]],\n",
-    "    transform=LoadImaged(keys=\"image\"),\n",
+    "    transform=Compose([\n",
+    "        LoadImaged(keys=\"image\"),\n",
+    "        FromMetaTensord(keys=\"image\"),\n",
+    "    ])\n",
     ")"
    ]
   },
diff --git a/modules/transfer_mmar.ipynb b/modules/transfer_mmar.ipynb
index 88da92434e..b3bc3b2cc6 100644
--- a/modules/transfer_mmar.ipynb
+++ b/modules/transfer_mmar.ipynb
@@ -146,6 +146,7 @@
     "from monai.transforms import (\n",
     "    AsDiscrete,\n",
     "    EnsureChannelFirstd,\n",
+    "    FromMetaTensord,\n",
     "    Compose,\n",
     "    LoadImaged,\n",
     "    ScaleIntensityRanged,\n",
@@ -331,6 +332,8 @@
     "        Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
     "        Spacingd(keys=[\"image\", \"label\"], pixdim=(\n",
     "            1.5, 1.5, 2.0), mode=(\"bilinear\", \"nearest\")),\n",
+    "\n",
+    "        FromMetaTensord(keys=[\"image\", \"label\"]),\n",
     "        ScaleIntensityRanged(\n",
     "            keys=[\"image\"], a_min=-57, a_max=164,\n",
     "            b_min=0.0, b_max=1.0, clip=True,\n",
@@ -351,7 +354,7 @@
     "            mode=('bilinear', 'nearest'),\n",
     "            prob=0.5,\n",
     "            spatial_size=(96, 96, 96),\n",
-    "            rotate_range=(np.pi/18, np.pi/18, np.pi/5),\n",
+    "            rotate_range=(np.pi / 18, np.pi / 18, np.pi / 5),\n",
     "            scale_range=(0.05, 0.05, 0.05)\n",
     "        ),\n",
     "        EnsureTyped(keys=[\"image\", \"label\"]),\n",
@@ -364,6 +367,7 @@
     "        Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
     "        Spacingd(keys=[\"image\", \"label\"], pixdim=(\n",
     "            1.5, 1.5, 2.0), mode=(\"bilinear\", \"nearest\")),\n",
+    "        FromMetaTensord(keys=[\"image\", \"label\"]),\n",
     "        ScaleIntensityRanged(\n",
     "            keys=[\"image\"], a_min=-57, a_max=164,\n",
     "            b_min=0.0, b_max=1.0, clip=True,\n",
@@ -371,9 +375,9 @@
     "        RandRotated(\n",
     "            keys=['image', 'label'],\n",
     "            mode=('bilinear', 'nearest'),\n",
-    "            range_x=np.pi/18,\n",
-    "            range_y=np.pi/18,\n",
-    "            range_z=np.pi/5,\n",
+    "            range_x=np.pi / 18,\n",
+    "            range_y=np.pi / 18,\n",
+    "            range_z=np.pi / 5,\n",
     "            prob=1.0,\n",
     "            padding_mode=('reflection', 'reflection'),\n",
     "        ),\n",
diff --git a/modules/transform_visualization.ipynb b/modules/transform_visualization.ipynb
index c64134e06d..7d6d6c94eb 100644
--- a/modules/transform_visualization.ipynb
+++ b/modules/transform_visualization.ipynb
@@ -76,6 +76,7 @@
     "    ScaleIntensityRanged,\n",
     "    Spacingd,\n",
     "    EnsureTyped,\n",
+    "    FromMetaTensord,\n",
     ")\n",
     "from monai.data import DataLoader, Dataset\n",
     "from monai.config import print_config\n",
@@ -214,6 +215,7 @@
     "    EnsureChannelFirstd(keys=[\"image\", \"label\"]),\n",
     "    Orientationd(keys=[\"image\", \"label\"], axcodes=\"PLS\"),\n",
     "    Spacingd(keys=[\"image\", \"label\"], pixdim=(1.5, 1.5, 2.0), mode=(\"bilinear\", \"nearest\")),\n",
+    "    FromMetaTensord(keys=[\"image\", \"label\"]),\n",
     "    ScaleIntensityRanged(keys=[\"image\"], a_min=-57, a_max=164, b_min=0.0, b_max=1.0, clip=True),\n",
     "    CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\"),\n",
     "    EnsureTyped(keys=[\"image\", \"label\"]),\n",