Revert to dti notebook updates

code/diffusion_tensor_imaging/diffusion_tensor_imaging.ipynb

@@ -23,7 +23,7 @@
     "\n",
     "Tensors are represented by ellipsoids characterized by calculated eigenvalues ($\\lambda_1, \\lambda_2, \\lambda_3$) and eigenvectors ($\\epsilon_1, \\epsilon_2, \\epsilon_3$) from the previously described matrix. Eigenvalues and eigenvectors are normally sorted in descending magnitude.\n",
     "\n",
-    "![Diffusion Tensor](DiffusionTensor.png) <br>\n",
+    "![Diffusion Tensor](../../fig/diffusion_tensor_imaging/DiffusionTensor.png) <br>\n",
     "Adapated from Jellison _et al._, 2004\n",
     "\n",
     "In the following example, we show how to model your diffusion datasets. It should be noted that there are a number of diffusion models and many of these are implemented in `Dipy`. However, for the purposes of this tutorial, we will be focus on the tensor model.\n",
@@ -436,7 +436,7 @@
    "source": [
     "Another way of viewing the tensors is to visualize the diffusion tensor in each imaging voxel with colour encoding (we will refer you to the [`Dipy` documentation](https://dipy.org/tutorials/) for the steps to perform this type of visualization as it can be memory intensive). Below is an example image of such tensor visualization.\n",
     "\n",
-    "![Tensor Visualization](TensorViz.png)"
+    "![Tensor Visualization](../../fig/diffusion_tensor_imaging/TensorViz.png)"
    ]
   },
   {
@@ -447,21 +447,12 @@
     "\n",
     "DTI is only one of many models and is one of the simplest models available for modelling diffusion. While it is used for many studies, there are also some drawbacks (eg. ability to distinguish multiple fibre orientations in one imaging voxel). Some examples can be seen below! \n",
     "\n",
-    "![fiber_configurations](FiberConfigurations.png)\n",
+    "![fiber_configurations](../../fig/diffusion_tensor_imaging/FiberConfigurations.png)\n",
     "\n",
-    "Sourced from: Sotiropoulos and Zalesky. (2017). Building connectomes using diffusion MRI: why, how, and but. NMR in Biomedicine. 4(32). e3752. 10.1002/nbm.3752. \n",
+    "Sourced from: Sotiropolous and Zalewsky. (2017). Building connectomes using diffusion MRI: why, how, and but. NMR in Biomedicine. 4(32). e3752. 10.1002/nbm.3752. \n",
     "\n",
     "Though other models are outside the scope of this lesson, we recommend looking into some of the pros and cons of each model (listed previously) to choose one best suited for your data! "
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s"
-   ]
   }
  ],
  "metadata": {

code/diffusion_tensor_imaging/solutions/diffusion_tensor_imaging_solutions.ipynb

@@ -259,7 +259,7 @@
     "\n",
     "![fiber_configurations](../../../fig/diffusion_tensor_imaging/FiberConfigurations.png)\n",
     "\n",
-    "Sourced from: Sotiropoulos and Zalesky. (2017). Building connectomes using diffusion MRI: why, how, and but. NMR in Biomedicine. 4(32). e3752. 10.1002/nbm.3752. \n",
+    "Sourced from: Sotiropolous and Zalewsky. (2017). Building connectomes using diffusion MRI: why, how, and but. NMR in Biomedicine. 4(32). e3752. 10.1002/nbm.3752. \n",
     "\n",
     "Though other models are outside the scope of this lesson, we recommend looking into some of the pros and cons of each model (listed previously) to choose one best suited for your data! "
    ]