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carpentries-incubator · May 31, 2022 · 6e5ff74 · 6e5ff74
2 parents cd29c90 + 30b44e2
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diff --git a/_episodes/scanner-to-computer.md b/_episodes/scanner-to-computer.md
@@ -90,7 +90,7 @@ dcm2niix -help
 > > ## Solution
 > > ~~~
 > > mkdir -p ../data/dicom_examples/nii
-> > dcm2niix -z y -o ../data/dicom_examples/nii ../data/dicom_examples/0219191_mystudy-0219-1114/dcm
+> > dcm2niix -z y -o ../data/dicom_examples/nii ../data/dicom_examples/0219191_mystudy-0219-1114/
 > > ~~~
 > > {: .language-bash}
 > {: .solution}

diff --git a/code/01-scanner-to-computer.ipynb b/code/01-scanner-to-computer.ipynb
@@ -75,7 +75,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "solution2": "hidden",
+    "solution2_first": true
+   },
    "source": [
     "<b>EXERCISE:</b> Convert the Princeton DICOM data to NIfTI"
    ]
@@ -85,12 +88,23 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "solution2": "hidden"
+   },
+   "outputs": [],
    "source": [
     "%%bash\n",
     "\n",
     "mkdir -p ../data/dicom_examples/nii\n",
-    "dcm2niix -z y -o ../data/dicom_examples/nii ../data/dicom_examples/0219191_mystudy-0219-1114/dcm\n",
-    "rm -fr ../data/dicom_examples/nii/*scout*"
+    "dcm2niix \\\n",
+    "    -z y \\\n",
+    "    -o ../data/dicom_examples/nii \\\n",
+    "    ../data/dicom_examples/0219191_mystudy-0219-1114/dcm"
    ]
   }
  ],

diff --git a/code/02-anatomy-of-nifti.ipynb b/code/02-anatomy-of-nifti.ipynb
@@ -119,6 +119,17 @@
     "<b>EXERCISE:</b> Extract the value of <code>pixdim</code> from <code>t1_hdr</code>"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "solution2": "hidden"
+   },
+   "outputs": [],
+   "source": [
+    "t1_hdr['pixdim']"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -145,7 +156,7 @@
     "\n",
     "with open(\"../data/dicom_examples/nii/dcm_anat_ses-01_T1w_20190219111436_5.json\", \"r\") as f:\n",
     "    t1_metadata = json.load(f)\n",
-    "    \n",
+    "\n",
     "t1_metadata"
    ]
   },
@@ -163,6 +174,17 @@
     "<b>EXERCISE:</b> Extract the value of <code>SliceThickness</code> from <code>t1_metadata</code> similar to how you did previously for <code>t1_hdr</code>."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "solution2": "hidden"
+   },
+   "outputs": [],
+   "source": [
+    "t1_metadata[\"SliceThickness\"]"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -218,6 +240,17 @@
     "<b>EXERCISE:</b> Let's check out some *attributes* of the array. How can we see the number of dimensions in the <code>t1_data</code> array? What about the how big each dimension is (shape)? Once again, all of the attributes of the array can be seen by typing `t1_data.` and <kbd>Tab</kbd>."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "solution2": "hidden"
+   },
+   "outputs": [],
+   "source": [
+    "t1_data.ndim"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -384,7 +417,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "solution2": "hidden"
+   },
    "outputs": [],
    "source": [
     "y_slice = t1_data[:, 79, :]"
@@ -400,7 +435,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "solution2": "hidden"
+   },
    "outputs": [],
    "source": [
     "z_slice = t1_data[:, :, 99]"
@@ -419,7 +456,7 @@
    "source": [
     "## Visualizing\n",
     "\n",
-    "We previously inspected the signal intensity of the voxel at coordinates (10,20,3). Let's see what out data looks like when we slice it at this location. We've already indexed the data at each x, y, and z axis. Let's use `matplotlib`."
+    "We previously inspected the signal intensity of the voxel at coordinates (10,20,3). Let's see what out data looks like when we slice it at this location. We've already indexed the data at each x, y, and z axis. Let's use `matplotlib`, Python's standard plotting library."
    ]
   },
   {
@@ -449,6 +486,24 @@
     "plotting.view_img(t1_img, black_bg=True, colorbar=False, cmap=\"gray\", symmetric_cmap=False, vmin=0)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An upgrade from `matplotlib` is `nilearn`, which provides several advanced plotting features for neuroimaging data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from nilearn import plotting\n",
+    "\n",
+    "plotting.view_img(t1_img, black_bg=True, colorbar=False, cmap=\"gray\", symmetric_cmap=False, vmin=0)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

diff --git a/code/03-data-organization-bids.ipynb b/code/03-data-organization-bids.ipynb
@@ -15,7 +15,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "solution2": "hidden",
+    "solution2_first": true
+   },
    "source": [
     "<b>EXERCISE:</b> Let's take a look at the rest of our NIfTI dataset from the last lesson. Imagine you are a collaborator who's currently gotten your hands on this dataset for an analysis. What are some immediate questions you might have that aren't apparent at a first glance?\n",
     "\n",
@@ -44,7 +47,9 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "solution2": "hidden"
+   },
    "source": [
     "- How many subjects were scanned?\n",
     "- What are the demographics of the participants?\n",
@@ -82,7 +87,7 @@
    "source": [
     "## Structure\n",
     "\n",
-    "The current spec https://bids-specification.readthedocs.io/en/stable/\n"
+    "The current BIDS specification is [documented online](https://bids-specification.readthedocs.io/en/stable/).\n"
    ]
   },
   {
@@ -204,6 +209,51 @@
     "  -o ../data/dicom_examples/dcm2bids"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "solution2": "hidden",
+    "solution2_first": true
+   },
+   "source": [
+    "`Dcm2Bids` uses a configuration file in order to match scans with the appropriate BIDS filename. We will create this file together but it can also be revealed by clicking the toggle below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "solution2": "hidden"
+   },
+   "source": [
+    "{\n",
+    "    \"descriptions\": [\n",
+    "        {\n",
+    "        \"dataType\": \"anat\",\n",
+    "        \"modalityLabel\": \"T1w\",\n",
+    "        \"criteria\": { \"SeriesDescription\": \"anat_ses-01_T1w\" }\n",
+    "        },\n",
+    "        {\n",
+    "        \"dataType\": \"func\",\n",
+    "        \"modalityLabel\": \"bold\",\n",
+    "        \"customLabels\": \"task-sound\",\n",
+    "        \"criteria\": { \"SeriesDescription\": \"func_ses-01_task-sound*\" }\n",
+    "        },\n",
+    "        {\n",
+    "        \"dataType\": \"func\",\n",
+    "        \"modalityLabel\": \"bold\",\n",
+    "        \"customLabels\": \"task-story\",\n",
+    "        \"criteria\": { \"SeriesDescription\": \"func_ses-01_task-story*\" }\n",
+    "        },\n",
+    "        {\n",
+    "        \"dataType\": \"func\",\n",
+    "        \"modalityLabel\": \"bold\",\n",
+    "        \"customLabels\": \"task-faces\",\n",
+    "        \"criteria\": { \"SeriesDescription\": \"func_ses-01_task-faces*\" }\n",
+    "        }\n",
+    "    ]\n",
+    "}"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -216,7 +266,7 @@
     "dcm2bids \\\n",
     "  -d ../data/dicom_examples/0219191_mystudy-0219-1114/dcm \\\n",
     "  -p 01 -s 01 \\\n",
-    "  -c ../data/dicom_examples/dcm2bids/code/config.json \\\n",
+    "  -c ../data/dicom_examples/dcm2bids/code/bids_config.json \\\n",
     "  -o ../data/dicom_examples/dcm2bids/rawdata"
    ]
   },