bump version, merge branch 'devel'

README.ipynb

@@ -97,6 +97,7 @@
     "\n",
     "- Siemens Biograph mMR resolution (~2mm) & dimensions (127, 344, 344)\n",
     "- PET/T1/T2/uMap intensities\n",
+    "  + PET defaults to FDG intensity ratios; could use e.g. Amyloid instead\n",
     "- randomised structure for PET/T1/T2\n",
     "  + $\\bm{\\theta} \\circ (\\bm{1} + \\gamma[2G_\\sigma(\\bm{\\rho}) - \\bm{1}])$\n",
     "    * $\\bm{\\rho} = rand(127, 344, 344) \\in [0, 1)$\n",
@@ -105,6 +106,18 @@
     "    * $\\bm{\\theta}$ is the PET or MR piecewise constant phantom"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# show region probability masks\n",
+    "PetClass = brainweb.FDG\n",
+    "label_probs = brainweb.get_label_probabilities(files[-1], labels=PetClass.all_labels)\n",
+    "volshow(label_probs[brainweb.trim_zeros_ROI(label_probs)], titles=PetClass.all_labels, frameon=False);"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -118,7 +131,7 @@
     "        f,\n",
     "        petNoise=1, t1Noise=0.75, t2Noise=0.75,\n",
     "        petSigma=1, t1Sigma=1, t2Sigma=1,\n",
-    "        PetClass=brainweb.FDG)"
+    "        PetClass=PetClass)"
    ]
   },
   {
@@ -128,13 +141,14 @@
    "outputs": [],
    "source": [
     "# show last subject\n",
-    "print(f)\n",
+    "print(files[-1])\n",
     "volshow([vol['PET' ][:, 100:-100, 100:-100],\n",
     "         vol['uMap'][:, 100:-100, 100:-100],\n",
     "         vol['T1'  ][:, 100:-100, 100:-100],\n",
     "         vol['T2'  ][:, 100:-100, 100:-100]],\n",
     "        cmaps=['hot', 'bone', 'Greys_r', 'Greys_r'],\n",
-    "        titles=[\"PET\", \"uMap\", \"T1\", \"T2\"]);"
+    "        titles=[\"PET\", \"uMap\", \"T1\", \"T2\"],\n",
+    "        frameon=False);"
    ]
   },
   {
@@ -172,9 +186,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {

README.rst

@@ -99,13 +99,26 @@ Convert raw image data:
 
 -  Siemens Biograph mMR resolution (~2mm) & dimensions (127, 344, 344)
 -  PET/T1/T2/uMap intensities
+
+   -  PET defaults to FDG intensity ratios; could use e.g. Amyloid instead
+
 -  randomised structure for PET/T1/T2
--  t (1 + g [2 G_sigma(r) - 1]), where
 
-   -  r = rand(127, 344, 344) in [0, 1),
-   -  Gaussian smoothing sigma = 1,
-   -  g = 1 for PET; 0.75 for MR, and
-   -  t = the PET or MR piecewise constant phantom
+   -  t (1 + g [2 G_sigma(r) - 1]), where
+
+      -  r = rand(127, 344, 344) in [0, 1),
+      -  Gaussian smoothing sigma = 1,
+      -  g = 1 for PET; 0.75 for MR, and
+      -  t = the PET or MR piecewise constant phantom
+
+.. code:: python
+
+    # show region probability masks
+    PetClass = brainweb.FDG
+    label_probs = brainweb.get_label_probabilities(files[-1], labels=PetClass.all_labels)
+    volshow(label_probs[brainweb.trim_zeros_ROI(label_probs)], titles=PetClass.all_labels, frameon=False);
+
+.. image:: https://raw.githubusercontent.com/casperdcl/brainweb/master/pmasks.png
 
 .. code:: python
 
@@ -116,7 +129,7 @@ Convert raw image data:
             f,
             petNoise=1, t1Noise=0.75, t2Noise=0.75,
             petSigma=1, t1Sigma=1, t2Sigma=1,
-            PetClass=brainweb.FDG)
+            PetClass=PetClass)
 
 .. code:: python
 
@@ -127,7 +140,8 @@ Convert raw image data:
              vol['T1'  ][:, 100:-100, 100:-100],
              vol['T2'  ][:, 100:-100, 100:-100]],
             cmaps=['hot', 'bone', 'Greys_r', 'Greys_r'],
-            titles=["PET", "uMap", "T1", "T2"]);
+            titles=["PET", "uMap", "T1", "T2"],
+            frameon=False);
 
 ::
 

brainweb/__init__.py

@@ -2,4 +2,4 @@
 from .utils import __all__  # NOQA
 __author__ = "Casper O. da Costa-Luis <casper.dcl@physics.org>"
 __licence__ = __license__ = "[MPLv2.0](https://www.mozilla.org/MPL/2.0)"
-__version__ = "1.5.2"
+__version__ = "1.6.0"