brainweb

BrainWeb: Multimodal models of 20 normal brains

https://github.com/casperdcl/brainweb

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BrainWeb: Multimodal models of 20 normal brains

README.ipynb

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    "# BrainWeb-based multimodal models of 20 normal brains\n",
    "\n",
    "This project was initially inspired by \"[BrainWeb: 20 Anatomical Models of 20 Normal Brains][RawData].\"\n",
    "\n",
    "However there are a number of generally useful tools, image processing & display functions included in this project. For example, this includes `volshow()` for interactive comparison of multiple 3D volumes, `get_file()` for caching data URLs, and `register()` for image coregistration.\n",
    "\n",
    "[![PyPI]][PyPI-target]|[![CI]][CI-target]|[![Quality]][Quality-target]|[![DOI]][DOI-target]|[![LICENCE]][LICENCE-target]\n",
    "-|-|-|-|-\n",
    "\n",
    "**Download and Preprocessing for PET-MR Simulations**\n",
    "\n",
    "This notebook will not re-download/re-process files if they already exist.\n",
    "\n",
    "- Output data\n",
    "    + `~/.brainweb/subject_*.npz`: dtype(shape): `float32(127, 344, 344)`\n",
    "\n",
    "- [Raw data source][RawData]\n",
    "    + `~/.brainweb/subject_*.bin.gz`: dtype(shape): `uint16(362, 434, 362)`\n",
    "\n",
    "- Install\n",
    "    + `pip install brainweb`\n",
    "\n",
    "----\n",
    "\n",
    "- Author: Casper da Costa-Luis <>\n",
    "- Date: 2017-2020\n",
    "- Licence: [MPLv2.0](https://www.mozilla.org/MPL/2.0)\n",
    "\n",
    "[RawData]: http://brainweb.bic.mni.mcgill.ca/brainweb/anatomic_normal_20.html\n",
    "[PyPI]: https://img.shields.io/pypi/v/brainweb.svg\n",
    "[PyPI-target]: https://pypi.org/project/brainweb\n",
    "[CI]: https://travis-ci.org/casperdcl/brainweb.svg?branch=master\n",
    "[CI-target]: https://travis-ci.org/casperdcl/brainweb\n",
    "[Quality]: https://api.codacy.com/project/badge/Grade/cdad13693b0141199c31d5b44c7ab185\n",
    "[Quality-target]: https://www.codacy.com/app/casper-dcl/brainweb\n",
    "[DOI]: https://zenodo.org/badge/DOI/10.5281/zenodo.3269888.svg\n",
    "[DOI-target]: https://doi.org/10.5281/zenodo.3269888\n",
    "[LICENCE]: https://img.shields.io/pypi/l/brainweb.svg?label=licence\n",
    "[LICENCE-target]: https://www.mozilla.org/MPL/2.0"
   ]
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  {
   "cell_type": "code",
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   "source": [
    "from __future__ import print_function, division\n",
    "%matplotlib notebook\n",
    "import brainweb\n",
    "from brainweb import volshow\n",
    "import numpy as np\n",
    "from os import path\n",
    "from tqdm.auto import tqdm\n",
    "import logging\n",
    "logging.basicConfig(level=logging.INFO)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Raw Data"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "# download\n",
    "files = brainweb.get_files()\n",
    "\n",
    "# read last file\n",
    "data = brainweb.load_file(files[-1])\n",
    "\n",
    "# show last subject\n",
    "print(files[-1])\n",
    "volshow(data, cmaps=['gist_ncar']);"
   ]
  },
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   "source": [
    "## Transform\n",
    "\n",
    "
$\\ifcsname bm\\endcsname\\else\\newcommand{\\bm}[1]{\\mathbf{#1}}\\fi$
\n",
    "Convert raw image data:\n",
    "\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",
    "    * Gaussian smoothing $\\sigma = 1$\n",
    "    * $\\gamma = \\left\\{\\matrix{1 & \\text{for PET}\\\\ 0.75 & \\text{for MR}}\\right.$\n",
    "    * $\\bm{\\theta}$ 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);"
   ]
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   "cell_type": "code",
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   "source": [
    "brainweb.seed(1337)\n",
    "\n",
    "for f in tqdm(files, desc=\"mMR ground truths\", unit=\"subject\"):\n",
    "    vol = brainweb.get_mmr_fromfile(\n",
    "        f,\n",
    "        petNoise=1, t1Noise=0.75, t2Noise=0.75,\n",
    "        petSigma=1, t1Sigma=1, t2Sigma=1,\n",
    "        PetClass=PetClass)"
   ]
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   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "# show last subject\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\"],\n",
    "        frameon=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add some lesions\n",
    "brainweb.seed(1337)\n",
    "im3d = brainweb.add_lesions(vol['PET'])\n",
    "volshow(im3d[:, 100:-100, 100:-100], cmaps=['hot']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bonus: use brute-force registration to transform\n",
    "#!pip install -U 'brainweb[register]'\n",
    "reg = brainweb.register(\n",
    "    data[:, ::-1], target=vol['PET'],\n",
    "    src_resolution=brainweb.Res.brainweb,\n",
    "    target_resolution=brainweb.Res.mMR)\n",
    "\n",
    "volshow({\n",
    "    \"PET\":    vol['PET'][:, 100:-100, 100:-100],\n",
    "    \"RawReg\": reg[       :, 100:-100, 100:-100],\n",
    "    \"T1\":     vol['T1' ][:, 100:-100, 100:-100],\n",
    "}, cmaps=['hot', 'gist_ncar', 'Greys_r'], ncols=3, tight_layout=5, figsize=(9.5, 3.5), frameon=False);"
   ]
  }
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