DTIMask.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nilearn\n",
    "import nilearn.masking\n",
    "import nilearn.image\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_volumes(volumes_path, prefix):\n",
    "    \"\"\"\n",
    "    Save volumes to disk as image slices\n",
    "    volumes_path: string\n",
    "        Path to voluemes that are of shape (Nx, Ny, Nz, NVolumes)\n",
    "    prefix: string\n",
    "        Prefix to use when saving frames\n",
    "    \"\"\"\n",
    "    img = nilearn.image.load_img(volumes_path)\n",
    "    I = img.get_fdata()\n",
    "    if len(I.shape) == 3:\n",
    "        I = I[:, :, :, None]\n",
    "    vmin = np.min(I)\n",
    "    vmax = np.max(I)\n",
    "    plt.figure(figsize=(6, 6))\n",
    "    for i in range(I.shape[-1]):\n",
    "        for z in range(I.shape[-2]):\n",
    "            Ii = I[:, :, z, i]\n",
    "            plt.clf()\n",
    "            plt.imshow(Ii, cmap='magma_r', vmin=vmin, vmax=vmax)\n",
    "            plt.title(\"Volume {} Slice {}\".format(i, z))\n",
    "            plt.colorbar()\n",
    "            plt.savefig(\"{}_{}_{}.png\".format(prefix, i, z))\n",
    "\n",
    "            \n",
    "def save_masked_regions(volumes_path, mask_path, prefix):\n",
    "    \"\"\"\n",
    "    Apply masks to volumes and save to disk\n",
    "    Parameters\n",
    "    ----------\n",
    "    volumes_path: string\n",
    "        File path to volumes that are of shape (Nx, Ny, Nz, NVolumes)\n",
    "    mask_path: string\n",
    "        File path to masks that are of shape (Nx, Ny, Nz)\n",
    "    prefix: string\n",
    "        Prefix to use when saving\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    list of nilearn.image\n",
    "        The masked image objects\n",
    "    \"\"\"\n",
    "    img = nilearn.image.load_img(volumes_path)\n",
    "    mask = nilearn.image.load_img(mask_path)\n",
    "    I = img.get_fdata()\n",
    "    M = mask.get_fdata()\n",
    "    if len(I) > len(M):\n",
    "        shape = tuple(list(M.shape) + [1])\n",
    "        M = np.reshape(M, shape)\n",
    "    vals = np.unique(M)\n",
    "    vals = vals[vals > 0]\n",
    "    imgs_masked = []\n",
    "    for v in vals:\n",
    "        Iv = np.array(I, dtype=np.int16)\n",
    "        Iv[np.abs(M - v) > 0] = 0\n",
    "        # Copy over affine and header\n",
    "        imgv = nilearn.image.new_img_like(img, Iv, img.affine, True)\n",
    "        filename = \"{}{}.nii\".format(prefix, int(v))\n",
    "        imgv.to_filename(filename)\n",
    "        imgs_masked.append(imgv)\n",
    "    return imgs_masked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "maskfiles = glob.glob(\"*labels.nii\")\n",
    "prefixes = [f.split(\"_labels.nii\")[0] for f in maskfiles]\n",
    "datafiles = [\"{}_sdc_GR_TV.nii\".format(p) for p in prefixes]\n",
    "for volume_file, mask_file, prefix in zip(datafiles, maskfiles, prefixes):\n",
    "    save_masked_regions(volume_file, mask_file, prefix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = nilearn.image.load_img(\"JH02_PQS1.nii\")\n",
    "img2 = nilearn.image.load_img(\"JH02_PQS_sdc_GR_TV.nii\")\n",
    "print(img.header)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}