{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading DICOM Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('/home/gpuvmadm/PyTomography/src')\n",
    "import os\n",
    "from pytomography.projections import ForwardProjectionNet, BackProjectionNet\n",
    "from pytomography.metadata import ObjectMeta, ImageMeta, PSFMeta\n",
    "from pytomography.mappings import SPECTAttenuationNet, SPECTPSFNet\n",
    "from pytomography.algorithms import OSEMOSL\n",
    "from pytomography.io import dicom_projections_to_data, dicom_CT_to_data\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "import pydicom\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In DICOM format, it is typically the case that SPECT tomography is contained in a `.dcm` file, and CT data is contained in a sequence of `.dcm` files: each file corresponding to a different axial slice. To open this data for reconstruction in pytomography, we need a single SPECT filepath and multiple CT filepaths."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_CT = '/home/gpuvmadm/PyTomography/test_files/ohif_CT'\n",
    "files_CT = [os.path.join(path_CT, file) for file in os.listdir(path_CT)]\n",
    "file_NM = '/home/gpuvmadm/PyTomography/test_files/ohif_NM.dcm'"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can get the object/image metadata and corresponding projections using the `dicom_projections_to_data` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 64, 128, 128])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "object_meta, image_meta, projections = dicom_projections_to_data(file_NM)\n",
    "projections.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note the size of the projections: we now have 2 energy windows corresponding to this SPECT scan, so the batch dimension now stores each scan."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can load the CT data using the `dicom_CT_to_data`. It takes in both the CT file path and SPECT file path, and returns 3D tensor of dimensions `[Lx, Ly, Lz]` containing the CT array. It uses the tomography data from `file_NM` to align the CT data with the SPECT projections using `affine` transformations and resampling:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "CT = dicom_CT_to_data(files_CT, file_NM).unsqueeze(dim=0)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that up to this point, we've used pytomography functions to load the data. Sometimes, however, DICOM files may not follow the strict rules for storing SPECT/CT data. In this case, as long as you can get (i) `object_meta`, (ii) `image_meta`, (iii) `projections` and (iv) `CT` from the corresponding DICOM files, you are set. The image metadata requires the angles (and if you want to use PSF correction, also the radii) of each projection. This can typically be found in the `ds.RotationInformationSequence` and `ds.DetectorInformationSequence` attributes of a loaded dicom file using `pydicom.read_file(filename)`. For more information, see `pytomography.io.dicom`: you can use similar functionality to load your own data. \n",
    "\n",
    "It's also important that the CT data is aligned with the projections. This can be accomplished by using affine transformations: for more information, see functionality in `pytomography.io.dicom`. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As usual, it is always good practice to ensure the CT and projections are aligned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(1, 4, figsize=(14,6))\n",
    "plt.subplot(141)\n",
    "plt.pcolormesh(CT[0].sum(axis=1).T, cmap='Greys_r')\n",
    "plt.title('CT; Coronal View')\n",
    "plt.subplot(142)\n",
    "plt.pcolormesh(projections[0][0].T, cmap='nipy_spectral')\n",
    "plt.title(r'Spect; $\\beta=0^{\\circ}$')\n",
    "plt.subplot(143)\n",
    "plt.pcolormesh(CT[0].sum(axis=0).T, cmap='Greys_r')\n",
    "plt.title('CT; Sagittal View')\n",
    "plt.subplot(144)\n",
    "plt.pcolormesh(projections[0][16].T, cmap='nipy_spectral')\n",
    "plt.title(r'Spect; $\\beta=90^{\\circ}$')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Note the CT scan is cut-off; the detector did not cover the full patient during the scan"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use this information to create an `OSEMNet` object, which will be used for ordered subset expectation maximum reconstruction.\n",
    "\n",
    "$$f_i^{(n+1)} = \\frac{f_i^{(n)}}{\\sum_j c_{ij} + \\beta \\frac{\\partial V}{\\partial f_r}|_{f_i=f_i^{(n)}}} \\sum_j c_{ij}\\frac{g_j}{\\sum_i c_{ij}f_i^{(n)}} $$\n",
    "\n",
    "First we need to give all required information for the forward projection operator $\\sum_i c_{ij} a_i$ and the back projection operator $\\sum_j c_{ij} b_j$ work.\n",
    "\n",
    "* This amounts to defining the type of modeling we want to use. First, let's get the network that models attenuation corrrection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "ct_net = SPECTAttenuationNet(CT, device=device)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For quantitative reconstruction, we'll want to also model PSF blurring. This requires knowing the `collimator_slope` and `collimator_intercept` parameters of the scanner. The assumption is that the PSF function is Gaussian with width $\\sigma$ dependent on the distance from the detector: \n",
    "$\\sigma =$ `collimator_slope` $\\cdot d + $ `collimator_intercept`. This information depends on the detector used: in particular, the collimator dimensions. These may be found in a data sheet for your particular scanner. It can also be determined by scanning a point source at various detector distances $d$, fitting each projection to a Gaussian profile and determining the width $\\sigma$, and then fitting $d$ vs. $\\sigma$ to a linear curve: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "psf_meta = PSFMeta(collimator_slope = 0.02, collimator_intercept = 0.005)\n",
    "psf_net = SPECTPSFNet(psf_meta, device)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we make sure to include these correction networks in the forward/back projection networks. It's important that CT correction occurs first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "fp_net = ForwardProjectionNet(obj2obj_nets=[ct_net, psf_net],\n",
    "                              im2im_nets=[],\n",
    "                              object_meta=object_meta,\n",
    "                              image_meta=image_meta,\n",
    "                              device=device)\n",
    "bp_net = BackProjectionNet(obj2obj_nets=[ct_net, psf_net],\n",
    "                           im2im_nets=[],\n",
    "                           object_meta=object_meta,\n",
    "                           image_meta=image_meta,\n",
    "                           device=device)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we create the OSEM net:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "osem_net = OSEMOSL(image = projections,\n",
    "                   forward_projection_net=fp_net,\n",
    "                   back_projection_net=bp_net)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we reconstruct:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 128, 128, 128])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reconstructed_object = osem_net(n_iters=2, n_subsets=8)\n",
    "reconstructed_object.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have two objects (one for each energy window). We can look at each energy window seperately:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "main_energy_window = reconstructed_object[0].cpu().numpy()\n",
    "lower_energy_window = reconstructed_object[1].cpu().numpy()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at an axial slice:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(1,3,figsize=(12,3))\n",
    "plt.subplot(131)\n",
    "plt.pcolormesh(main_energy_window[:,:,70].T, cmap='nipy_spectral')\n",
    "plt.colorbar()\n",
    "plt.axis('off')\n",
    "plt.title('Main Energy Window')\n",
    "plt.subplot(132)\n",
    "plt.pcolormesh(lower_energy_window[:,:,70].T, cmap='nipy_spectral')\n",
    "plt.colorbar()\n",
    "plt.axis('off')\n",
    "plt.title('Lower Energy Window')\n",
    "plt.subplot(133)\n",
    "plt.pcolormesh(CT[0,:,:,70].T, cmap='Greys_r')\n",
    "plt.colorbar()\n",
    "plt.axis('off')\n",
    "plt.title('$\\mu$ (CT scan)')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "torch",
   "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": "3.9.16"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "324f5aa1485c6e81c0125c86ad1340c603dab08bc11babd97bd730cfafa2ec4a"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
