{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d42c0672",
   "metadata": {
    "papermill": {
     "duration": 0.006107,
     "end_time": "2025-04-30T22:20:17.841505",
     "exception": false,
     "start_time": "2025-04-30T22:20:17.835398",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# DICOM Introduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "18553297",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-30T22:20:17.854095Z",
     "iopub.status.busy": "2025-04-30T22:20:17.853600Z",
     "iopub.status.idle": "2025-04-30T22:20:26.157989Z",
     "shell.execute_reply": "2025-04-30T22:20:26.157201Z"
    },
    "papermill": {
     "duration": 8.312371,
     "end_time": "2025-04-30T22:20:26.159650",
     "exception": false,
     "start_time": "2025-04-30T22:20:17.847279",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "from pytomography.io.SPECT import dicom\n",
    "from pytomography.transforms.SPECT import SPECTAttenuationTransform, SPECTPSFTransform\n",
    "from pytomography.algorithms import OSEM\n",
    "from pytomography.projectors.SPECT import SPECTSystemMatrix\n",
    "from pytomography.likelihoods import PoissonLogLikelihood\n",
    "from pytomography.utils import print_collimator_parameters\n",
    "import matplotlib.pyplot as plt\n",
    "import pydicom\n",
    "import shutil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a9388a8e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-30T22:20:26.172156Z",
     "iopub.status.busy": "2025-04-30T22:20:26.171755Z",
     "iopub.status.idle": "2025-04-30T22:20:26.175307Z",
     "shell.execute_reply": "2025-04-30T22:20:26.174748Z"
    },
    "papermill": {
     "duration": 0.010201,
     "end_time": "2025-04-30T22:20:26.176155",
     "exception": false,
     "start_time": "2025-04-30T22:20:26.165954",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# change this to where you downloaded the SPECT tutorial data\n",
    "PATH = '/mnt/mydisk2/pytomo_tutorial_data/SPECT'\n",
    "PATH = os.path.join(PATH, 'Lu177-NEMA-SymT2')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcaad5aa",
   "metadata": {
    "papermill": {
     "duration": 0.005126,
     "end_time": "2025-04-30T22:20:26.186668",
     "exception": false,
     "start_time": "2025-04-30T22:20:26.181542",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Part 1: Opening Data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "272f9ffa",
   "metadata": {
    "papermill": {
     "duration": 0.005353,
     "end_time": "2025-04-30T22:20:26.197235",
     "exception": false,
     "start_time": "2025-04-30T22:20:26.191882",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "First we specify the required filepaths to open the SPECT scan (`file_NM`) and the CT data files (`files_CT`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eab43513",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-30T22:20:26.209221Z",
     "iopub.status.busy": "2025-04-30T22:20:26.208826Z",
     "iopub.status.idle": "2025-04-30T22:20:26.212542Z",
     "shell.execute_reply": "2025-04-30T22:20:26.211932Z"
    },
    "papermill": {
     "duration": 0.011022,
     "end_time": "2025-04-30T22:20:26.213511",
     "exception": false,
     "start_time": "2025-04-30T22:20:26.202489",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# initialize the `path`` variable below to specify the location of the required data\n",
    "path_CT = os.path.join(PATH, 'CT')\n",
    "files_CT = [os.path.join(path_CT, file) for file in os.listdir(path_CT)]\n",
    "file_NM = os.path.join(PATH, 'projection_data.dcm')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef816082",
   "metadata": {
    "papermill": {
     "duration": 0.005023,
     "end_time": "2025-04-30T22:20:26.223590",
     "exception": false,
     "start_time": "2025-04-30T22:20:26.218567",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The metadata for object space and projection space can be obtained from the projection data file using `dicom.get_metadata`\n",
    "\n",
    "* This contains informaiton such as the voxel size, number of angles, scanner radius, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1a38b044",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-30T22:20:26.234770Z",
     "iopub.status.busy": "2025-04-30T22:20:26.234359Z",
     "iopub.status.idle": "2025-04-30T22:20:26.663128Z",
     "shell.execute_reply": "2025-04-30T22:20:26.662407Z"
    },
    "papermill": {
     "duration": 0.436041,
     "end_time": "2025-04-30T22:20:26.664744",
     "exception": false,
     "start_time": "2025-04-30T22:20:26.228703",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "object_meta, proj_meta = dicom.get_metadata(file_NM, index_peak=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "004b3e30",
   "metadata": {
    "papermill": {
     "duration": 0.005115,
     "end_time": "2025-04-30T22:20:26.676102",
     "exception": false,
     "start_time": "2025-04-30T22:20:26.670987",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We can get the photopeak projection data from the DICOM file provided we know the indices of each of the energy windows. We can obtain that as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "25c71173",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-30T22:20:26.687473Z",
     "iopub.status.busy": "2025-04-30T22:20:26.687052Z",
     "iopub.status.idle": "2025-04-30T22:20:26.698388Z",
     "shell.execute_reply": "2025-04-30T22:20:26.697726Z"
    },
    "papermill": {
     "duration": 0.018015,
     "end_time": "2025-04-30T22:20:26.699305",
     "exception": false,
     "start_time": "2025-04-30T22:20:26.681290",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index 0:   Name: \"Window Group A\", Energies: [187.19999694824keV, 228.80000305176keV]\n",
      "Index 1:   Name: \"Window Group B\", Energies: [166.39999389648keV, 187.19999694824keV]\n",
      "Index 2:   Name: \"Window Group C\", Energies: [228.80000305176keV, 249.60000610352keV]\n",
      "Index 3:   Name: \"Window Group D\", Energies: [101.69999694824keV, 124.30000305176keV]\n",
      "Index 4:   Name: \"Window Group E\", Energies: [85.879997253418keV, 101.69999694824keV]\n",
      "Index 5:   Name: \"Window Group F\", Energies: [124.30000305176keV, 146.89999389648keV]\n"
     ]
    }
   ],
   "source": [
    "dicom.print_energy_window_info(file_NM)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b0b23350",
   "metadata": {
    "papermill": {
     "duration": 0.005539,
     "end_time": "2025-04-30T22:20:26.709995",
     "exception": false,
     "start_time": "2025-04-30T22:20:26.704456",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "In this case, since we are specifically imaging the 208 keV peak, we specify `index_peak=0`, so it only returns the projections corresponding to the 187-228 energy window above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0bab5813",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-30T22:20:26.721264Z",
     "iopub.status.busy": "2025-04-30T22:20:26.720858Z",
     "iopub.status.idle": "2025-04-30T22:20:26.825345Z",
     "shell.execute_reply": "2025-04-30T22:20:26.824732Z"
    },
    "papermill": {
     "duration": 0.111219,
     "end_time": "2025-04-30T22:20:26.826323",
     "exception": false,
     "start_time": "2025-04-30T22:20:26.715104",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([96, 128, 128])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "photopeak = dicom.get_projections(file_NM, index_peak=0)\n",
    "photopeak.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23f8b4ce",
   "metadata": {
    "papermill": {
     "duration": 0.005423,
     "end_time": "2025-04-30T22:20:26.838028",
     "exception": false,
     "start_time": "2025-04-30T22:20:26.832605",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The shape of the photopeak is $(N_{angles}, \\text{Width}, \\text{Height})$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b811f6b8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-30T22:20:26.849732Z",
     "iopub.status.busy": "2025-04-30T22:20:26.849254Z",
     "iopub.status.idle": "2025-04-30T22:20:26.986818Z",
     "shell.execute_reply": "2025-04-30T22:20:26.986231Z"
    },
    "papermill": {
     "duration": 0.144459,
     "end_time": "2025-04-30T22:20:26.987707",
     "exception": false,
     "start_time": "2025-04-30T22:20:26.843248",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x78ed2e5a8a70>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 700x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,6))\n",
    "plt.imshow(photopeak[0].cpu().T, cmap='nipy_spectral', origin='lower')\n",
    "plt.axis('off')\n",
    "plt.colorbar(label='Counts')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "233f5cb3",
   "metadata": {
    "papermill": {
     "duration": 0.005477,
     "end_time": "2025-04-30T22:20:26.999186",
     "exception": false,
     "start_time": "2025-04-30T22:20:26.993709",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Scatter projections can be estimated using the triple energy window function `dicom_get_scatterTEW`. One needs to specify the index of the peak, and lower/upper energy windows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5c653ab8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-30T22:20:27.011152Z",
     "iopub.status.busy": "2025-04-30T22:20:27.010742Z",
     "iopub.status.idle": "2025-04-30T22:20:27.147735Z",
     "shell.execute_reply": "2025-04-30T22:20:27.146994Z"
    },
    "papermill": {
     "duration": 0.144707,
     "end_time": "2025-04-30T22:20:27.149343",
     "exception": false,
     "start_time": "2025-04-30T22:20:27.004636",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "scatter = dicom.get_energy_window_scatter_estimate(file_NM, index_peak=0, index_lower=1, index_upper=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f057acd",
   "metadata": {
    "papermill": {
     "duration": 0.005449,
     "end_time": "2025-04-30T22:20:27.161502",
     "exception": false,
     "start_time": "2025-04-30T22:20:27.156053",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Part 2: Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8153f02b",
   "metadata": {
    "papermill": {
     "duration": 0.00557,
     "end_time": "2025-04-30T22:20:27.172582",
     "exception": false,
     "start_time": "2025-04-30T22:20:27.167012",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Attenuation Transform"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd7f5ffc",
   "metadata": {
    "papermill": {
     "duration": 0.013323,
     "end_time": "2025-04-30T22:20:27.191399",
     "exception": false,
     "start_time": "2025-04-30T22:20:27.178076",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The attenuation transform is used to \"correct\" for the fact that some photons get atteunated in the patient before they reach the detector. PyTomography has functionality to do attenuation correction provided either (i) an argument `attenuation_map=...` OR (ii) an argument `filepath=...` is provided to the `SPECTAttenuationTransform`.\n",
    "\n",
    "* `attenuation_map` must be aligned with the SPECT projection data and have units of cm $^{-1}$ .\n",
    "* `filepath` specifies the directory of some CT scan which will be used for attenuation correction. PyTomography has functionality to align this with the SPECT projections and convert to units of cm $^{-1}$.\n",
    "\n",
    "In this case, since we're reconstructing DICOM data, it's easier to use `filepath` than to manually align/rescale the CT data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "66f048d2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-30T22:20:27.215588Z",
     "iopub.status.busy": "2025-04-30T22:20:27.215095Z",
     "iopub.status.idle": "2025-04-30T22:20:27.631003Z",
     "shell.execute_reply": "2025-04-30T22:20:27.630243Z"
    },
    "papermill": {
     "duration": 0.433929,
     "end_time": "2025-04-30T22:20:27.632809",
     "exception": false,
     "start_time": "2025-04-30T22:20:27.198880",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "att_transform = SPECTAttenuationTransform(filepath=files_CT)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "675f9e64",
   "metadata": {
    "papermill": {
     "duration": 0.005471,
     "end_time": "2025-04-30T22:20:27.645505",
     "exception": false,
     "start_time": "2025-04-30T22:20:27.640034",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "If we want to view the attenuation map, we need to *configure* the `att_transform` so that (i) the CT data can be aligned with SPECT projection data and (ii) the CT data can be converted to attenuation coefficients.\n",
    "\n",
    "* *Note*: This is not required if you don't want to view the attenuation map. Later, when the system matrix is built, it automatically configures all the transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9632593e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-30T22:20:27.657980Z",
     "iopub.status.busy": "2025-04-30T22:20:27.657538Z",
     "iopub.status.idle": "2025-04-30T22:20:29.650524Z",
     "shell.execute_reply": "2025-04-30T22:20:29.649519Z"
    },
    "papermill": {
     "duration": 2.000881,
     "end_time": "2025-04-30T22:20:29.652160",
     "exception": false,
     "start_time": "2025-04-30T22:20:27.651279",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Given photopeak energy 208.0 keV and CT energy 130 keV from the CT DICOM header, the HU->mu conversion from the following configuration is used: 208.0 keV SPECT energy, 130 keV CT energy, and scanner model symbiat2\n"
     ]
    }
   ],
   "source": [
    "att_transform.configure(object_meta, proj_meta)\n",
    "attenuation_map = att_transform.attenuation_map"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c41ac6a9",
   "metadata": {
    "papermill": {
     "duration": 0.005841,
     "end_time": "2025-04-30T22:20:29.664430",
     "exception": false,
     "start_time": "2025-04-30T22:20:29.658589",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Viewing the attenuation map allows for a consistency check to ensure that it's properly aligned with the projection data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3e8e4c05",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-30T22:20:29.677244Z",
     "iopub.status.busy": "2025-04-30T22:20:29.676758Z",
     "iopub.status.idle": "2025-04-30T22:20:29.681297Z",
     "shell.execute_reply": "2025-04-30T22:20:29.680744Z"
    },
    "papermill": {
     "duration": 0.012104,
     "end_time": "2025-04-30T22:20:29.682271",
     "exception": false,
     "start_time": "2025-04-30T22:20:29.670167",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "sample_slice = attenuation_map.cpu()[:,70].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "699e1dc0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-30T22:20:29.694999Z",
     "iopub.status.busy": "2025-04-30T22:20:29.694787Z",
     "iopub.status.idle": "2025-04-30T22:20:30.140913Z",
     "shell.execute_reply": "2025-04-30T22:20:30.140266Z"
    },
    "papermill": {
     "duration": 0.453926,
     "end_time": "2025-04-30T22:20:30.141978",
     "exception": false,
     "start_time": "2025-04-30T22:20:29.688052",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x78ed2c33f6e0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 700x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(1,2,figsize=(7,3))\n",
    "plt.subplot(121)\n",
    "plt.title('Projection Data')\n",
    "plt.imshow(photopeak[0].cpu().T, cmap='nipy_spectral', origin='lower')\n",
    "plt.axis('off')\n",
    "plt.colorbar(label='Counts')\n",
    "plt.subplot(122)\n",
    "plt.title('From CT Slices')\n",
    "plt.imshow(sample_slice, cmap='Greys_r', origin='lower', interpolation='Gaussian')\n",
    "plt.axis('off')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a06b768",
   "metadata": {
    "papermill": {
     "duration": 0.006164,
     "end_time": "2025-04-30T22:20:30.155219",
     "exception": false,
     "start_time": "2025-04-30T22:20:30.149055",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### PSF Modeling"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b21aeb9b",
   "metadata": {
    "papermill": {
     "duration": 0.005942,
     "end_time": "2025-04-30T22:20:30.167137",
     "exception": false,
     "start_time": "2025-04-30T22:20:30.161195",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "PSF modeling requires knowing the parameters of the collimators used for the scan. A list of all supported collimator types can be determined by uncommenting the following line and running it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5f310bef",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-30T22:20:30.180240Z",
     "iopub.status.busy": "2025-04-30T22:20:30.179814Z",
     "iopub.status.idle": "2025-04-30T22:20:30.182746Z",
     "shell.execute_reply": "2025-04-30T22:20:30.182188Z"
    },
    "papermill": {
     "duration": 0.010669,
     "end_time": "2025-04-30T22:20:30.183734",
     "exception": false,
     "start_time": "2025-04-30T22:20:30.173065",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#print_collimator_parameters()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "358a99e4",
   "metadata": {
    "papermill": {
     "duration": 0.070462,
     "end_time": "2025-04-30T22:20:30.260481",
     "exception": false,
     "start_time": "2025-04-30T22:20:30.190019",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "In our case, we know that this scan was performed on a Siemens Symbia scanner with medium energy collimators and 3/8'' crystals\n",
    "* There is an option to use intrinsic resolution of the crystals of the scanner (if you know it); this has a minor impact on the reconstruction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7c403021",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-30T22:20:30.274105Z",
     "iopub.status.busy": "2025-04-30T22:20:30.273723Z",
     "iopub.status.idle": "2025-04-30T22:20:30.280445Z",
     "shell.execute_reply": "2025-04-30T22:20:30.279888Z"
    },
    "papermill": {
     "duration": 0.014514,
     "end_time": "2025-04-30T22:20:30.281390",
     "exception": false,
     "start_time": "2025-04-30T22:20:30.266876",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kernel_dimensions = 2D\n",
      "min_sigmas = 3\n",
      "shape = gaussian\n",
      "sigma_fit_params = [np.float64(0.03161982627275948), np.float64(0.1248503046423388), np.float64(0.16137114205472364)]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "collimator_name = 'SY-ME'\n",
    "energy_kev = 208 #keV\n",
    "intrinsic_resolution=0.38 #mm\n",
    "psf_meta = dicom.get_psfmeta_from_scanner_params(\n",
    "    collimator_name,\n",
    "    energy_kev,\n",
    "    intrinsic_resolution=intrinsic_resolution\n",
    ")\n",
    "print(psf_meta)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da253908",
   "metadata": {
    "papermill": {
     "duration": 0.005987,
     "end_time": "2025-04-30T22:20:30.293799",
     "exception": false,
     "start_time": "2025-04-30T22:20:30.287812",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Now we can build our PSF transform required to create the system matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f59ac1ac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-30T22:20:30.307694Z",
     "iopub.status.busy": "2025-04-30T22:20:30.307285Z",
     "iopub.status.idle": "2025-04-30T22:20:30.310174Z",
     "shell.execute_reply": "2025-04-30T22:20:30.309584Z"
    },
    "papermill": {
     "duration": 0.011216,
     "end_time": "2025-04-30T22:20:30.311084",
     "exception": false,
     "start_time": "2025-04-30T22:20:30.299868",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "psf_transform = SPECTPSFTransform(psf_meta)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8522b4af",
   "metadata": {
    "papermill": {
     "duration": 0.006293,
     "end_time": "2025-04-30T22:20:30.331880",
     "exception": false,
     "start_time": "2025-04-30T22:20:30.325587",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## System Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eadc002a",
   "metadata": {
    "papermill": {
     "duration": 0.006219,
     "end_time": "2025-04-30T22:20:30.344813",
     "exception": false,
     "start_time": "2025-04-30T22:20:30.338594",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Now that we have the required transforms, we can begin to build the system matrix. It requires:\n",
    "1. `obj2obj_transforms`. These map from object space to object space. In the case of SPECT imaging, both the attenuation transform and PSF transform correspond to these types of transforms.\n",
    "2. `proj2proj_transforms`: These map from projection to projection space. Though we have none here, they would be used to model PSF in PET imaging.\n",
    "3. `object_meta` and `proj_meta`. These are used to initialize the transforms. For example, data from `object_meta` is used to align the attenuation map, and radial position from `proj_meta` is used to get proper blurring parameters for the PSF transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4f2371df",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T22:19:04.547670Z",
     "iopub.status.busy": "2024-11-19T22:19:04.547297Z",
     "iopub.status.idle": "2024-11-19T22:19:06.682191Z",
     "shell.execute_reply": "2024-11-19T22:19:06.681328Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": false,
     "start_time": "2025-04-30T22:20:30.350936",
     "status": "running"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Given photopeak energy 208.0 keV and CT energy 130 keV from the CT DICOM header, the HU->mu conversion from the following configuration is used: 208.0 keV SPECT energy, 130 keV CT energy, and scanner model symbiat2\n"
     ]
    }
   ],
   "source": [
    "system_matrix = SPECTSystemMatrix(\n",
    "        obj2obj_transforms = [att_transform,psf_transform],\n",
    "        proj2proj_transforms = [],\n",
    "        object_meta = object_meta,\n",
    "        proj_meta = proj_meta)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "730619bf",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "The system matrix $H$ represents the imaging system and maps an object $f$ to a corresponding image $g$ under the imaging system"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b22a7707",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "## Likelihood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c48b18b2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T22:19:06.728785Z",
     "iopub.status.busy": "2024-11-19T22:19:06.728263Z",
     "iopub.status.idle": "2024-11-19T22:19:06.732101Z",
     "shell.execute_reply": "2024-11-19T22:19:06.731486Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "likelihood = PoissonLogLikelihood(system_matrix, photopeak, scatter)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c8014eff",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "# Reconstruct the object"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c31e20a",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "We'll reconstruct the object using OSEM. For this, we need to specify the measured projection data (`photopeak`/`scatter`) and the `system_matrix`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5e07e874",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T22:19:06.777941Z",
     "iopub.status.busy": "2024-11-19T22:19:06.777398Z",
     "iopub.status.idle": "2024-11-19T22:19:06.798739Z",
     "shell.execute_reply": "2024-11-19T22:19:06.798136Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "reconstruction_algorithm = OSEM(likelihood)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "376c0192",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "We can reconstruct the object by calling the reconstruction algorithm for a given number of iterations/subsets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e24adf5a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T22:19:06.830982Z",
     "iopub.status.busy": "2024-11-19T22:19:06.830443Z",
     "iopub.status.idle": "2024-11-19T22:19:14.451292Z",
     "shell.execute_reply": "2024-11-19T22:19:14.450279Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "reconstructed_object = reconstruction_algorithm(n_iters=4, n_subsets=8)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "8a366340",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "We can compare this reconstruction to one done by the vendor:\n",
    "\n",
    "* Note: we divide the vendor reconstruction by 96 because this particualr vendor multiplies by the number of acquisition angles when saving the data. (*some vendors alternatively divide by 48, the number of projection angles, so be careful*)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c27122df",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T22:19:14.484704Z",
     "iopub.status.busy": "2024-11-19T22:19:14.484335Z",
     "iopub.status.idle": "2024-11-19T22:19:14.544684Z",
     "shell.execute_reply": "2024-11-19T22:19:14.543955Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "ds_recon = pydicom.dcmread(os.path.join(PATH, 'scanner_recon.dcm'))\n",
    "recon_vendor = ds_recon.pixel_array / proj_meta.num_projections\n",
    "recon_vendor = np.transpose(recon_vendor, (2,1,0))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "519423ed",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "We can looks at axial slices from all three reconstructions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "20e3c8d3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T22:19:14.576403Z",
     "iopub.status.busy": "2024-11-19T22:19:14.575993Z",
     "iopub.status.idle": "2024-11-19T22:19:14.583099Z",
     "shell.execute_reply": "2024-11-19T22:19:14.582516Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "idx_z = 61\n",
    "slice_pytomography = reconstructed_object.cpu()[:,:,idx_z].T\n",
    "slice_vendor = recon_vendor[:,:,idx_z].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6d6a9571",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T22:19:14.599848Z",
     "iopub.status.busy": "2024-11-19T22:19:14.599473Z",
     "iopub.status.idle": "2024-11-19T22:19:14.736270Z",
     "shell.execute_reply": "2024-11-19T22:19:14.735639Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 700x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(1,2,figsize=(7,3), gridspec_kw={'wspace':0.0})\n",
    "plt.subplot(121)\n",
    "plt.imshow(slice_pytomography , cmap='magma', vmax=27, interpolation='gaussian', origin='lower')\n",
    "plt.text(0.03, 0.97, 'PyTomography', color='white', fontsize=12, fontweight='bold', transform=plt.gca().transAxes, ha='left', va='top')\n",
    "plt.xlim(30,98)\n",
    "plt.ylim(98,40)\n",
    "plt.axis('off')\n",
    "plt.subplot(122)\n",
    "plt.imshow(slice_vendor, cmap='magma', vmax=27, interpolation='gaussian', origin='lower')\n",
    "plt.text(0.03, 0.97, 'Siemens Software', color='white', fontsize=12, fontweight='bold', transform=plt.gca().transAxes, ha='left', va='top')\n",
    "plt.xlim(30,98)\n",
    "plt.ylim(98,40)\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5fefb40",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "## Saving Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8c49be3",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "PyTomography has functionality for saving DICOM data. There are a few things required:\n",
    "\n",
    "1. The folder where you want to save the data. The folder can't exist already (has to be a new folder); this prevents overwriting potential data.\n",
    "2. The reconstructed object itself: in this case, `recon_CTslices`\n",
    "3. The filepath of the projection data `file_NM`. Much of the study/patient information is copied over from this file to the reconstructed file.\n",
    "4. An optional string that represents the type of reconstruction performed. If left blank, will be an empty string `''`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49b269c4",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "**Note: Some vendor reconstructions (i) multiply by the total number of projections and/or (ii) normalize to counts/second when they save their SPECT reconstructions. Make sure to multiply `reconstructed_object` by these values before you save if you want to do such comparisons.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bf87b778",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T22:19:14.802808Z",
     "iopub.status.busy": "2024-11-19T22:19:14.802233Z",
     "iopub.status.idle": "2024-11-19T22:19:15.219061Z",
     "shell.execute_reply": "2024-11-19T22:19:15.218333Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Modify the path below to a location on your computer where you want to save the data\n",
    "save_path = os.path.join(PATH, 'SPECT', 'Pytomo-Recon')\n",
    "# Code only works if folder doesnt exist, so delete it if present\n",
    "if os.path.exists(save_path) and os.path.isdir(save_path):\n",
    "    shutil.rmtree(save_path)\n",
    "# Save\n",
    "dicom.save_dcm(\n",
    "    save_path = save_path,\n",
    "    object = reconstructed_object,\n",
    "    file_NM = file_NM,\n",
    "    recon_name = 'OSEM_4it_8ss')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa1e5272",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "The data can now be exported to any DICOM viewer of your choice!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytomo",
   "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": "3.12.9"
  },
  "papermill": {
   "default_parameters": {},
   "duration": null,
   "end_time": null,
   "environment_variables": {},
   "exception": null,
   "input_path": "t_dicomdata.ipynb",
   "output_path": "t_dicomdata.ipynb",
   "parameters": {},
   "start_time": "2025-04-30T22:20:16.907827",
   "version": "2.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}