{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "66912687",
   "metadata": {
    "papermill": {
     "duration": 0.004448,
     "end_time": "2024-11-19T22:25:59.141178",
     "exception": false,
     "start_time": "2024-11-19T22:25:59.136730",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Uncertainty Estimation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed4cd800",
   "metadata": {
    "papermill": {
     "duration": 0.003852,
     "end_time": "2024-11-19T22:25:59.148949",
     "exception": false,
     "start_time": "2024-11-19T22:25:59.145097",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Special thanks to Dr. Peyman Sheikhzadeh at the Tehran University Of Medical Science for providing this anonymized data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7f9a8ba9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T22:25:59.158329Z",
     "iopub.status.busy": "2024-11-19T22:25:59.157764Z",
     "iopub.status.idle": "2024-11-19T22:26:03.465255Z",
     "shell.execute_reply": "2024-11-19T22:26:03.464382Z"
    },
    "papermill": {
     "duration": 4.314213,
     "end_time": "2024-11-19T22:26:03.467039",
     "exception": false,
     "start_time": "2024-11-19T22:25:59.152826",
     "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, PGAAMultiBedSPECT\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 torch\n",
    "from rt_utils import RTStructBuilder\n",
    "from pytomography.callbacks import DataStorageCallback"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8a2f4f0c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T22:26:03.476933Z",
     "iopub.status.busy": "2024-11-19T22:26:03.476427Z",
     "iopub.status.idle": "2024-11-19T22:26:03.480117Z",
     "shell.execute_reply": "2024-11-19T22:26:03.479498Z"
    },
    "papermill": {
     "duration": 0.009603,
     "end_time": "2024-11-19T22:26:03.481373",
     "exception": false,
     "start_time": "2024-11-19T22:26:03.471770",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "save_path = '/mnt/mydisk2/pytomo_tutorial_data/SPECT'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95aeb947",
   "metadata": {
    "papermill": {
     "duration": 0.003717,
     "end_time": "2024-11-19T22:26:03.489102",
     "exception": false,
     "start_time": "2024-11-19T22:26:03.485385",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The following code cell loads projection data and a corresponding CT file and creates a reconstruction algorithm; if you are unfamiliar with the code below or require more explanation, see that \"DICOM Data Introduction\" tutorial.\n",
    "* **The only difference is that we now get a variance estimate for the scatter in line 7 of the cell below, and then input that to the likelihood in line 22**\n",
    "* The collimator corresponding to this data is General Electric\n",
    "s medium energy general purpose collimator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "78485fd2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T22:26:03.497785Z",
     "iopub.status.busy": "2024-11-19T22:26:03.497443Z",
     "iopub.status.idle": "2024-11-19T22:26:14.224077Z",
     "shell.execute_reply": "2024-11-19T22:26:14.223078Z"
    },
    "papermill": {
     "duration": 10.733302,
     "end_time": "2024-11-19T22:26:14.226068",
     "exception": false,
     "start_time": "2024-11-19T22:26:03.492766",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Given photopeak energy 208.0 keV and CT energy 120 keV from the CT DICOM header, the HU->mu conversion from the following configuration is used: 208.0 keV SPECT energy, 120 keV CT energy, and scanner model optimact540\n"
     ]
    }
   ],
   "source": [
    "file_NM = os.path.join(save_path, 'Lu177-PSMA-GEDisc', 'bed2_projections.dcm')\n",
    "path_CT = os.path.join(save_path, 'Lu177-PSMA-GEDisc', 'CT')\n",
    "files_CT = [os.path.join(path_CT, file) for file in os.listdir(path_CT)]\n",
    "object_meta, proj_meta = dicom.get_metadata(file_NM, index_peak=1)\n",
    "projections = dicom.get_projections(file_NM)\n",
    "photopeak = projections[1]\n",
    "scatter, scatter_variance_estimate = dicom.get_energy_window_scatter_estimate(file_NM, index_peak=1, index_lower=3, index_upper=2, return_scatter_variance_estimate=True)\n",
    "# Build system matrix\n",
    "attenuation_map = dicom.get_attenuation_map_from_CT_slices(files_CT, file_NM, index_peak=1)\n",
    "psf_meta = dicom.get_psfmeta_from_scanner_params('GI-MEGP', energy_keV=208)\n",
    "att_transform = SPECTAttenuationTransform(attenuation_map)\n",
    "psf_transform = SPECTPSFTransform(psf_meta)\n",
    "system_matrix = SPECTSystemMatrix(\n",
    "    obj2obj_transforms = [att_transform,psf_transform],\n",
    "    proj2proj_transforms = [],\n",
    "    object_meta = object_meta,\n",
    "    proj_meta = proj_meta)\n",
    "# Likelihood\n",
    "likelihood = PoissonLogLikelihood(system_matrix, photopeak, additive_term=scatter, additive_term_variance_estimate=scatter_variance_estimate)\n",
    "# Reconstruction algorithm\n",
    "recon_algorithm = OSEM(likelihood)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bba6abcf",
   "metadata": {
    "papermill": {
     "duration": 0.004379,
     "end_time": "2024-11-19T22:26:14.235055",
     "exception": false,
     "start_time": "2024-11-19T22:26:14.230676",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Up to this point, we've created the reconstruction algorithm but we haven't performed reconstruction yet. If we plan on computing uncertainty in various VOIs, we are required to use the `DataStorageCallback` callback in the image reconstruction algorithm. This callback stores a copy of the reconstructed image at each iteration and subiteration: all this data is required when computing uncertainties later. It can be created as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "52b1fbba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T22:26:14.244433Z",
     "iopub.status.busy": "2024-11-19T22:26:14.244186Z",
     "iopub.status.idle": "2024-11-19T22:26:14.248106Z",
     "shell.execute_reply": "2024-11-19T22:26:14.247566Z"
    },
    "papermill": {
     "duration": 0.010356,
     "end_time": "2024-11-19T22:26:14.249363",
     "exception": false,
     "start_time": "2024-11-19T22:26:14.239007",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data_storage_callback = DataStorageCallback(likelihood, torch.clone(recon_algorithm.object_prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c909c950",
   "metadata": {
    "papermill": {
     "duration": 0.003706,
     "end_time": "2024-11-19T22:26:14.257512",
     "exception": false,
     "start_time": "2024-11-19T22:26:14.253806",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Then we reconstruct using this callback:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f1d8655a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T22:26:14.266606Z",
     "iopub.status.busy": "2024-11-19T22:26:14.266100Z",
     "iopub.status.idle": "2024-11-19T22:26:18.606608Z",
     "shell.execute_reply": "2024-11-19T22:26:18.605312Z"
    },
    "papermill": {
     "duration": 4.347206,
     "end_time": "2024-11-19T22:26:18.608590",
     "exception": false,
     "start_time": "2024-11-19T22:26:14.261384",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "recon_OSEM = recon_algorithm(n_iters = 4, n_subsets = 8, callback=data_storage_callback)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf807646",
   "metadata": {
    "papermill": {
     "duration": 0.003714,
     "end_time": "2024-11-19T22:26:18.616750",
     "exception": false,
     "start_time": "2024-11-19T22:26:18.613036",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Here's a coronal maximum intensity projection of the reconstruction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c755d491",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T22:26:18.626656Z",
     "iopub.status.busy": "2024-11-19T22:26:18.626000Z",
     "iopub.status.idle": "2024-11-19T22:26:18.641277Z",
     "shell.execute_reply": "2024-11-19T22:26:18.640652Z"
    },
    "papermill": {
     "duration": 0.021786,
     "end_time": "2024-11-19T22:26:18.642537",
     "exception": false,
     "start_time": "2024-11-19T22:26:18.620751",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "maximum_intensity_projection = recon_OSEM.max(axis=1)[0].cpu()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c1aea5dd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T22:26:18.653844Z",
     "iopub.status.busy": "2024-11-19T22:26:18.653334Z",
     "iopub.status.idle": "2024-11-19T22:26:18.740009Z",
     "shell.execute_reply": "2024-11-19T22:26:18.739452Z"
    },
    "papermill": {
     "duration": 0.093133,
     "end_time": "2024-11-19T22:26:18.741360",
     "exception": false,
     "start_time": "2024-11-19T22:26:18.648227",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4,4))\n",
    "plt.imshow(maximum_intensity_projection.T, cmap='Greys', vmax=4, interpolation='gaussian', origin='lower')\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2172e650",
   "metadata": {
    "papermill": {
     "duration": 0.004483,
     "end_time": "2024-11-19T22:26:18.750595",
     "exception": false,
     "start_time": "2024-11-19T22:26:18.746112",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Suppose we want to estimate the uncertainty in activity in one of the kidneys. Let's obtain it from an RTStruct file containing segmentations of all the organs. (The cell below is not required for this, but it will allow us to determine the mask names contained in the RTStruct file):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "110fa625",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T22:26:18.761299Z",
     "iopub.status.busy": "2024-11-19T22:26:18.760763Z",
     "iopub.status.idle": "2024-11-19T22:26:20.199909Z",
     "shell.execute_reply": "2024-11-19T22:26:20.199266Z"
    },
    "papermill": {
     "duration": 1.445642,
     "end_time": "2024-11-19T22:26:20.201225",
     "exception": false,
     "start_time": "2024-11-19T22:26:18.755583",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['spleen', 'kidney_right', 'kidney_left', 'gallbladder', 'liver', 'stomach', 'pancreas', 'adrenal_gland_right', 'adrenal_gland_left', 'lung_upper_lobe_left', 'lung_lower_lobe_left', 'lung_upper_lobe_right', 'lung_middle_lobe_right', 'lung_lower_lobe_right', 'esophagus', 'trachea', 'thyroid_gland', 'small_bowel', 'duodenum', 'colon', 'urinary_bladder', 'sacrum', 'vertebrae_S1', 'vertebrae_L5', 'vertebrae_L4', 'vertebrae_L3', 'vertebrae_L2', 'vertebrae_L1', 'vertebrae_T12', 'vertebrae_T11', 'vertebrae_T10', 'vertebrae_T9', 'vertebrae_T8', 'vertebrae_T7', 'vertebrae_T6', 'vertebrae_T5', 'vertebrae_T4', 'vertebrae_T3', 'vertebrae_T2', 'vertebrae_T1', 'vertebrae_C7', 'vertebrae_C6', 'vertebrae_C5', 'vertebrae_C4', 'vertebrae_C3', 'vertebrae_C2', 'vertebrae_C1', 'heart', 'aorta', 'pulmonary_vein', 'brachiocephalic_trunk', 'subclavian_artery_right', 'subclavian_artery_left', 'common_carotid_artery_right', 'common_carotid_artery_left', 'brachiocephalic_vein_left', 'brachiocephalic_vein_right', 'atrial_appendage_left', 'superior_vena_cava', 'inferior_vena_cava', 'portal_vein_and_splenic_vein', 'iliac_artery_left', 'iliac_artery_right', 'iliac_vena_left', 'iliac_vena_right', 'humerus_left', 'humerus_right', 'scapula_left', 'scapula_right', 'clavicula_left', 'clavicula_right', 'femur_right', 'hip_left', 'hip_right', 'spinal_cord', 'gluteus_maximus_left', 'gluteus_maximus_right', 'gluteus_medius_left', 'gluteus_medius_right', 'gluteus_minimus_left', 'gluteus_minimus_right', 'autochthon_left', 'autochthon_right', 'iliopsoas_left', 'iliopsoas_right', 'brain', 'skull', 'rib_right_4', 'rib_right_3', 'rib_left_1', 'rib_left_2', 'rib_left_3', 'rib_left_4', 'rib_left_5', 'rib_left_6', 'rib_left_7', 'rib_left_8', 'rib_left_9', 'rib_left_10', 'rib_left_11', 'rib_left_12', 'rib_right_1', 'rib_right_2', 'rib_right_5', 'rib_right_6', 'rib_right_7', 'rib_right_8', 'rib_right_9', 'rib_right_10', 'rib_right_11', 'rib_right_12', 'sternum', 'costal_cartilages']\n"
     ]
    }
   ],
   "source": [
    "file_RT = os.path.join(save_path, 'Lu177-PSMA-GEDisc', 'segmentations.dcm')\n",
    "rtstruct = RTStructBuilder.create_from(\n",
    "        dicom_series_path=path_CT, \n",
    "        rt_struct_path=file_RT\n",
    "    )\n",
    "print(rtstruct.get_roi_names())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d24b1f29",
   "metadata": {
    "papermill": {
     "duration": 0.004694,
     "end_time": "2024-11-19T22:26:20.211006",
     "exception": false,
     "start_time": "2024-11-19T22:26:20.206312",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We can see the `'kidney_right'` is listed as one of the available masks. We can obtain this mask as follows:\n",
    "* `file_RT` is the path to the RTStruct file\n",
    "* `file_NM` is the path the the SPECT projection data; this is required for aligning the mask\n",
    "* `dicom_series_path` is the reference DICOM path of the RTStruct\n",
    "* `rt_struct_name` is the name of the RTStruct we wish to open"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "57027624",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T22:26:20.221535Z",
     "iopub.status.busy": "2024-11-19T22:26:20.221140Z",
     "iopub.status.idle": "2024-11-19T22:26:23.236056Z",
     "shell.execute_reply": "2024-11-19T22:26:23.235416Z"
    },
    "papermill": {
     "duration": 3.02225,
     "end_time": "2024-11-19T22:26:23.237737",
     "exception": false,
     "start_time": "2024-11-19T22:26:20.215487",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "mask_name = 'kidney_left'\n",
    "kidney_mask = dicom.get_aligned_rtstruct(\n",
    "    file_RT = file_RT,\n",
    "    file_NM = file_NM,\n",
    "    dicom_series_path = path_CT,\n",
    "    rt_struct_name = mask_name\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24bf9411",
   "metadata": {
    "papermill": {
     "duration": 0.00455,
     "end_time": "2024-11-19T22:26:23.247437",
     "exception": false,
     "start_time": "2024-11-19T22:26:23.242887",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We can compute the relative and absolute uncertainty corresponding to counts in this region using the `compute_uncertainty` function of the reconstruction algorithm. We also need to pass in the `DataStorageCallback` which contains every iterative update to the objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7b6a3bf1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T22:26:23.259318Z",
     "iopub.status.busy": "2024-11-19T22:26:23.258950Z",
     "iopub.status.idle": "2024-11-19T22:26:32.285770Z",
     "shell.execute_reply": "2024-11-19T22:26:32.284920Z"
    },
    "papermill": {
     "duration": 9.034789,
     "end_time": "2024-11-19T22:26:32.287128",
     "exception": false,
     "start_time": "2024-11-19T22:26:23.252339",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated uncertainty in kidney_left: 0.94%\n"
     ]
    }
   ],
   "source": [
    "uncertainty_abs, uncertainty_pct = recon_algorithm.compute_uncertainty(\n",
    "    mask = kidney_mask,\n",
    "    data_storage_callback = data_storage_callback,\n",
    "    return_pct = True,\n",
    "    include_additive_term=True # must have additive_term_variance_estimate in likelihood\n",
    ")\n",
    "print(f'Estimated uncertainty in {mask_name}: {uncertainty_pct:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "599e77ba",
   "metadata": {
    "papermill": {
     "duration": 0.00449,
     "end_time": "2024-11-19T22:26:32.296581",
     "exception": false,
     "start_time": "2024-11-19T22:26:32.292091",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "This can be repeated for any of the masks above! In addition, you can play around the with iterations, subsets (or even try different reconstruction algorithms) and see how it effects the estimated uncertainty."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ee0ac2c",
   "metadata": {
    "papermill": {
     "duration": 0.004658,
     "end_time": "2024-11-19T22:26:32.305951",
     "exception": false,
     "start_time": "2024-11-19T22:26:32.301293",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Multiple Bed Positions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3905d686",
   "metadata": {
    "papermill": {
     "duration": 0.004567,
     "end_time": "2024-11-19T22:26:32.314938",
     "exception": false,
     "start_time": "2024-11-19T22:26:32.310371",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "PyTomography is also capable of uncertainty estimation in masks that are contained in two seperate bed positions. This uncertainty estimation takes into account the stitching weights between two different bed positions (if the mask is near the edge) when computing the organ variance in each seperate region.\n",
    "\n",
    "* The code below is a summary of the multibed position tutorial, except that we now include a DataStorageCallback for each bed position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1295937e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T22:26:32.325605Z",
     "iopub.status.busy": "2024-11-19T22:26:32.325105Z",
     "iopub.status.idle": "2024-11-19T22:26:32.460131Z",
     "shell.execute_reply": "2024-11-19T22:26:32.459370Z"
    },
    "papermill": {
     "duration": 0.142444,
     "end_time": "2024-11-19T22:26:32.461871",
     "exception": false,
     "start_time": "2024-11-19T22:26:32.319427",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "files_NM = [\n",
    "    os.path.join(save_path, 'Lu177-PSMA-GEDisc', 'bed1_projections.dcm'),\n",
    "    os.path.join(save_path, 'Lu177-PSMA-GEDisc', 'bed2_projections.dcm'),\n",
    "]\n",
    "path_CT = os.path.join(save_path, 'Lu177-PSMA-GEDisc', 'CT')\n",
    "files_CT = [os.path.join(path_CT, file) for file in os.listdir(path_CT)]\n",
    "\n",
    "projectionss = dicom.load_multibed_projections(files_NM)\n",
    "\n",
    "\n",
    "def initialize_reconstruction_algorithm_singlebed(i):\n",
    "    # Change these depending on your file:\n",
    "    index_peak = 1\n",
    "    index_lower = 3\n",
    "    index_upper = 2\n",
    "    projections = projectionss[i]\n",
    "    file_NM = files_NM[i]\n",
    "    object_meta, proj_meta = dicom.get_metadata(file_NM, index_peak=index_peak)\n",
    "    photopeak = projections[index_peak]\n",
    "    scatter = dicom.get_energy_window_scatter_estimate_projections(file_NM, projections, index_peak, index_lower, index_upper)\n",
    "    # Build system matrix\n",
    "    attenuation_map = dicom.get_attenuation_map_from_CT_slices(files_CT, file_NM, index_peak=index_peak)\n",
    "    psf_meta = dicom.get_psfmeta_from_scanner_params('GI-MEGP', energy_keV=208)\n",
    "    att_transform = SPECTAttenuationTransform(attenuation_map)\n",
    "    psf_transform = SPECTPSFTransform(psf_meta)\n",
    "    # Create system matrix\n",
    "    system_matrix = SPECTSystemMatrix(\n",
    "        obj2obj_transforms = [att_transform,psf_transform],\n",
    "        proj2proj_transforms= [],\n",
    "        object_meta = object_meta,\n",
    "        proj_meta = proj_meta)\n",
    "    likelihood = PoissonLogLikelihood(system_matrix, photopeak, additive_term=scatter)\n",
    "    reconstruction_algorithm = OSEM(likelihood)\n",
    "    # Return only the reconstruction algorithm initialization (not calling it for any iterations/subsets yet)\n",
    "    return reconstruction_algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df6e1284",
   "metadata": {
    "papermill": {
     "duration": 0.004813,
     "end_time": "2024-11-19T22:26:32.471812",
     "exception": false,
     "start_time": "2024-11-19T22:26:32.466999",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "In order to compute uncertainty, we need to use the `PGAAMultiBedWrapper` reconstruction algorithm, which takes in multiple initialized reconstruction algorithms for each bed position (this class can also be used to reconstruct multiple bed positions without uncertainty as well)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "eb4626f9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T22:26:32.482219Z",
     "iopub.status.busy": "2024-11-19T22:26:32.481805Z",
     "iopub.status.idle": "2024-11-19T22:27:04.069161Z",
     "shell.execute_reply": "2024-11-19T22:27:04.066698Z"
    },
    "papermill": {
     "duration": 31.596329,
     "end_time": "2024-11-19T22:27:04.072837",
     "exception": false,
     "start_time": "2024-11-19T22:26:32.476508",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Given photopeak energy 208.0 keV and CT energy 120 keV from the CT DICOM header, the HU->mu conversion from the following configuration is used: 208.0 keV SPECT energy, 120 keV CT energy, and scanner model optimact540\n",
      "Given photopeak energy 208.0 keV and CT energy 120 keV from the CT DICOM header, the HU->mu conversion from the following configuration is used: 208.0 keV SPECT energy, 120 keV CT energy, and scanner model optimact540\n"
     ]
    }
   ],
   "source": [
    "recon_algo_upper = initialize_reconstruction_algorithm_singlebed(0)\n",
    "recon_algo_lower  = initialize_reconstruction_algorithm_singlebed(1)\n",
    "recon_algo = PGAAMultiBedSPECT(files_NM, [recon_algo_upper, recon_algo_lower])\n",
    "# Initialize callback for each bed position\n",
    "callbacks = [DataStorageCallback(r.likelihood, r.object_prediction) for r in recon_algo.reconstruction_algorithms]\n",
    "reconstructed_image_multibed = recon_algo(4, 8, callback=callbacks)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62f60190",
   "metadata": {
    "papermill": {
     "duration": 0.004781,
     "end_time": "2024-11-19T22:27:04.083575",
     "exception": false,
     "start_time": "2024-11-19T22:27:04.078794",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Lets plot the reconstruction (note how the liver overlaps two different bed positions, since it spans the center of the image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "95fbefff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T22:27:04.094949Z",
     "iopub.status.busy": "2024-11-19T22:27:04.094402Z",
     "iopub.status.idle": "2024-11-19T22:27:04.177045Z",
     "shell.execute_reply": "2024-11-19T22:27:04.176444Z"
    },
    "papermill": {
     "duration": 0.09011,
     "end_time": "2024-11-19T22:27:04.178474",
     "exception": false,
     "start_time": "2024-11-19T22:27:04.088364",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "maximum_intensity_projection = reconstructed_image_multibed.max(axis=1)[0].cpu()\n",
    "plt.figure(figsize=(3,5))\n",
    "plt.imshow(maximum_intensity_projection.T, cmap='Greys', vmax=5, origin='lower', interpolation='gaussian')\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5f8ee25",
   "metadata": {
    "papermill": {
     "duration": 0.005423,
     "end_time": "2024-11-19T22:27:04.189662",
     "exception": false,
     "start_time": "2024-11-19T22:27:04.184239",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "When we obtain the organ mask, we need to \n",
    "1. Specify the shape as the shape of the reconstructed image (not the batch dimension, so we use `[1:]`)\n",
    "2. Specify the `file_NM` in the most superior bed position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e11f80b8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T22:27:04.201412Z",
     "iopub.status.busy": "2024-11-19T22:27:04.201073Z",
     "iopub.status.idle": "2024-11-19T22:27:07.542181Z",
     "shell.execute_reply": "2024-11-19T22:27:07.541298Z"
    },
    "papermill": {
     "duration": 3.348925,
     "end_time": "2024-11-19T22:27:07.544059",
     "exception": false,
     "start_time": "2024-11-19T22:27:04.195134",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "mask_name = 'liver'\n",
    "liver_mask = dicom.get_aligned_rtstruct(\n",
    "    file_RT = file_RT,\n",
    "    file_NM = files_NM[0],\n",
    "    dicom_series_path = path_CT,\n",
    "    rt_struct_name = mask_name,\n",
    "    shape=reconstructed_image_multibed.shape\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6df93d50",
   "metadata": {
    "papermill": {
     "duration": 0.005962,
     "end_time": "2024-11-19T22:27:07.556137",
     "exception": false,
     "start_time": "2024-11-19T22:27:07.550175",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Now we can compute the uncertainty in the liver, which spans both bed positions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1f5732f4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T22:27:07.568677Z",
     "iopub.status.busy": "2024-11-19T22:27:07.568182Z",
     "iopub.status.idle": "2024-11-19T22:27:13.218098Z",
     "shell.execute_reply": "2024-11-19T22:27:13.217068Z"
    },
    "papermill": {
     "duration": 5.658289,
     "end_time": "2024-11-19T22:27:13.219984",
     "exception": false,
     "start_time": "2024-11-19T22:27:07.561695",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "uncertainty_abs, uncertainty_pct = recon_algo.compute_uncertainty(liver_mask, callbacks, return_pct=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1b8b9ae0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T22:27:13.235378Z",
     "iopub.status.busy": "2024-11-19T22:27:13.235055Z",
     "iopub.status.idle": "2024-11-19T22:27:13.239361Z",
     "shell.execute_reply": "2024-11-19T22:27:13.238830Z"
    },
    "papermill": {
     "duration": 0.014608,
     "end_time": "2024-11-19T22:27:13.240563",
     "exception": false,
     "start_time": "2024-11-19T22:27:13.225955",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated uncertainty in liver: 1.56%\n"
     ]
    }
   ],
   "source": [
    "print(f'Estimated uncertainty in {mask_name}: {uncertainty_pct:.2f}%')"
   ]
  }
 ],
 "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": 76.143651,
   "end_time": "2024-11-19T22:27:14.363496",
   "environment_variables": {},
   "exception": null,
   "input_path": "t_uncertainty_spect.ipynb",
   "output_path": "t_uncertainty_spect.ipynb",
   "parameters": {},
   "start_time": "2024-11-19T22:25:58.219845",
   "version": "2.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}