{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b9a3bee9",
   "metadata": {
    "papermill": {
     "duration": 0.002971,
     "end_time": "2024-11-19T22:30:03.804772",
     "exception": false,
     "start_time": "2024-11-19T22:30:03.801801",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Ac-225 Advanced PSF Modeling (SIMIND)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f60caca",
   "metadata": {
    "papermill": {
     "duration": 0.003258,
     "end_time": "2024-11-19T22:30:03.835886",
     "exception": false,
     "start_time": "2024-11-19T22:30:03.832628",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "This tutorial uses the PSF operators obtained using the SPECTPSF toolbox to reconstruct Ac225 data. The operator was obtained using tutorial 5, available at this [link](https://github.com/lukepolson/SPECTPSFToolbox/blob/main/tutorials/5_optimization_ac225.ipynb). Use of this operator requires the SPECTPSFToolbox to be installed; instructions for installing can be found on the README [here](https://github.com/lukepolson/SPECTPSFToolbox/tree/main)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0509a980",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T22:30:03.843273Z",
     "iopub.status.busy": "2024-11-19T22:30:03.842502Z",
     "iopub.status.idle": "2024-11-19T22:30:07.898427Z",
     "shell.execute_reply": "2024-11-19T22:30:07.897697Z"
    },
    "papermill": {
     "duration": 4.06191,
     "end_time": "2024-11-19T22:30:07.900586",
     "exception": false,
     "start_time": "2024-11-19T22:30:03.838676",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import torch\n",
    "import dill\n",
    "import pytomography\n",
    "from pytomography.io.SPECT import simind\n",
    "from pytomography.projectors.SPECT import SPECTSystemMatrix\n",
    "from pytomography.transforms.SPECT  import SPECTAttenuationTransform, SPECTPSFTransform\n",
    "from pytomography.algorithms import OSEM\n",
    "from pytomography.io.SPECT.shared import subsample_projections_and_modify_metadata, subsample_amap\n",
    "from pytomography.likelihoods import PoissonLogLikelihood\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "90753cf1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T22:30:07.912542Z",
     "iopub.status.busy": "2024-11-19T22:30:07.912065Z",
     "iopub.status.idle": "2024-11-19T22:30:07.915307Z",
     "shell.execute_reply": "2024-11-19T22:30:07.914770Z"
    },
    "papermill": {
     "duration": 0.013257,
     "end_time": "2024-11-19T22:30:07.916925",
     "exception": false,
     "start_time": "2024-11-19T22:30:07.903668",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# change to where you saved the data\n",
    "PATH = '/mnt/mydisk2/pytomo_tutorial_data/SPECT/SIMIND-Jaszak'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "846534d4",
   "metadata": {
    "papermill": {
     "duration": 0.002724,
     "end_time": "2024-11-19T22:30:07.922208",
     "exception": false,
     "start_time": "2024-11-19T22:30:07.919484",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Load the SIMIND data. In this case, we'll use the ground truth scatter from SIMIND."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e45a3064",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T22:30:07.928659Z",
     "iopub.status.busy": "2024-11-19T22:30:07.928330Z",
     "iopub.status.idle": "2024-11-19T22:30:09.767193Z",
     "shell.execute_reply": "2024-11-19T22:30:09.766551Z"
    },
    "papermill": {
     "duration": 1.843949,
     "end_time": "2024-11-19T22:30:09.769033",
     "exception": false,
     "start_time": "2024-11-19T22:30:07.925084",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "dT = 2.5 * 60 # seconds per projection\n",
    "activity_conc = 10 # kBq/L, a very high activity conc for Ac225\n",
    "CPSpMBq = 17 # an approximate calibration factor\n",
    "\n",
    "# Specify the isotopes and equilibirum ratios based on Bateman equations\n",
    "isotopes = ['ac225', 'bi213', 'fr221', 'tl209']\n",
    "isotopes_ratios = [1,1,1,0.0209] \n",
    "# look at the .h00 files; there are many E windows simulated. These are 5% scat win for 440peak\n",
    "i_peak, i_lower, i_upper = 10, 11, 14 \n",
    "files_NM = [[os.path.join(PATH, f'{isotope}', f'tot_w{i}.h00') for isotope in isotopes] for i in [i_lower, i_peak, i_upper]]\n",
    "object_meta, proj_meta = simind.get_metadata(files_NM[0][0])\n",
    "activity_concs = [activity_conc * ratio for ratio in isotopes_ratios]\n",
    "projections = simind.get_projections(files_NM, activity_concs)\n",
    "projections *= dT\n",
    "\n",
    "# Based on how they are loaded, this is idx of peak, lower upper\n",
    "idx_peak, idx_lower, idx_upper = 1, 0, 2\n",
    "photopeak = projections[idx_peak]\n",
    "ww_lower, ww_peak, ww_upper = [simind.get_energy_window_width(path) for path in [files_NM[0][0], files_NM[0][1], files_NM[0][2]]]\n",
    "scatter_estimate_TEW = simind.compute_EW_scatter(\n",
    "    projections[idx_lower], projections[idx_upper],\n",
    "    ww_lower,\n",
    "    ww_upper,\n",
    "    ww_peak,\n",
    "    sigma_r=0.5,\n",
    "    sigma_z=0.5,\n",
    "    proj_meta=proj_meta\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca228773",
   "metadata": {
    "papermill": {
     "duration": 0.002635,
     "end_time": "2024-11-19T22:30:09.774911",
     "exception": false,
     "start_time": "2024-11-19T22:30:09.772276",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We're reconstructing the 440keV window so we'll use the 440keV attenuation map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4fb7f631",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T22:30:09.781661Z",
     "iopub.status.busy": "2024-11-19T22:30:09.781286Z",
     "iopub.status.idle": "2024-11-19T22:30:09.801417Z",
     "shell.execute_reply": "2024-11-19T22:30:09.800098Z"
    },
    "papermill": {
     "duration": 0.025307,
     "end_time": "2024-11-19T22:30:09.802811",
     "exception": false,
     "start_time": "2024-11-19T22:30:09.777504",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "path_amap = os.path.join(PATH, 'attenuation_maps', 'amap440.hct')\n",
    "amap = simind.get_attenuation_map(path_amap)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c080296",
   "metadata": {
    "papermill": {
     "duration": 0.002614,
     "end_time": "2024-11-19T22:30:09.844157",
     "exception": false,
     "start_time": "2024-11-19T22:30:09.841543",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The code below reconstructs using OSEM with 50 iterations and 4 subsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0c961aea",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T22:30:09.850779Z",
     "iopub.status.busy": "2024-11-19T22:30:09.850444Z",
     "iopub.status.idle": "2024-11-19T22:30:09.854307Z",
     "shell.execute_reply": "2024-11-19T22:30:09.853657Z"
    },
    "papermill": {
     "duration": 0.008873,
     "end_time": "2024-11-19T22:30:09.855708",
     "exception": false,
     "start_time": "2024-11-19T22:30:09.846835",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def perform_reconstruction(psf_transform):\n",
    "    att_transform = SPECTAttenuationTransform(attenuation_map=amap)\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, scatter_estimate_TEW)\n",
    "    algorithm = OSEM(likelihood)\n",
    "    return algorithm(n_iters=50, n_subsets=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b86383c8",
   "metadata": {
    "papermill": {
     "duration": 0.002604,
     "end_time": "2024-11-19T22:30:09.861065",
     "exception": false,
     "start_time": "2024-11-19T22:30:09.858461",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Open the PSF operator created in [this tutorial](https://github.com/lukepolson/SPECTPSFToolbox/blob/main/tutorials/5_optimization_ac225.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "658544c9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T22:30:09.867653Z",
     "iopub.status.busy": "2024-11-19T22:30:09.867264Z",
     "iopub.status.idle": "2024-11-19T22:30:56.917779Z",
     "shell.execute_reply": "2024-11-19T22:30:56.916719Z"
    },
    "papermill": {
     "duration": 47.056016,
     "end_time": "2024-11-19T22:30:56.919634",
     "exception": false,
     "start_time": "2024-11-19T22:30:09.863618",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Note that you may need to create this operator yourself with the SPECTPSFToolbox to match your GPU architecture\n",
    "with open(os.path.join(PATH, 'ac225_psf_operator.pkl'), 'rb') as f:\n",
    "    psf_operator = dill.load(f)\n",
    "psf_operator.set_device(pytomography.device)\n",
    "psf_transform = SPECTPSFTransform(psf_operator=psf_operator)\n",
    "recon_1Dfit = perform_reconstruction(psf_transform)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dc2d2fb",
   "metadata": {
    "papermill": {
     "duration": 0.0025,
     "end_time": "2024-11-19T22:30:56.925140",
     "exception": false,
     "start_time": "2024-11-19T22:30:56.922640",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Reconstruct using the Gaussian modeling which doesn't account for the septal penetration and septal scatter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "385bdd76",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T22:30:56.932466Z",
     "iopub.status.busy": "2024-11-19T22:30:56.931970Z",
     "iopub.status.idle": "2024-11-19T22:31:10.067021Z",
     "shell.execute_reply": "2024-11-19T22:31:10.065923Z"
    },
    "papermill": {
     "duration": 13.140973,
     "end_time": "2024-11-19T22:31:10.069067",
     "exception": false,
     "start_time": "2024-11-19T22:30:56.928094",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "path_bi213_prim = files_NM[1][1]\n",
    "psf_meta = simind.get_psfmeta_from_header(path_bi213_prim)\n",
    "psf_transform = SPECTPSFTransform(psf_meta)\n",
    "reconbad = perform_reconstruction(psf_transform)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddc98aeb",
   "metadata": {
    "papermill": {
     "duration": 0.002669,
     "end_time": "2024-11-19T22:31:10.075288",
     "exception": false,
     "start_time": "2024-11-19T22:31:10.072619",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Lets compare the two reconstructions: the first one using the fitted PSF and the second one using the Monte Carlo kernels. Note that the fitted one was over twice as fast for reconstructing!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4b8abb4b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T22:31:10.082476Z",
     "iopub.status.busy": "2024-11-19T22:31:10.081995Z",
     "iopub.status.idle": "2024-11-19T22:31:10.085930Z",
     "shell.execute_reply": "2024-11-19T22:31:10.085375Z"
    },
    "papermill": {
     "duration": 0.009786,
     "end_time": "2024-11-19T22:31:10.087578",
     "exception": false,
     "start_time": "2024-11-19T22:31:10.077792",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "recons = [reconbad, recon_1Dfit]\n",
    "titles = ['Gaussian PSF', 'Full PSF']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "81470adb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T22:31:10.094266Z",
     "iopub.status.busy": "2024-11-19T22:31:10.093805Z",
     "iopub.status.idle": "2024-11-19T22:31:10.387069Z",
     "shell.execute_reply": "2024-11-19T22:31:10.386452Z"
    },
    "papermill": {
     "duration": 0.299265,
     "end_time": "2024-11-19T22:31:10.389655",
     "exception": false,
     "start_time": "2024-11-19T22:31:10.090390",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2,figsize=(6,4), gridspec_kw={'wspace':0.0})\n",
    "vmax = recons[1].max()\n",
    "for i in range(2):\n",
    "    plt.sca(ax[i])\n",
    "    plt.imshow(recons[i].cpu()[:,:,64].T, cmap='magma', interpolation='gaussian', vmax=vmax)\n",
    "    plt.text(0.05, 0.87, titles[i], ha='left', va='bottom', fontsize=22, transform=ax[i].transAxes, color='white')\n",
    "    plt.axis('off')\n",
    "    plt.xlim(35,95)\n",
    "    plt.ylim(35,95)\n",
    "fig.tight_layout()"
   ]
  }
 ],
 "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": 68.724101,
   "end_time": "2024-11-19T22:31:11.713931",
   "environment_variables": {},
   "exception": null,
   "input_path": "t_ac225_simind_recon.ipynb",
   "output_path": "t_ac225_simind_recon.ipynb",
   "parameters": {},
   "start_time": "2024-11-19T22:30:02.989830",
   "version": "2.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}