notebooks/single_material-reproducing_CT_scan-JSON_file.ipynb

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   "source": [
    "<a href=\"https://colab.research.google.com/github/TomographicImaging/gVXR-Tutorials/blob/main/notebooks/single_material-reproducing_CT_scan-JSON_file.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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   "cell_type": "code",
   "execution_count": null,
   "id": "e0fe41a6-8340-40b7-b4a5-a239daab3fb7",
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   "source": [
    "# -*- coding: utf-8 -*-\n",
    "#\n",
    "#  Copyright 2025 United Kingdom Research and Innovation\n",
    "#  Copyright 2025 Bangor University\n",
    "#\n",
    "#  Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "#  you may not use this file except in compliance with the License.\n",
    "#  You may obtain a copy of the License at\n",
    "#\n",
    "#      http://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "#  Unless required by applicable law or agreed to in writing, software\n",
    "#  distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "#  See the License for the specific language governing permissions and\n",
    "#  limitations under the License.\n",
    "#\n",
    "#   Authored by:    Franck Vidal (UKRI-STFC) & Iwan Mitchell (Bangor University)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ffcfed3-2145-4ba8-8715-17327e58b0d2",
   "metadata": {
    "id": "4ffcfed3-2145-4ba8-8715-17327e58b0d2",
    "tags": []
   },
   "source": [
    "![gVXR](https://github.com/TomographicImaging/gVXR-Tutorials/blob/main/img/Logo-transparent-small.png?raw=1)\n",
    "\n",
    "# Single-material example: reproducing the CT scan of a teapot using a JSON file\n",
    "\n",
    "They say French people drink coffee not tea.\n",
    " However, we found a teapot in the lab and scanned it. \n",
    " It's made of a dense material (cast iron), making it interesting to scan. \n",
    " It also has a fine wavy texture. \n",
    " We acquired the data presented below with the [Dual Tube High Energy (DTHE) device](https://www.rx-solutions.com/en/blog/126/dthe-technology) by [RX Solutions](https://www.rx-solutions.com/) installed at the [MATEIS Laboratory](https://mateis.insa-lyon.fr/en) of [INSA-Lyon (France)](http://www.insa-lyon.fr/). The DTHE is a double tomograph designed around one rotation axis and two 300 kV X-ray beamlines. \n",
    "We'll aim to reproduce one of the scans we made. \n",
    "We will take into account the source and detector properties as finely as possible. \n",
    "We'll use the metadata generated by the device to create a JSON file that describes our virtual experiment.\n",
    "It'll makes it so much easier than typing all the Python code:\n",
    "Describe the simulation in a human-friendly format, load the file and there you go!\n",
    "We will eventually use CIL to reconstruct the CT data with the traditional FDK algorithm.\n",
    "\n",
    "<!-- ![Sample on the turntable of the device.](https://github.com/TomographicImaging/gVXR-Tutorials/blob/main/data/Al-part/IMG_3508-smaller.jpg?raw=1) -->\n",
    "\n",
    "![Sample on the turntable of the device.](https://github.com/TomographicImaging/gVXR-Tutorials/blob/main/data/theiere-Fe-10mm/IMG_3458-smaller.jpg?raw=1)\n",
    "\n",
    "\n",
    "<!-- ![Rendering of the simulation](https://github.com/TomographicImaging/gVXR-Tutorials/blob/main/notebooks/output_data/single_material-reproducing_CT_scan/k3d_screenshot-cropped.png?raw=1) -->\n",
    "\n",
    "\n",
    "<!-- Up to now, we generated 2D radiographs. \n",
    "This time we are going to simulate the computed tomography (CT) acquisition of a section of electric cable. \n",
    "A cone-beam geometry is used. \n",
    "To keep it simple, we consider an ideal case: no scintillation, no point-spread function (PSF), and a monochromatic spectrum. -->\n",
    "\n",
    "<!-- We will use CIL to reconstruct the CT data with the traditional FDK algorithm. -->\n",
    "\n",
    "\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "    <b>Note:</b> Make sure the Python packages are already installed. See <a href=\"../README.md\">README.md</a> in the root directory of the repository. If you are running this notebook from Google Colab, please run the cell below to install the required packages.\n",
    "</div>"
   ]
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   "id": "8ba4b4be-a52c-4fa4-9122-3eb873a7ce4d",
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   "source": [
    "## Aim and objectives of this session\n",
    "\n",
    "The aim of this session is to replicate an actual scan.\n",
    "\n",
    "To achieve this, the main objectives are to:\n",
    "\n",
    "1. Identify the parameters are of importance in the metadata file(s) produced by the device.\n",
    "2. Describe the simulation and reconstruction in a human-friendly file.\n",
    "3. Run the simulation using this file instead of writing a lot of Python code.\n",
    "4. Perform the reconstruction using CIL.\n",
    "\n",
    "![Screenshot of the 3D environment using K3D](https://github.com/TomographicImaging/gVXR-Tutorials/blob/main/notebooks/output_data/single_material-reproducing_CT_scan-JSON_file/k3d_screenshot-cropped.png?raw=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b2b9e4b",
   "metadata": {},
   "source": [
    "## Import packages\n",
    "\n",
    "<!-- - `pandas`\n",
    "- `xml.etree` -->\n",
    "\n",
    "<!-- - `os` to create the output directory if needed\n",
    "- `matplotlib` to show 2D images\n",
    "- `viewscad` to use OpenSCAD and create STL files\n",
    "- `base64` to use save a screenshot of the K3D visualisation\n",
    "- `tifffile.imread` to read the X-ray projection of the real experiment\n",
    "- `xml.etree.ElementTree` to read the scanning parameters of the real experiment\n",
    "- `gvxrPython3.gvxr` to simulate X-ray images\n",
    "- `gvxrPython3.visualise` to use K3D to visualise the 3D environment\n",
    "- `gvxrPython3.gvxr2json` to save the state of the simulation in JSON file\n",
    "- `gvxrPython3.JSON2gVXRDataReader` to read the simulated data and prepare it for CIL\n",
    "- `cil.recon.FDK` to reconstruct the CT volume using the FDK algorithm (3D-CBCT only) \n",
    "- `cil.recon.FBP` to reconstruct the CT volume using the FBP algorithm (2D/3D parallel geometry only) \n",
    "- `cil.io.TIFFWriter` to save the reconstructed slices as a TIFF stack\n",
    "- `cil.processors.TransmissionAbsorptionConverter` to normalise the data and apply the minus log\n",
    "- `cil.utilities.display.show_geometry` to display the acquisition geometry\n",
    "- `cil.utilities.display.show2D` to show static 2D images\n",
    "- `cil.utilities.jupyter.islicer` to show and navigate a stack of 2D images using a slider\n",
    "- `cil.utilities.jupyter.link_islicer` to compare and navigate stacks of 2D images using a slider -->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4e64e49",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os # Create the output directory if necessary\n",
    "import numpy as np # Who does not use Numpy?\n",
    "\n",
    "# import math\n",
    "\n",
    "import matplotlib # To plot images\n",
    "import matplotlib.pyplot as plt # Plotting\n",
    "import matplotlib.gridspec as gridspec\n",
    "\n",
    "font = {'family' : 'serif',\n",
    "         'size'   : 15\n",
    "       }\n",
    "matplotlib.rc('font', **font)\n",
    "\n",
    "# # Uncomment the line below to use LaTeX fonts\n",
    "# # matplotlib.rc('text', usetex=True)\n",
    "\n",
    "# import viewscad # Use OpenSCAD to create STL files\n",
    "\n",
    "import base64\n",
    "\n",
    "from tifffile import imread, imwrite\n",
    "\n",
    "# import xml.etree.ElementTree as ET\n",
    "\n",
    "from gvxrPython3 import gvxr\n",
    "from gvxrPython3 import json2gvxr\n",
    "\n",
    "# import pandas\n",
    "from xml.etree import ElementTree\n",
    "from gvxrPython3.utils import visualise\n",
    "\n",
    "from gvxrPython3 import gvxr2json\n",
    "from gvxrPython3 import json2gvxr\n",
    "\n",
    "\n",
    "from tqdm import tqdm\n",
    "\n",
    "\n",
    "from gvxrPython3.JSON2gVXRDataReader import *\n",
    "\n",
    "from cil.recon import FDK # For CBCT\n",
    "# from cil.recon import FBP # For parallel beam geometry\n",
    "\n",
    "from cil.io import TIFFWriter\n",
    "\n",
    "from cil.processors import TransmissionAbsorptionConverter\n",
    "from cil.utilities.display import show_geometry, show2D\n",
    "from cil.utilities.jupyter import islicer, link_islicer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adfa643e",
   "metadata": {},
   "source": [
    "## Getting the data ready\n",
    "\n",
    "Where to save the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9ea6181",
   "metadata": {},
   "outputs": [],
   "source": [
    "output_path = \"../notebooks/output_data/single_material-reproducing_CT_scan-JSON_file\"\n",
    "if not os.path.exists(output_path):\n",
    "    os.makedirs(output_path);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e174bf3-e7a7-4df9-a0ad-75baccc01d4c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1e174bf3-e7a7-4df9-a0ad-75baccc01d4c",
    "outputId": "64c02357-fae0-41b6-a185-a3f9cbe48b2f"
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "IN_COLAB = 'google.colab' in sys.modules\n",
    "\n",
    "dataset_path = \"../data/theiere-Fe-10mm\"\n",
    "projection_fname = \"Proj/img_00598.tif\"\n",
    "JSON_fname = os.path.join(dataset_path, \"parameters.json\")\n",
    "\n",
    "if IN_COLAB:\n",
    "    !pip install -q condacolab\n",
    "    import condacolab\n",
    "    condacolab.install()\n",
    "\n",
    "    !conda install -y -c conda-forge -c https://software.repos.intel.com/python/conda -c ccpi cil=24.2.0 ipp=2021.12 astra-toolbox=*=cuda* tigre k3d\n",
    "\n",
    "    !pip install gvxr\n",
    "\n",
    "    import os, urllib.request\n",
    "\n",
    "    if not os.path.exists(os.path.join(dataset_path, \"Proj\")):\n",
    "        os.makedirs(os.path.join(dataset_path, \"Proj\"));\n",
    "\n",
    "    files = [os.path.join(dataset_path, \"parameters.json\"),\n",
    "             os.path.join(dataset_path, \"geom.csv\"),\n",
    "             os.path.join(dataset_path, \"restore.macro\"),\n",
    "             os.path.join(dataset_path, \"unireconstruction.xml\"),\n",
    "             os.path.join(dataset_path, projection_fname),\n",
    "             \"notebooks/gvxr2json.py\",\n",
    "             \"notebooks/json2gvxr.py\",\n",
    "            ];\n",
    "\n",
    "    for fname in files:\n",
    "        url = \"https://github.com/TomographicImaging/gVXR-Tutorials/raw/refs/heads/main/\" + fname;\n",
    "        print(\"Download\", url);\n",
    "        urllib.request.urlretrieve(url, fname);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17e42b9b",
   "metadata": {},
   "source": [
    "# 1. Identify the parameters are of importance in the metadata file(s) produced by the device"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea0ad5b2-dab2-4df9-9128-5d1292dc4bb7",
   "metadata": {
    "id": "ea0ad5b2-dab2-4df9-9128-5d1292dc4bb7"
   },
   "source": [
    "## Summary of the ~~simulation~~ experimental parameters\n",
    "\n",
    "| Parameters | Values | Units |\n",
    "|------------|--------|-------|\n",
    "| Source type (beam shape) | conebeam | |\n",
    "| Beamline   | ?? | |\n",
    "| Number of projections | ?? | |\n",
    "| Tube voltage | ?? | kV |  |\n",
    "| Tube current | ?? | &mu;A |  |\n",
    "| Source to detector distance | ?? | mm |\n",
    "| Source to object distance | ?? | mm |\n",
    "| Object to detector distance | ?? | mm |\n",
    "| Filter material | ?? | |\n",
    "| Filter thickness | ?? | mm |\n",
    "| Exposure time | ?? | sec |\n",
    "| Exposure | ??| mAs |\n",
    "| Pixel pitch | ?? x ?? | &mu;m |\n",
    "| Image size | ?? x ?? | pixels |\n",
    "\n",
    "\n",
    "<!-- | Detector orientation | [0, 0, 1] |  |\n",
    "| Detector resolution | ?? &times; ?? | pixels |\n",
    "| Pixel pitch | 150 &times; 150 | um | -->"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4511ea8",
   "metadata": {},
   "source": [
    "The table above should contain all the parameters of interest that we need to fully describe our simulation and CT acquisition. \n",
    "It contains a lot of `??`. It's your job to complete it! For that purpose we will extract all the useful data from the metadata file. There are two of interest in the case of the DTHE:\n",
    "\n",
    "- [unireconstruction.xml](../data/theiere-Fe-10mm/unireconstruction.xml)\n",
    "- [restore.macro](../data/theiere-Fe-10mm/restore.macro)\n",
    "\n",
    "---\n",
    "## Tasks:\n",
    "\n",
    "1. Double-click on the table above to edit it.\n",
    "2. Open [unireconstruction.xml](../data/theiere-Fe-10mm/unireconstruction.xml).\n",
    "3. As there are two beamlines, we need to know which one was use. \n",
    "    - Look for `Imager_1` or `Imager_2` in the `profile acquisition`.\n",
    "    - As we are at it, identify how many images were captured.\n",
    "    - Report the corresponding information in the table.\n",
    "4. We will now record the details about the source. With the voltage and current, we'll be able to generate the spectrum and the flux.\n",
    "    - Look for `voltage` or `current`.\n",
    "    - Report the corresponding information in the table.\n",
    "5. Do the same as above for source to detector distance (`sdd`) and the source to object distance (`sod`). Note that the distances are given in millimetres. You can see in the picture above that the distance from the source to the teapot is roughly 40 cm. \n",
    "6. Uncomment all the lines of the cell below and write down the corresponding values.\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57696225",
   "metadata": {},
   "outputs": [],
   "source": [
    "# nb_images = \n",
    "# voltage_in_kV = \n",
    "# current_in_uA = \n",
    "# sdd_in_mm = \n",
    "# sod_in_mm = "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5da1d3f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %load 'snippets/single_material-reproducing_CT_scan-JSON_file-01.py'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e563446",
   "metadata": {},
   "source": [
    "Assuming that we will centre the teapot in the middle of our world, we will need to know the object to detector distance (odd) to position the detector.\n",
    "\n",
    "\n",
    "---\n",
    "## Tasks:\n",
    "\n",
    "1. Uncomment the line in the cell below,\n",
    "2. Compute the `odd` using the `sdd` and `sod`.\n",
    "2. Report the value in the table.\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5c7478a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# odd_in_mm = "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be832147",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %load 'snippets/single_material-reproducing_CT_scan-JSON_file-02.py'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e33a01f",
   "metadata": {},
   "source": [
    "We are done with [unireconstruction.xml](../data/theiere-Fe-10mm/unireconstruction.xml). \n",
    "We'll have a look at [restore.macro](../data/theiere-Fe-10mm/restore.macro), another XML file. \n",
    "This file could be used to restore the device to the state it was when the file was recorded. \n",
    "\n",
    "---\n",
    "## Tasks:\n",
    "\n",
    "1. Double-click on the table above to edit it.\n",
    "2. Open [restore.macro](../data/theiere-Fe-10mm/restore.macro).\n",
    "3. Some information is redundant and it is worth double-checking if possible.\n",
    "    - Identify the object named `ExtendedTomo`.\n",
    "        - Is the imager the same as what we expected (i.e. `Imager_2`)?\n",
    "        - Is the number of images per turn the same too (i.e. 1504)?\n",
    "    - Identify the object named `XRay300_2`.\n",
    "        - What about the voltage,\n",
    "        - and the current?\n",
    "        - Was the beam on (see `xRayEnabled`)?\n",
    "4. Have a look at the photograph above. Can you notice that something is held between the source and the sample? It is the filter that will alter the beam spectrum in order to reduce the relative amount of low energy photons, i.e. reduce beamhardening.\n",
    "5. Go back to [restore.macro](../data/theiere-Fe-10mm/restore.macro) and checkout all the fields of the object names `XRay300_2`.\n",
    "    - Identify the filter.\n",
    "    - In the cell below, uncomment all the lines of code, and input the values of `filter_material`, `filter_thickness`, and `thickness_unit`. Note that `filter_material` and `thickness_unit` are strings; `filter_thickness` int or float.\n",
    "    - Report the corresponding information in the table.\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df0af106",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter_material = \"\"\n",
    "# filter_thickness = \n",
    "# thickness_unit = \"\"\n",
    "\n",
    "# filtration = [\n",
    "#     [filter_material, filter_thickness, thickness_unit]\n",
    "# ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca3ac387",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %load 'snippets/single_material-reproducing_CT_scan-JSON_file-03.py'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb0df847",
   "metadata": {},
   "source": [
    "There is one more thing we need: the exposure time. We'll be able to generate the right amount of noise with this information.\n",
    "The exposure is related to the detector/imager. We will record the exposure in seconds and compute it in terms of mAs (the useful one to control the noise).\n",
    "\n",
    "---\n",
    "## Tasks:\n",
    "\n",
    "- Identify the object names `Imager_2` in [restore.macro](../data/theiere-Fe-10mm/restore.macro).\n",
    "- What is the exposure?\n",
    "- It is an index in a lookup table, which is provided below.\n",
    "- Uncomment the line with `exposure_ID = ` and input the corresponding index.\n",
    "- Compute the exposure in milliampereseconds, i.e. the product of the current in mA and exposure time in seconds.\n",
    "- Report the corresponding information in the table.\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d8f468e",
   "metadata": {},
   "outputs": [],
   "source": [
    "exposure_lookup_table = [0.067, 0.083, 0.111, 0.167, 0.333, 0.500] # [s]\n",
    "# exposure_ID = \n",
    "exposure_time_in_sec = exposure_lookup_table[exposure_ID - 1]\n",
    "mAs = exposure_time_in_sec * current_in_uA / 1000\n",
    "\n",
    "print(\"Exposure time:\", exposure_time_in_sec, \"in sec\")\n",
    "print(\"Exposure:\", mAs, \"mAs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98e06092",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %load 'snippets/single_material-reproducing_CT_scan-JSON_file-04.py'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2748468f",
   "metadata": {},
   "source": [
    "We have now collected all the information we needed from the metadata. We are still missing two pieces of information. The image size and the pixel pitch (space between the centre of two consecutive pixels). I can help you, it's 150 &mu;m along both directions.\n",
    "\n",
    "---\n",
    "## Task:\n",
    "\n",
    "- Report the corresponding information in the table.\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0cac08b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "pixel_pitch_in_um = np.array([150, 150])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ac93b62",
   "metadata": {},
   "source": [
    "We also compute the magnification as it will give a sence of the sample size when displaying radiographs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "665cf2b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "magnification = sdd_in_mm / sod_in_mm;\n",
    "print(\"Magnification:\", magnification)\n",
    "spacing_in_mm = (pixel_pitch_in_um / magnification) / 1000.0;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aed993af",
   "metadata": {},
   "source": [
    "For the image size, we'll open and display a radiograph. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7babc918",
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_radiograph_reference = np.array(imread(os.path.join(dataset_path, projection_fname)), dtype=np.single)\n",
    "\n",
    "# Average of the pixel intensity over the first 400 rows, i.e. no object\n",
    "white_value = raw_radiograph_reference[:400,:].mean()\n",
    "\n",
    "# Normalisation\n",
    "raw_radiograph_reference /= white_value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d500cdd7",
   "metadata": {},
   "source": [
    "---\n",
    "## Tasks:\n",
    "\n",
    "- Run the cell below,\n",
    "- Report the corresponding information in the table.\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3df60ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "projection_number_of_rows = raw_radiograph_reference.shape[0]\n",
    "projection_number_of_cols = raw_radiograph_reference.shape[1]\n",
    "\n",
    "print(\"Image size:\", str(projection_number_of_cols) + \"x\" + str(projection_number_of_rows), \"pixels\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6288782",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %load 'snippets/single_material-reproducing_CT_scan-JSON_file-05.py'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98690c49",
   "metadata": {},
   "source": [
    "and display it either as an attenuation image or absorption image (using the $-\\log$ of the attenuation)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eec2bab0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot the two images side-by-side\n",
    "fig = plt.figure(figsize=(15, 7))\n",
    "gs = gridspec.GridSpec(1, 2)\n",
    "axes = [fig.add_subplot(ss) for ss in gs]\n",
    "\n",
    "extent=[0,(raw_radiograph_reference.shape[1]-1)*spacing_in_mm[0],0,(raw_radiograph_reference.shape[0]-1)*spacing_in_mm[1]]\n",
    "\n",
    "im1 = axes[0].imshow(raw_radiograph_reference, cmap=\"gray\", extent=extent)\n",
    "im2 = axes[1].imshow(-np.log(raw_radiograph_reference), cmap=\"gray\", extent=extent)\n",
    "\n",
    "plt.suptitle(\"Digital radiograph of a teapot\")\n",
    "\n",
    "axes[0].set_title(\"Attenuation\")\n",
    "axes[1].set_title(\"Absorption\")\n",
    "\n",
    "axes[0].set_xlabel(\"Pixel position\\n(in mm)\")\n",
    "axes[1].set_xlabel(\"Pixel position\\n(in mm)\")\n",
    "axes[0].set_ylabel(\"Pixel position\\n(in mm)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig(output_path + '/reference-radiograph.pdf')\n",
    "plt.savefig(output_path + '/reference-radiograph.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce52d78e",
   "metadata": {},
   "source": [
    "# 2. Describe the simulation and reconstruction in a human-friendly file\n",
    "\n",
    "We will populate the file [../data/theiere-Fe-10mm/parameters.json](../data/theiere-Fe-10mm/parameters.json) with information from our table. \n",
    "Note, when file or directory paths are given, they are relative to the path of the JSON file. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b13bb1a",
   "metadata": {},
   "source": [
    "- The file typically starts with:\n",
    "\n",
    "    ```json\n",
    "    {\n",
    "        \"File format version\": [1, 0, 0],\n",
    "        \"Window size\": [500, 500],\n",
    "\n",
    "        ...\n",
    "    }\n",
    "    ```\n",
    "    It is to initialise the simulation engine and the 3D visualisation environment."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "130de02e",
   "metadata": {},
   "source": [
    "- It is followed by the detector. The JSON format would be:\n",
    "    ```json\n",
    "    {\n",
    "        ...\n",
    "\n",
    "        \"Detector\": {\n",
    "            \"Position\": [0.0, ODD, 0.0, \"mm\"],\n",
    "            \"UpVector\": [0.0, 0.0, -1.0],\n",
    "            \"RightVector\": [-1.0, 0.0, 0.0],\n",
    "            \"NumberOfPixels\": [COLS, ROWS],\n",
    "            \"Spacing\": [PITCH, PITCH, \"um\"],\n",
    "            \"Scintillator\": {\n",
    "                \"Material\": \"Gd2O2S DRZ-Plus\",\n",
    "                \"Thickness\": 210,\n",
    "                \"Unit\": \"um\"\n",
    "            }\n",
    "        },\n",
    "        \n",
    "        ...\n",
    "    }\n",
    "    ```\n",
    "\n",
    "---\n",
    "## Tasks:\n",
    "\n",
    "- To speeds thing up, particularly on laptops or on Cloud instances without OpenGL support (e.g. Google Colab), \n",
    "    - multiply the pixel pitch by 4 and \n",
    "    - divide the number of pixels by 4. Make sure to use an integer division!\n",
    "- Open and edit [../data/theiere-Fe-10mm/parameters.json](../data/theiere-Fe-10mm/parameters.json) to\n",
    "- Replace with their respective values:\n",
    "    - `ODD`, \n",
    "    - `COLS`, \n",
    "    - `ROWS`, and\n",
    "    - `PITCH`.\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc5ad4cf",
   "metadata": {},
   "source": [
    "- We can now worry about the source. The JSON format would be:\n",
    "\n",
    "    ```json\n",
    "    {\n",
    "        ...\n",
    "        \n",
    "        \"Source\": {\n",
    "            \"Position\": [0.0, MINUS SOD, 0.0, \"mm\"],\n",
    "            \"Shape\": \"CONEBEAM\",\n",
    "            \"Beam\": {\n",
    "                \"Peak kilo voltage\": VOLTAGE_IN_kV,\n",
    "                \"Tube angle\": 12.0,\n",
    "                \"mAs\": mAS_IF_KNOWN,\n",
    "                \"filter\": [\n",
    "                    [\"SYMBOL_OF_FILTER_MATERIAL\", FILTER_THICKNESS, \"THICKNESS_UNIT\"]\n",
    "                ],\n",
    "                \"MaxNumberOfEnergyBins\": 50\n",
    "            }\n",
    "        },\n",
    "\n",
    "        ...\n",
    "\n",
    "    }\n",
    "    ```\n",
    "\n",
    "    Note that the shape could be `PARALLEL` to mimick synchrotron radiation. There are also other ways to specify the beam spectrum. `maxNumberOfEnergyBins` is used to reduce the number of energy bins in the spectrum without altering the flux. If it is too high, and the resoultion too high, then the simulation will be very slooOoooOoow.\n",
    "\n",
    "---\n",
    "## Tasks:\n",
    "\n",
    "Replace with their respective values:\n",
    "\n",
    "- `MINUS SOD`, \n",
    "- `VOLTAGE_IN_kV`, \n",
    "- `mAS_IF_KNOWN`,\n",
    "- `SYMBOL_OF_FILTER_MATERIAL`,\n",
    "- `FILTER_THICKNESS`, and \n",
    "- `THICKNESS_UNIT`.\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6799ff7",
   "metadata": {},
   "source": [
    "- We can now add our beautiful teapot. It is made of cast iron. Its density is about 6.675 g.cm<sup>-3</sup>. The material composition is roughly:\n",
    "\n",
    "    - Iron (Z=26): 95% of the alloy,\n",
    "    - Carbon (Z=6): 3%,\n",
    "    - Silicon: 2%.\n",
    "\n",
    "    As the teapot is cast iron, it's matt black and we can set the colour for the 3D visualisation. To make things easy, we move the samples to the centre of the world. We also rotate the teapot so that it is oriented as it was in the experimental scan.\n",
    "\n",
    "```json\n",
    "{\n",
    "    ...\n",
    "\n",
    "        \"Samples\": [\n",
    "            \"MoveToCentre\",\n",
    "            \n",
    "            {\n",
    "                \"Label\": \"Teapot\",\n",
    "                \"Path\": \"models-teapot-decimated.stl\",\n",
    "                \"Unit\": \"mm\",\n",
    "                \"Material\": [\n",
    "                    \"Mixture\", [\n",
    "                        26, 0.95,\n",
    "                        6, 0.03,\n",
    "                        14, 0.02\n",
    "                    ]\n",
    "                ],\n",
    "                \"Density\": 5.5,\n",
    "                \"AmbientColour\": [ 0.20, 0.20, 0.20, 1.0 ],\n",
    "                \"DiffuseColour\": [ 0.0, 0.0, 0.0, 1.0 ],\n",
    "                \"SpecularColour\": [ 0.0, 0.0, 0.0, 1.0 ],\n",
    "                \"Shininess\": 20.0\n",
    "            }\n",
    "        ],\n",
    "\n",
    "\n",
    "    ...\n",
    "}\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5561c868",
   "metadata": {},
   "source": [
    "- We finally describe the CT acquisition with:\n",
    "\n",
    "    ```json\n",
    "    {\n",
    "        ...\n",
    "        \n",
    "        \"Scan\": {\n",
    "            \"NumberOfProjections\": NUMBER_OF_PROJECTION,\n",
    "            \"FinalAngle\": 360,\n",
    "            \"IncludeFinalAngle\": false,\n",
    "            \"CenterOfRotation\": [0,0,0],\n",
    "            \"RotationAxis\": [SAME_AS_UpVector_OF_DETECTOR],\n",
    "            \"OutFolder\": \"../notebooks/output_data/single_material-reproducing_CT_scan-JSON_file/Proj-simulated\"\n",
    "        }\n",
    "    }\n",
    "\n",
    "---\n",
    "## Tasks:\n",
    "\n",
    "Replace with their respective values:\n",
    "\n",
    "- `NUMBER_OF_PROJECTION`, and \n",
    "- `SAME_AS_UpVector_OF_DETECTOR`.\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5ad6322",
   "metadata": {},
   "source": [
    "# 3. Run the simulation using this file"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84713b9b",
   "metadata": {},
   "source": [
    "Initialise the simulation engine and the 3D visualisation environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea471ba1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# JSON_fname = os.path.join(dataset_path, \"parameters-solution.json\")\n",
    "JSON_fname = os.path.join(dataset_path, \"parameters.json\")\n",
    "json2gvxr.initGVXR(JSON_fname)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d89414ed",
   "metadata": {},
   "source": [
    "We can now load the detector properties."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1191ec86",
   "metadata": {},
   "outputs": [],
   "source": [
    "json2gvxr.initDetector(verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d42ff3ef",
   "metadata": {},
   "source": [
    "Display the energy response of the detector corresponding to the scintillator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad6f085a",
   "metadata": {},
   "outputs": [],
   "source": [
    "detector_response = np.array(gvxr.getEnergyResponse(\"keV\"));\n",
    "\n",
    "plt.figure(figsize= (20,10))\n",
    "# plt.title(\"Detector response\")\n",
    "plt.plot(detector_response[:,0], detector_response[:,1])\n",
    "plt.xlabel('Incident energy: E (in keV)')\n",
    "plt.ylabel('Detector energy response: $\\\\delta$(E) (in keV)')\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig(output_path + '/detector_response.pdf')\n",
    "plt.savefig(output_path + '/detector_response.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a753fc5c",
   "metadata": {},
   "source": [
    "We can now load the geometrical source properties."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5dfc7850",
   "metadata": {},
   "outputs": [],
   "source": [
    "json2gvxr.initSourceGeometry(verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb9d5999",
   "metadata": {},
   "source": [
    "and the spectrum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5cb0edfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "spectrum, unit, k, f = json2gvxr.initSpectrum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56be2a70",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize= (20,10))\n",
    "# plt.title(\"Beam spectrum\")\n",
    "plt.bar(k, f, width=1)\n",
    "plt.xlabel('Energy in keV')\n",
    "plt.ylabel('Probability distribution of photons per keV')\n",
    "plt.savefig(output_path + \"/spectrum.pdf\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "782ce728-1b50-4fcb-a76e-535538ebd34e",
   "metadata": {},
   "source": [
    "If you have enough computational power, you may enable Poisson noise. It'll be based on the exposure in mAs. If you use the same pixel resolution as the experimental data, you'll see it is quite realistic!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c268984a-0460-4be9-9db8-8abf522deb35",
   "metadata": {},
   "outputs": [],
   "source": [
    "gvxr.disablePoissonNoise()\n",
    "# gvxr.enablePoissonNoise()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cd1c1c4",
   "metadata": {},
   "source": [
    "We load the sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78398a12",
   "metadata": {},
   "outputs": [],
   "source": [
    "json2gvxr.initSamples(verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22b532bf-5d51-4564-b75c-b0632f9ba990",
   "metadata": {},
   "source": [
    "We are now ready to compute an X-ray image and display it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba709949-ac29-43bb-84b8-867a57d32b33",
   "metadata": {
    "id": "ba709949-ac29-43bb-84b8-867a57d32b33"
   },
   "outputs": [],
   "source": [
    "x_ray_image = np.array(gvxr.computeXRayImage(), dtype=np.single) / gvxr.getTotalEnergyWithDetectorResponse();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ced67736-c499-4056-b455-45106e94cffd",
   "metadata": {
    "id": "ced67736-c499-4056-b455-45106e94cffd"
   },
   "source": [
    "We can display it side-by-side with the experimental image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "665db86a-40a2-499c-9e16-b49de17ee0ac",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 528
    },
    "id": "665db86a-40a2-499c-9e16-b49de17ee0ac",
    "outputId": "88b301f2-6d7f-433f-9391-e29196e23fdb"
   },
   "outputs": [],
   "source": [
    "# Plot the two images side-by-side\n",
    "fig = plt.figure(figsize=(15, 7))\n",
    "gs = gridspec.GridSpec(1, 2)\n",
    "axes = [fig.add_subplot(ss) for ss in gs]\n",
    "\n",
    "extent=[0,(raw_radiograph_reference.shape[1]-1)*spacing_in_mm[0],0,(raw_radiograph_reference.shape[0]-1)*spacing_in_mm[1]]\n",
    "\n",
    "im1 = axes[0].imshow(raw_radiograph_reference, cmap=\"gray\", extent=extent, vmin=0, vmax=1)\n",
    "im2 = axes[1].imshow(x_ray_image, cmap=\"gray\", extent=extent, vmin=0, vmax=1)\n",
    "\n",
    "axes[0].plot([extent[1] // 2, extent[1] // 2], [0, extent[3]])\n",
    "axes[1].plot([extent[1] // 2, extent[1] // 2], [0, extent[3]])\n",
    "\n",
    "plt.suptitle(\"Digital radiograph of a teapot\")\n",
    "\n",
    "axes[0].set_title(\"Experimental\")\n",
    "axes[1].set_title(\"Simulated\")\n",
    "\n",
    "axes[0].set_xlabel(\"Pixel position\\n(in mm)\")\n",
    "axes[1].set_xlabel(\"Pixel position\\n(in mm)\")\n",
    "axes[0].set_ylabel(\"Pixel position\\n(in mm)\")\n",
    "\n",
    "axes[0].set_ylim(19, extent[3])\n",
    "axes[1].set_ylim(19, extent[3])\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig(output_path + '/difference-radiograph.pdf')\n",
    "plt.savefig(output_path + '/difference-radiograph.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e27f57f8-ccd6-4e44-9feb-cf1013b41314",
   "metadata": {},
   "source": [
    "and compare intensity profiles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9eb76b1a-6979-4b0d-a8a9-899ba21bae89",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot the two images side-by-side\n",
    "fig = plt.figure(figsize=(10, 7))\n",
    "\n",
    "plt.plot(np.linspace(0,(raw_radiograph_reference.shape[1]-1)*spacing_in_mm[0], raw_radiograph_reference.shape[1]),\n",
    "         raw_radiograph_reference[:, raw_radiograph_reference.shape[1] // 2], \n",
    "         label=\"Experimental\")\n",
    "\n",
    "plt.plot(np.linspace(0,(raw_radiograph_reference.shape[1]-1)*spacing_in_mm[0], x_ray_image.shape[1]),\n",
    "         x_ray_image[:, x_ray_image.shape[1] // 2],\n",
    "         label=\"Simulated\")\n",
    "\n",
    "plt.title(\"Intensity profiles\")\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig(output_path + '/difference-radiograph-profiles.pdf')\n",
    "plt.savefig(output_path + '/difference-radiograph-profiles.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdcb7ff7-00c2-4593-b8d4-eead24531761",
   "metadata": {},
   "source": [
    "The main difference may be explained:\n",
    "- The material composition is actually unknown. We used average values found on the web.\n",
    "- The simulation is not registered on the experimental data.\n",
    "- The impulse response of the detector has been ignored (it could be added if known).\n",
    "- The focal spot of the tube has been ignored (it could be added if known).\n",
    "- If Poisson noise was enable, it would have been reduced compared to the experimental data as we downsampled the detector. Same number of photons per cm<sup>2</sup> but a lot more per pixels."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5d86e8a-6bb6-4536-aa33-d48e3abd9575",
   "metadata": {
    "id": "b5d86e8a-6bb6-4536-aa33-d48e3abd9575",
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "It may be useful to visualise the 3D environment to ascertain everything is as expected. We use k3D if possible. It's a nice 3D visualisation framework for Jupyter notebooks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c6958eb-6bb5-449c-bd60-3c810828578b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 529,
     "referenced_widgets": [
      "31758d98413b4a45893d3a6cff28aa46",
      "b1e8819fca7243659303974a19d56e3b",
      "e829a05c9d684439b72f26128d63a665",
      "92cf90640f5f4b8bbae4f8a8de220e28"
     ]
    },
    "id": "3c6958eb-6bb5-449c-bd60-3c810828578b",
    "outputId": "124ebaf5-4d99-4804-c619-16803f40f150"
   },
   "outputs": [],
   "source": [
    "if IN_COLAB:\n",
    "    from google.colab import output\n",
    "    output.enable_custom_widget_manager()\n",
    "\n",
    "plot = visualise(use_log=True, use_negative=True, sharpen_ksize=2, sharpen_alpha=1.0);\n",
    "\n",
    "if plot:\n",
    "    plot.display();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba4c6d06-9c35-4b42-a375-23b2c010e58c",
   "metadata": {
    "id": "ba4c6d06-9c35-4b42-a375-23b2c010e58c"
   },
   "outputs": [],
   "source": [
    "if plot:\n",
    "    plot.fetch_screenshot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee6e2b76-bcd7-4327-b9a3-335d9d2daa2f",
   "metadata": {
    "id": "ee6e2b76-bcd7-4327-b9a3-335d9d2daa2f"
   },
   "outputs": [],
   "source": [
    "if plot:\n",
    "    screenshot_fname = os.path.join(output_path, \"k3d_screenshot.png\");\n",
    "    if not os.path.exists(screenshot_fname):\n",
    "        k3d_screenshot = plot.screenshot;\n",
    "        data = base64.b64decode(k3d_screenshot);\n",
    "        with open(screenshot_fname, \"wb\") as fp:\n",
    "            fp.write(data);\n",
    "            fp.flush();\n",
    "            fp.close();\n",
    "\n",
    "if IN_COLAB:\n",
    "    output.disable_custom_widget_manager()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b44447a6-526b-4cff-936b-c52808dd2098",
   "metadata": {
    "id": "b44447a6-526b-4cff-936b-c52808dd2098"
   },
   "source": [
    "Compute the X-ray projections for a CT acquisition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33c9412c-c0ed-4eb8-8ff4-804babbe84f0",
   "metadata": {
    "id": "33c9412c-c0ed-4eb8-8ff4-804babbe84f0"
   },
   "outputs": [],
   "source": [
    "json2gvxr.initScan()\n",
    "angles=json2gvxr.doCTScan()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4dd4d84-fff0-405e-afa7-058252ada4c2",
   "metadata": {},
   "source": [
    "# 4. Perform the reconstruction using CIL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "588f216c-59c1-407d-8bae-a1169e4583db",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "588f216c-59c1-407d-8bae-a1169e4583db",
    "outputId": "19545aef-9710-49fd-a03d-1ecb7470a562"
   },
   "outputs": [],
   "source": [
    "# Read the simulated data with CIL.\n",
    "reader = JSON2gVXRDataReader(JSON_fname);\n",
    "data = reader.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "073f7724-5eb1-4024-a54d-8f321d6e72a2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "073f7724-5eb1-4024-a54d-8f321d6e72a2",
    "outputId": "5fe1366d-71ca-4aba-f949-99b6f60e121e"
   },
   "outputs": [],
   "source": [
    "print(\"data.geometry\", data.geometry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7606c60a",
   "metadata": {},
   "outputs": [],
   "source": [
    "show_geometry(data.geometry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2782e164-e253-4857-b448-d524fefa3bdd",
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    "colab": {
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     "height": 824
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    "id": "2782e164-e253-4857-b448-d524fefa3bdd",
    "outputId": "6573b00a-265a-4e0f-ceec-939bd5818c8c"
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   "outputs": [],
   "source": [
    "show2D(data, origin='upper-left')"
   ]
  },
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    "islicer(data, origin='upper-left')"
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   "cell_type": "code",
   "execution_count": null,
   "id": "f94ee485-2a84-4cb5-9ff8-90b8ed1958e0",
   "metadata": {
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   "source": [
    "data_corr = TransmissionAbsorptionConverter(white_level=data.max())(data)"
   ]
  },
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   "cell_type": "code",
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    "show2D(data_corr, origin='upper-left')"
   ]
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   "source": [
    "islicer(data_corr, origin='upper-left')"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ebb3d8e6-3356-4c52-9aec-3dd51517c140",
   "metadata": {
    "id": "ebb3d8e6-3356-4c52-9aec-3dd51517c140"
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   "source": [
    "data_corr.reorder(order='tigre')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de95bef8-216b-4b24-84ec-78c0d7b08459",
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    "colab": {
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    "id": "de95bef8-216b-4b24-84ec-78c0d7b08459",
    "outputId": "b5a66bd6-231c-4ee7-b693-15b8cd3f774f"
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   "source": [
    "ig = data_corr.geometry.get_ImageGeometry();\n",
    "\n",
    "print(\"Image geometry\", ig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f2d00e0-b1b7-4a6a-a4ce-5edac518f092",
   "metadata": {
    "colab": {
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    },
    "id": "1f2d00e0-b1b7-4a6a-a4ce-5edac518f092",
    "outputId": "563d9ecf-de73-4488-bf58-e965507c5c87"
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   "source": [
    "# Perform the reconstruction with CIL\n",
    "FDK_reconstruction = FDK(data_corr, ig).run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc9bf6d0-92e6-4f0c-919f-d42e13cdd50b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 391
    },
    "id": "cc9bf6d0-92e6-4f0c-919f-d42e13cdd50b",
    "outputId": "55026c38-f5ee-4348-d6fa-a4a2d7217bb8"
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   "source": [
    "show2D(FDK_reconstruction, slice_list=[1, FDK_reconstruction.shape[0] // 2, FDK_reconstruction.shape[0] - 2], title=[\"2nd slice\", \"Middle slice\", \"Slice before last\"], num_cols=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1d90a31-4d61-45ae-866c-b0f5428c6ee8",
   "metadata": {},
   "source": [
    "Save the reconstructed slices as a TIFF stack using 16-bit unsigned integers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9879dc45-8a81-4aa8-a61b-2de4e453d2e3",
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    "id": "9879dc45-8a81-4aa8-a61b-2de4e453d2e3"
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   "source": [
    "# Save the reconstructed CT images\n",
    "# writer = TIFFWriter(data=FDK_reconstruction, file_name=os.path.join(output_path, \"recons-\" + str(nb_images), \"slice_\"), compression=\"uint16\");\n",
    "# writer.write();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5b40171-cae4-4173-b2de-be6a05593d2b",
   "metadata": {},
   "source": [
    "Explore the reconstructed volume and illustrate the partial view artefacts due to the partial view effect on the first and last slices."
   ]
  },
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   "source": [
    "islicer(FDK_reconstruction, minmax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a4892c6-4355-4a90-9e8f-2fedc3d4866d",
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   "source": [
    "# Further work\n",
    "\n",
    "The 10 mm of iron has dramatically attenuated the beam hardening. We could try to generate a lot of artefacts.\n",
    "\n",
    "---\n",
    "## Task:\n",
    "\n",
    "- Delete the filter from the JSON file, and\n",
    "- Re-run the notebook.\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ce68bc6-9fab-4818-b09f-5159873fae2c",
   "metadata": {
    "id": "4ce68bc6-9fab-4818-b09f-5159873fae2c"
   },
   "source": [
    "# Cleaning up\n",
    "\n",
    "Once we have finished, it is good practice to clean up the OpenGL contexts and windows with the following command. Note that due to the object-oriented programming nature of the core API of gVXR, this step is automatic anyway."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f8713a20-0639-4ebf-be91-34c76d362e1c",
   "metadata": {
    "id": "f8713a20-0639-4ebf-be91-34c76d362e1c"
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   "outputs": [],
   "source": [
    "gvxr.destroy();"
   ]
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\n",
         "text/plain": "<Figure size 640x480 with 2 Axes>"
        },
        "metadata": {},
        "output_type": "display_data"
       }
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