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+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This workbook loads and converts a SCANCO .ISQ or .AIM file in the directory to several \n",
+    ".nrrd formatted images with the following units: density (g/cc), Hounsfield Units, and a\n",
+    "binary thresholded image mask \n",
+    "\n",
+    "Created by: Jason Cox, 04/07/2023\n",
+    "\n",
+    "Citations: McCormick, Matthew, et al. \"ITK: Enabling Reproducible \n",
+    "Research and Open Science.” Frontiers in Neuroinformatics, vol. 8, \n",
+    "Frontiers Media SA, 2014, doi:10.3389/fninf.2014.00013."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Install non-default packages\n",
+    "import sys\n",
+    "!{sys.executable} -m pip install itk pooch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import required packages\n",
+    "from pathlib import Path\n",
+    "import re\n",
+    "import itk\n",
+    "import pooch\n",
+    "import glob"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ISQ and AIM metadata returned by dict (cell 3) contain the same fields. Therefore, this program could be modified to loop over all files\n",
+    "# in the directory, if that is the desired implementation. The variable all_filenames is unused in the current version, but can be used in a\n",
+    "# modified version that implements a loop.\n",
+    "\n",
+    "all_AIM_filenames = glob.glob(\"*.AIM\")\n",
+    "all_ISQ_filenames = glob.glob(\"*.ISQ\")\n",
+    "all_filenames = all_AIM_filenames + all_ISQ_filenames\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This cell originally operated on the image C0005757_SCAP.AIM, though for the purposes of employing pooch, a sample file stored online at the provided URL is now used.\n",
+    "# This section will need to be modified to handle general implementation.\n",
+    "\n",
+    "# filename = 'C0005757_SCAP.AIM'\n",
+    "filename = 'C0004255.ISQ'\n",
+    "file_url = 'https://data.kitware.com/api/v1/file/591e56178d777f16d01e0d20/download'\n",
+    "file_sha256 = 'c2a3750c75826cb077d92093d43976cc0350198b55edecd681265eebabfb438b'\n",
+    "file_path = pooch.retrieve(file_url, file_sha256, fname=filename, progressbar=False)\n",
+    "image = itk.imread(file_path)\n",
+    "metadata = dict(image)\n",
+    "HU_pixels = itk.array_view_from_image(image)\n",
+    "# del image\n",
+    "# HU_pixels = np.array(HU_pixels)                     # Array arithmetic below seems to work without this line, but certain operations were not tested as they require too much memory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# As discussed below, some required constants are missing from metadata. Compare the output of this cell with the file header at:\n",
+    "# https://docs.google.com/document/d/1L2GCnHa4jmK87p0pGI9MNN10MWRMwrk2WqPbPeklP_I/edit\n",
+    "# This section does, however, collect some metadata and write it to a .txt file.\n",
+    "\n",
+    "print(metadata)\n",
+    "metadata_str = str(metadata)\n",
+    "\n",
+    "metadata_outname = Path(filename).stem + \"_metadata.txt\"\n",
+    "with open(metadata_outname, \"w\") as file:\n",
+    "    file.write(metadata_str)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The next cell provides a template file header from C0005757_SCAP.AIM, but this needs to be read from the .AIM or .ISQ.\n",
+    "\n",
+    "Then, the following cell identifies numeric constants in the header necessary for converting the image in HU (units given by itk.imread)\n",
+    "to density, or for thresholding/masking the image.\n",
+    "\n",
+    "The only constants not readable from the header which must be provided by the user are the per-milli values for low and high thresholds. In basic terms, the low/high threshold pixel values are computed as the respective low/high per-milli value multiplied by the highest pixel value in the image (a quanitity that is given by the header)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "header = '{Global Density: intercept=-1.96600006e+02, Global Calib. default unit type=2 (Density), Global Position Slice 1 [um]=47050, Global HU: mu water=0.51400, Global Reconstruction-Alg.=3, Global Parameter units=[1/cm], Global Average data value=322.51215, Global Minimum data value=-2891.00000, Global Created by=ISQ_TO_AIM (IPL), Global Patient Name=JC-NBPI-Rat-May17, Global Energy [V]=70000, Global Scanner ID=2135, Global Standard deviation=0.32443, Global Time=29-JUN-2022 12:39:11.75, Global Orig-ISQ-Dim-p=1792       1792       1781, Global Original Creation-Date=20-AUG-2018 23:18:59.99, Global Parameter name=Linear Attenuation, Global Orig-ISQ-Dim-um=17918      17918      17808, Global Scanner type=10, Global Scan Distance [um]=20480, Global Index Patient=136, Global Index Measurement=7866, Global Minimum value=-0.70581, Global Maximum data value=32767.00000, Global Intensity [uA]=114, Global Maximum value=7.99976, Global Density: slope=3.55519989e+02, Global Mu_Scaling=4096, Global Reference line [um]=47050, Global No. samples=2048, Global Default-Eval=15, Global Site=4, Global Average value=0.07874, Global Calibration Data=Unknown. Backconverted from AIM., Global Standard data deviation=1328.87610, Global No. projections per 180=1000, Global Original file=DK0:[MICROCT.DATA.00000136.00007866]B0005757.ISQ, Global Scaled by factor=4096, Global Angle-Offset [mdeg]=0, Global Integration time [us]=800000, Global Density: unit=mg HA/ccm}'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Gather numeric constants needed for unit conversion. Currently, the file metadata returned with dict does not contain all of the\n",
+    "# necessary constants. For an example of a full file header with highlighted variables of interest, see the google doc \n",
+    "# \"SCANCO micro-CT Units Conversion\" at: https://docs.google.com/document/d/1L2GCnHa4jmK87p0pGI9MNN10MWRMwrk2WqPbPeklP_I/edit\n",
+    "\n",
+    "# Thresholds must be entered by the user, they are not contained in the file header\n",
+    "mu_threshold_low = 0.250\n",
+    "mu_threshold_high = 1.0\n",
+    "\n",
+    "scanco_mu = float(re.search('Global Scaled by factor=(.*?),', header).group(1))\n",
+    "# scanco_mu = metadata['MuScaling']\n",
+    "max_val = float(re.search('Global Maximum data value=(.*?),', header).group(1))\n",
+    "# max_val = metadata['**********']\n",
+    "density_slope = float(re.search('slope=(.*?),', header).group(1))\n",
+    "# density_slope = metadata['**********']\n",
+    "density_intercept = float(re.search('intercept=(.*?),', header).group(1))\n",
+    "# density_intercept = metadata['**********']\n",
+    "water_mu = float(re.search('water=(.*?),', header).group(1))\n",
+    "# water_mu = metadata['MuWater']\n",
+    "\n",
+    "# del header                  # when the code base is modified such that metadata gives all relevant constants, this line and others that reference \"header\" can be deleted\n",
+    "# del metadata\n",
+    "\n",
+    "mu_low = mu_threshold_low * max_val\n",
+    "mu_pixel_low = mu_low/scanco_mu\n",
+    "mu_high = mu_threshold_high * max_val\n",
+    "mu_pixel_high = mu_high/scanco_mu\n",
+    "HU_pixel_low = (-1000) + mu_pixel_low * (1000/water_mu)\n",
+    "HU_pixel_high = (-1000) + mu_pixel_high * (1000/water_mu)\n",
+    "HU_values = [HU_pixel_low, HU_pixel_high]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a binarized version of the input image by applying low and high thresholds. Threshold values are accepted as int type variables, so some rounding is necessary.\n",
+    "lower_threshold = int(round(HU_values[0]))\n",
+    "upper_threshold = int(round(HU_values[1]))\n",
+    "inside_value = 1\n",
+    "outside_value = 0\n",
+    "\n",
+    "BW_image = itk.binary_threshold_image_filter(image,lower_threshold=lower_threshold,upper_threshold=upper_threshold,inside_value=inside_value,outside_value=outside_value)\n",
+    "\n",
+    "BW_outname = Path(filename).stem + \"_BWmask.nrrd\"\n",
+    "\n",
+    "itk.imwrite(BW_image, BW_outname)\n",
+    "\n",
+    "# The following can be enabled to free up memory\n",
+    "# del image\n",
+    "# del BW_image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create unit-converted outputs. MUpixels is not generally desired as an output due to representing non-standard units. However, creation of MUpixels\n",
+    "# can be skipped, as RHOpixels can be created by combining the functions needed to convert HU to MU and MU to RHO.\n",
+    "\n",
+    "# MUpixels = (HUpixels+1000)/(1000/water_mu)\n",
+    "# RHOpixels = (MUpixels*densitySlope) + densityIntercept\n",
+    "RHO_pixels = ((((HU_pixels+1000)/(1000/water_mu))*density_slope) + density_intercept)/1000  # Dividing by 1000 at the end changes units from mg/cc to g/cc, which is more commonly published\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create .nrrd file output with units of density (mg/cc or g/cc)\n",
+    "\n",
+    "RHO_out = itk.image_view_from_array(RHO_pixels)\n",
+    "for k, v in metadata.items():\n",
+    "    RHO_out[k] = v\n",
+    "RHO_outname = Path(filename).stem + \"_RHO.nrrd\"\n",
+    "itk.imwrite(RHO_out, RHO_outname)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create .nrrd file output with Hounsfield Units (equivalent to loading the file in 3D Slicer)\n",
+    "\n",
+    "HU_out = itk.image_view_from_array(HU_pixels)\n",
+    "for k, v in metadata.items():\n",
+    "    HU_out[k] = v\n",
+    "HU_outname = Path(filename).stem + \"_HU.nrrd\"\n",
+    "itk.imwrite(HU_out, HU_outname)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "kitfisto",
+   "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": "3.11.2"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}