DICOM Dataset EDA tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------------\n",
    "# DICOM Dataset EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will discuss some of the methods that could be applied in dataset analysis, when you are dealing with large multitude of 3D volumes. A lot of the same principles could be applied here as were used with individual volume EDA.\n",
    "\n",
    "In this case we will look at a collection of images and try to figure out what we are looking at. We will look at some of the techniques that might help us collect relevant meta information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pydicom\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "import numpy.ma as ma\n",
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us load all series metadata, but not pixels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "ename": "InvalidDicomError",
     "evalue": "File is missing DICOM File Meta Information header or the 'DICM' prefix is missing from the header. Use force=True to force reading.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mInvalidDicomError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-23-5e9004ccf6fa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mr\"data/data\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m series = np.array([[(os.path.join(dp, f), pydicom.dcmread(os.path.join(dp, f), stop_before_pixels = True)) for f in files]\n\u001b[0;32m----> 3\u001b[0;31m                    for dp,_,files in os.walk(path) if len(files) != 0])\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-23-5e9004ccf6fa>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mr\"data/data\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m series = np.array([[(os.path.join(dp, f), pydicom.dcmread(os.path.join(dp, f), stop_before_pixels = True)) for f in files]\n\u001b[0;32m----> 3\u001b[0;31m                    for dp,_,files in os.walk(path) if len(files) != 0])\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-23-5e9004ccf6fa>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mr\"data/data\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m series = np.array([[(os.path.join(dp, f), pydicom.dcmread(os.path.join(dp, f), stop_before_pixels = True)) for f in files]\n\u001b[0m\u001b[1;32m      3\u001b[0m                    for dp,_,files in os.walk(path) if len(files) != 0])\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pydicom/filereader.py\u001b[0m in \u001b[0;36mdcmread\u001b[0;34m(fp, defer_size, stop_before_pixels, force, specific_tags)\u001b[0m\n\u001b[1;32m    869\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    870\u001b[0m         dataset = read_partial(fp, stop_when, defer_size=defer_size,\n\u001b[0;32m--> 871\u001b[0;31m                                force=force, specific_tags=specific_tags)\n\u001b[0m\u001b[1;32m    872\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    873\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcaller_owns_file\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pydicom/filereader.py\u001b[0m in \u001b[0;36mread_partial\u001b[0;34m(fileobj, stop_when, defer_size, force, specific_tags)\u001b[0m\n\u001b[1;32m    666\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    667\u001b[0m     \u001b[0;31m# Read preamble (if present)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 668\u001b[0;31m     \u001b[0mpreamble\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mread_preamble\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforce\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    669\u001b[0m     \u001b[0;31m# Read any File Meta Information group (0002,eeee) elements (if present)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    670\u001b[0m     \u001b[0mfile_meta_dataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_read_file_meta_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pydicom/filereader.py\u001b[0m in \u001b[0;36mread_preamble\u001b[0;34m(fp, force)\u001b[0m\n\u001b[1;32m    619\u001b[0m         \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseek\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    620\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mmagic\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34mb\"DICM\"\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mforce\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 621\u001b[0;31m         raise InvalidDicomError(\"File is missing DICOM File Meta Information \"\n\u001b[0m\u001b[1;32m    622\u001b[0m                                 \u001b[0;34m\"header or the 'DICM' prefix is missing from \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    623\u001b[0m                                 \"the header. Use force=True to force reading.\")\n",
      "\u001b[0;31mInvalidDicomError\u001b[0m: File is missing DICOM File Meta Information header or the 'DICM' prefix is missing from the header. Use force=True to force reading."
     ]
    }
   ],
   "source": [
    "path = r\"data/data\"\n",
    "series = np.array([[(os.path.join(dp, f), pydicom.dcmread(os.path.join(dp, f), stop_before_pixels = True)) for f in files]\n",
    "                   for dp,_,files in os.walk(path) if len(files) != 0])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's print a few, see what we've got:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['data/JohnDoe_CT_series/IMG0122.dcm'\n",
      " Dataset.file_meta -------------------------------\n",
      "(0002, 0000) File Meta Information Group Length  UL: 256\n",
      "(0002, 0001) File Meta Information Version       OB: b'\\x00\\x01'\n",
      "(0002, 0002) Media Storage SOP Class UID         UI: CT Image Storage\n",
      "(0002, 0003) Media Storage SOP Instance UID      UI: 1.2.826.0.1.3680043.2.1125.1.58474308251689643731343805324251581\n",
      "(0002, 0010) Transfer Syntax UID                 UI: Implicit VR Little Endian\n",
      "(0002, 0012) Implementation Class UID            UI: 1.2.826.0.1.3680043.2.1143.107.104.103.115.2.8.5.111.124.113\n",
      "(0002, 0013) Implementation Version Name         SH: 'GDCM 2.8.5'\n",
      "(0002, 0016) Source Application Entity Title     AE: 'GDCM/ITK 4.13.0'\n",
      "-------------------------------------------------\n",
      "(0008, 0008) Image Type                          CS: ['ORIGINAL', 'PRIMARY', 'AXIAL']\n",
      "(0008, 0016) SOP Class UID                       UI: CT Image Storage\n",
      "(0008, 0018) SOP Instance UID                    UI: 1.2.826.0.1.3680043.2.1125.1.58474308251689643731343805324251581\n",
      "(0008, 0020) Study Date                          DA: '20190101'\n",
      "(0008, 0021) Series Date                         DA: '20190101'\n",
      "(0008, 0023) Content Date                        DA: '20190101'\n",
      "(0008, 0030) Study Time                          TM: '112200'\n",
      "(0008, 0031) Series Time                         TM: '112200'\n",
      "(0008, 0033) Content Time                        TM: '112200'\n",
      "(0008, 0050) Accession Number                    SH: '1'\n",
      "(0008, 0060) Modality                            CS: 'CT'\n",
      "(0008, 0070) Manufacturer                        LO: 'Unknown manufacturer'\n",
      "(0008, 0090) Referring Physician's Name          PN: 'Unknown'\n",
      "(0008, 1030) Study Description                   LO: 'Sample Study'\n",
      "(0008, 103e) Series Description                  LO: 'No series description'\n",
      "(0008, 1090) Manufacturer's Model Name           LO: 'Unknown model'\n",
      "(0010, 0010) Patient's Name                      PN: 'Doe^John'\n",
      "(0010, 0020) Patient ID                          LO: '123456'\n",
      "(0010, 0030) Patient's Birth Date                DA: '20060101'\n",
      "(0010, 0032) Patient's Birth Time                TM: '010100.000000'\n",
      "(0010, 0040) Patient's Sex                       CS: 'M'\n",
      "(0010, 4000) Patient Comments                    LT: 'None'\n",
      "(0018, 0050) Slice Thickness                     DS: \"2.5\"\n",
      "(0018, 5100) Patient Position                    CS: 'HFS'\n",
      "(0020, 000d) Study Instance UID                  UI: 1.2.826.0.1.3680043.2.1125.1.9839520250291993940949520616565396\n",
      "(0020, 000e) Series Instance UID                 UI: 1.2.826.0.1.3680043.2.1125.1.45859137663006505718300393375464286\n",
      "(0020, 0010) Study ID                            SH: 'SLICER10001'\n",
      "(0020, 0011) Series Number                       IS: \"1\"\n",
      "(0020, 0013) Instance Number                     IS: \"122\"\n",
      "(0020, 0032) Image Position (Patient)            DS: [-195, -171.7, -45.25]\n",
      "(0020, 0037) Image Orientation (Patient)         DS: [1, 0, 0, 0, 1, 0]\n",
      "(0020, 0052) Frame of Reference UID              UI: 1.2.826.0.1.3680043.2.1125.1.66604316561905469340955684312626989\n",
      "(0020, 1040) Position Reference Indicator        LO: 'SN'\n",
      "(0028, 0002) Samples per Pixel                   US: 1\n",
      "(0028, 0004) Photometric Interpretation          CS: 'MONOCHROME2'\n",
      "(0028, 0010) Rows                                US: 512\n",
      "(0028, 0011) Columns                             US: 512\n",
      "(0028, 0030) Pixel Spacing                       DS: [0.761718988418579, 0.761718988418579]\n",
      "(0028, 0100) Bits Allocated                      US: 16\n",
      "(0028, 0101) Bits Stored                         US: 16\n",
      "(0028, 0102) High Bit                            US: 15\n",
      "(0028, 0103) Pixel Representation                US: 1\n",
      "(0028, 1050) Window Center                       DS: \"23.0\"\n",
      "(0028, 1051) Window Width                        DS: \"6095.0\"\n",
      "(0028, 1052) Rescale Intercept                   DS: \"0.0\"\n",
      "(0028, 1053) Rescale Slope                       DS: \"1.0\"\n",
      "(0028, 1054) Rescale Type                        LO: 'US'\n",
      "(0032, 4000) Study Comments                      LT: 'None']\n"
     ]
    }
   ],
   "source": [
    "print(series[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset.file_meta -------------------------------\n",
       "(0002, 0000) File Meta Information Group Length  UL: 256\n",
       "(0002, 0001) File Meta Information Version       OB: b'\\x00\\x01'\n",
       "(0002, 0002) Media Storage SOP Class UID         UI: CT Image Storage\n",
       "(0002, 0003) Media Storage SOP Instance UID      UI: 1.2.826.0.1.3680043.2.1125.1.58474308251689643731343805324251581\n",
       "(0002, 0010) Transfer Syntax UID                 UI: Implicit VR Little Endian\n",
       "(0002, 0012) Implementation Class UID            UI: 1.2.826.0.1.3680043.2.1143.107.104.103.115.2.8.5.111.124.113\n",
       "(0002, 0013) Implementation Version Name         SH: 'GDCM 2.8.5'\n",
       "(0002, 0016) Source Application Entity Title     AE: 'GDCM/ITK 4.13.0'\n",
       "-------------------------------------------------\n",
       "(0008, 0008) Image Type                          CS: ['ORIGINAL', 'PRIMARY', 'AXIAL']\n",
       "(0008, 0016) SOP Class UID                       UI: CT Image Storage\n",
       "(0008, 0018) SOP Instance UID                    UI: 1.2.826.0.1.3680043.2.1125.1.58474308251689643731343805324251581\n",
       "(0008, 0020) Study Date                          DA: '20190101'\n",
       "(0008, 0021) Series Date                         DA: '20190101'\n",
       "(0008, 0023) Content Date                        DA: '20190101'\n",
       "(0008, 0030) Study Time                          TM: '112200'\n",
       "(0008, 0031) Series Time                         TM: '112200'\n",
       "(0008, 0033) Content Time                        TM: '112200'\n",
       "(0008, 0050) Accession Number                    SH: '1'\n",
       "(0008, 0060) Modality                            CS: 'CT'\n",
       "(0008, 0070) Manufacturer                        LO: 'Unknown manufacturer'\n",
       "(0008, 0090) Referring Physician's Name          PN: 'Unknown'\n",
       "(0008, 1030) Study Description                   LO: 'Sample Study'\n",
       "(0008, 103e) Series Description                  LO: 'No series description'\n",
       "(0008, 1090) Manufacturer's Model Name           LO: 'Unknown model'\n",
       "(0010, 0010) Patient's Name                      PN: 'Doe^John'\n",
       "(0010, 0020) Patient ID                          LO: '123456'\n",
       "(0010, 0030) Patient's Birth Date                DA: '20060101'\n",
       "(0010, 0032) Patient's Birth Time                TM: '010100.000000'\n",
       "(0010, 0040) Patient's Sex                       CS: 'M'\n",
       "(0010, 4000) Patient Comments                    LT: 'None'\n",
       "(0018, 0050) Slice Thickness                     DS: \"2.5\"\n",
       "(0018, 5100) Patient Position                    CS: 'HFS'\n",
       "(0020, 000d) Study Instance UID                  UI: 1.2.826.0.1.3680043.2.1125.1.9839520250291993940949520616565396\n",
       "(0020, 000e) Series Instance UID                 UI: 1.2.826.0.1.3680043.2.1125.1.45859137663006505718300393375464286\n",
       "(0020, 0010) Study ID                            SH: 'SLICER10001'\n",
       "(0020, 0011) Series Number                       IS: \"1\"\n",
       "(0020, 0013) Instance Number                     IS: \"122\"\n",
       "(0020, 0032) Image Position (Patient)            DS: [-195, -171.7, -45.25]\n",
       "(0020, 0037) Image Orientation (Patient)         DS: [1, 0, 0, 0, 1, 0]\n",
       "(0020, 0052) Frame of Reference UID              UI: 1.2.826.0.1.3680043.2.1125.1.66604316561905469340955684312626989\n",
       "(0020, 1040) Position Reference Indicator        LO: 'SN'\n",
       "(0028, 0002) Samples per Pixel                   US: 1\n",
       "(0028, 0004) Photometric Interpretation          CS: 'MONOCHROME2'\n",
       "(0028, 0010) Rows                                US: 512\n",
       "(0028, 0011) Columns                             US: 512\n",
       "(0028, 0030) Pixel Spacing                       DS: [0.761718988418579, 0.761718988418579]\n",
       "(0028, 0100) Bits Allocated                      US: 16\n",
       "(0028, 0101) Bits Stored                         US: 16\n",
       "(0028, 0102) High Bit                            US: 15\n",
       "(0028, 0103) Pixel Representation                US: 1\n",
       "(0028, 1050) Window Center                       DS: \"23.0\"\n",
       "(0028, 1051) Window Width                        DS: \"6095.0\"\n",
       "(0028, 1052) Rescale Intercept                   DS: \"0.0\"\n",
       "(0028, 1053) Rescale Slope                       DS: \"1.0\"\n",
       "(0028, 1054) Rescale Type                        LO: 'US'\n",
       "(0032, 4000) Study Comments                      LT: 'None'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series[0][0][1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Looks like we have MR data*  \n",
    "*Looks like we can rely on patient IDs*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many total files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "139"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "instances = [f for l in series for f in l]\n",
    "len(instances)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many patients?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patient_ids = np.unique([inst[1].PatientID for inst in instances])\n",
    "len(patient_ids)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Great - no errors hence all instances have the PatientID tag, and looks like we can rely on it*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many total series (i.e. 3D volumes)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(series)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the relationship between patients, studies and series?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# How many studies?\n",
    "\n",
    "\n",
    "studies = {}\n",
    "\n",
    "for s in series:\n",
    "    studies.setdefault(s[0][1].StudyInstanceUID, []).append(s)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(studies)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how many studies per patient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[len([st for st in studies.values() if st[0][0][1].PatientID == p]) for p in patient_ids]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Nice, all even. Let's look at directory on the file system.*  \n",
    "*Looks like 2 points in time per patient*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, '123456')]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's see how many series per study\n",
    "\n",
    "series_per_study = [(len(sr), sr[0][0][1].PatientID) for sr in studies.values()]\n",
    "series_per_study"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a quick glimpse at that outlier on the file system. \n",
    "\n",
    "- seems like it's missing some sequences"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, how many images per series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[139]\n"
     ]
    }
   ],
   "source": [
    "img_per_series = [len(s) for s in series]\n",
    "print(img_per_series)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Nice, no outliers*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at spacing and in-plane resolution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = {}\n",
    "spc = {}\n",
    "thck = {}\n",
    "\n",
    "for sr in series:\n",
    "    dcm = sr[0][1]\n",
    "    key = str(dcm.PixelSpacing)\n",
    "    spc.setdefault(key, [])\n",
    "    spc[key].append((dcm.PatientID, dcm.StudyDescription, dcm.StudyDate, dcm.SeriesDescription))\n",
    "    \n",
    "    key = str((dcm.Rows, dcm.Columns))\n",
    "    res.setdefault(key, [])\n",
    "    res[key].append((dcm.PatientID, dcm.StudyDescription, dcm.StudyDate, dcm.SeriesDescription))\n",
    "    \n",
    "    key = str(dcm.SliceThickness)\n",
    "    thck.setdefault(key, [])\n",
    "    thck[key].append((dcm.PatientID, dcm.StudyDescription, dcm.StudyDate, dcm.SeriesDescription))\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at slice thickness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['2.5'])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thck.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Great, all consistent*\n",
    "\n",
    "Let's look at pixel spacing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['[0.761718988418579, 0.761718988418579]'])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spc.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Not very consistent, let's try to see what is going on*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'[0.761718988418579, 0.761718988418579]': [('123456',\n",
       "   'Sample Study',\n",
       "   '20190101',\n",
       "   'No series description')]}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*So looks like there is a slight discrepancy among T1/T2 series. Note to self - make sure to resample if I'm using them.  \n",
    "Also, seems that some sequences have tighter pixels than others. If I'm using those, need to make sure resampling is meaningful*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at in-plane resolution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['(512, 512)'])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Not a lot of variety, but clearly some seq types are high-res, need to see what's up*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'(512, 512)': [('123456',\n",
       "   'Sample Study',\n",
       "   '20190101',\n",
       "   'No series description')]}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's visualize some of those images and see if the series align with each other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's try to see how images from same series stack w each other. We might want to use multiple\n",
    "# input channels for our problem\n",
    "\n",
    "# Remember, though, that we don't have the pixel data? Let's load it properly:\n",
    "\n",
    "seq1 = r\"PGBM-003/10-17-1995-MR RCBV SEQUENCE-57198/34911-T1prereg-46949\"\n",
    "t1_slices = [pydicom.dcmread(os.path.join(path, seq1, f)) for f in os.listdir(os.path.join(path, seq1))]\n",
    "t1_slices.sort(key = lambda inst: int(inst.ImagePositionPatient[2]))\n",
    "\n",
    "seq2 = r\"PGBM-003/10-17-1995-MR RCBV SEQUENCE-57198/36471-FLAIRreg-02052\"\n",
    "flair_slices = [pydicom.dcmread(os.path.join(path, seq2, f)) for f in os.listdir(os.path.join(path, seq2))]\n",
    "flair_slices.sort(key = lambda inst: int(inst.ImagePositionPatient[2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# t1_slices[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# flair_slices[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "t1 = np.stack([s.pixel_array for s in t1_slices])\n",
    "flair = np.stack([s.pixel_array for s in flair_slices])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[-109.536, -158.482, -59.1396],\n",
       " [-109.536, -157.977, -52.6593],\n",
       " [-109.536, -157.472, -46.179],\n",
       " [-109.536, -156.966, -39.6986],\n",
       " [-109.536, -156.461, -33.2183],\n",
       " [-109.536, -155.956, -26.7379],\n",
       " [-109.536, -155.451, -20.2576],\n",
       " [-109.536, -154.946, -13.7772],\n",
       " [-109.536, -154.441, -7.2969],\n",
       " [-109.536, -153.936, -0.816556],\n",
       " [-109.536, -153.431, 5.66379],\n",
       " [-109.536, -152.926, 12.1441],\n",
       " [-109.536, -152.421, 18.6245],\n",
       " [-109.536, -151.916, 25.1048],\n",
       " [-109.536, -151.41, 31.5852],\n",
       " [-109.536, -150.905, 38.0655],\n",
       " [-109.536, -150.4, 44.5459],\n",
       " [-109.536, -149.895, 51.0262],\n",
       " [-109.536, -149.39, 57.5065],\n",
       " [-109.536, -148.885, 63.9869],\n",
       " [-109.536, -148.38, 70.4672],\n",
       " [-109.536, -147.875, 76.9476],\n",
       " [-109.536, -147.37, 83.4279],\n",
       " [-109.536, -146.865, 89.9083]]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[ipp.ImagePositionPatient for ipp in t1_slices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[-109.536, -158.482, -59.1396],\n",
       " [-109.536, -157.977, -52.6593],\n",
       " [-109.536, -157.472, -46.179],\n",
       " [-109.536, -156.966, -39.6986],\n",
       " [-109.536, -156.461, -33.2183],\n",
       " [-109.536, -155.956, -26.7379],\n",
       " [-109.536, -155.451, -20.2576],\n",
       " [-109.536, -154.946, -13.7772],\n",
       " [-109.536, -154.441, -7.2969],\n",
       " [-109.536, -153.936, -0.816556],\n",
       " [-109.536, -153.431, 5.66379],\n",
       " [-109.536, -152.926, 12.1441],\n",
       " [-109.536, -152.421, 18.6245],\n",
       " [-109.536, -151.916, 25.1048],\n",
       " [-109.536, -151.41, 31.5852],\n",
       " [-109.536, -150.905, 38.0655],\n",
       " [-109.536, -150.4, 44.5459],\n",
       " [-109.536, -149.895, 51.0262],\n",
       " [-109.536, -149.39, 57.5065],\n",
       " [-109.536, -148.885, 63.9869],\n",
       " [-109.536, -148.38, 70.4672],\n",
       " [-109.536, -147.875, 76.9476],\n",
       " [-109.536, -147.37, 83.4279],\n",
       " [-109.536, -146.865, 89.9083]]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[ipp.ImagePositionPatient for ipp in flair_slices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([ipp.ImageOrientationPatient for ipp in t1_slices]) - np.array([ipp.ImageOrientationPatient for ipp in flair_slices])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24, 512, 512)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24, 512, 512)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flair.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f8cdb2d6518>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow((flair+1.0*t1)[9,:,:], cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f8cdb93ed30>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow((0.0*flair+t1)[9,:,:], cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point we could visualize a few more series, and probably run some code to ensure IPP and IOP alignment across all of the dataset. We could maybe load this data in Slicer to get a feel of what we are dealing with, and then start looking at the ground truth and photometrics. \n",
    "\n",
    "However, by going through these simple steps, we already learned the following about our dataset:\n",
    "\n",
    "* We are dealing with MR images\n",
    "* We have data from 4 patients\n",
    "* Each patient has 2 studies representing 2 time points\n",
    "* Each study has between 8 and 10 series, 76 series altogether\n",
    "* In total we have 1794 slices\n",
    "* We have varying pixel spacing, even within same sequence types\n",
    "* We have consistent slice thickness (5mm)\n",
    "* We have low-res slices (older ones) and higher-res slices (newer ones)\n",
    "* Our volumes seem to all be registered together and thus it is safe to just align them. I.e. they could be passed as multi-channel data into our ML algorithms\n",
    "\n",
    "That is a lot! "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}