From a7862bfb4e07cc6ca061eaffa31b44acb7adc19a Mon Sep 17 00:00:00 2001
From: nriss <nicolas.riss22@gmail.com>
Date: Thu, 14 Apr 2022 15:11:12 +0200
Subject: [PATCH 1/2] add em dicom extract

---
 .../Dicom_API-checkpoint.ipynb                | 819 ++++++++++++++++++
 notebooks/Dicom_API.ipynb                     | 290 +++++++
 notebooks/dicom_file.dcm                      | Bin 0 -> 169976 bytes
 notebooks/modele_osiris_radiomics.xlsx        | Bin 0 -> 81091 bytes
 notebooks/requirements.txt                    |   4 +
 5 files changed, 1113 insertions(+)
 create mode 100644 notebooks/.ipynb_checkpoints/Dicom_API-checkpoint.ipynb
 create mode 100644 notebooks/Dicom_API.ipynb
 create mode 100644 notebooks/dicom_file.dcm
 create mode 100644 notebooks/modele_osiris_radiomics.xlsx
 create mode 100644 notebooks/requirements.txt

diff --git a/notebooks/.ipynb_checkpoints/Dicom_API-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/Dicom_API-checkpoint.ipynb
new file mode 100644
index 0000000..bdc2c18
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/Dicom_API-checkpoint.ipynb
@@ -0,0 +1,819 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b9f92452-b2df-4fa2-b014-bd95e51ae571",
+   "metadata": {},
+   "source": [
+    "# Extracting data from DICOM files\n",
+    "### This notebook allows to extract the data from Dicom files using the dicomTags."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "09335bfd-7719-4c49-ac02-ad28ad21f374",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from pydicom import dcmread\n",
+    "import re"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2409bef7-55d0-452b-a562-8903ec428e9b",
+   "metadata": {},
+   "source": [
+    "#### DICOM_file_path: The path containing the Dicom File, tags_file_osiris: the file containing the dicomTags to be extracted\n",
+    "##### For this example the original excel file of Christophe is used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "085dd51a-7840-4329-a47a-2e5bdc7220f3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DICOM_file_path = '/Users/elenamylonas/Desktop/Elena_Arkhn/osiris/example_imagery_2/manifest-1635157999631/Brain-Tumor-Progression/PGBM-001/04-02-1992-NA-FH-HEADBrain Protocols-79896/11.000000-T1post-80644/1-01.dcm'\n",
+    "tags_file_osiris = pd.read_excel(\"Modèle_OSIRIS_IMAGE_RADIOMIC.xlsx\", sheet_name='Feuil1', header = 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adf57be1-0a6a-4e52-be03-132205e36f7a",
+   "metadata": {},
+   "source": [
+    "#### Reading the Dicom File"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f18b72b6-01b8-4b20-8b82-0c698157b02c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "DICOM_file = dcmread(DICOM_file_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b8b6f69-5c99-46a8-b933-44474b65427a",
+   "metadata": {},
+   "source": [
+    "#### Display tags_file_osiris and Dicom File"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "74f1435d-e7e2-4a4c-965c-1028c77f1c26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ObjectClass</th>\n",
+       "      <th>ObjectProperty</th>\n",
+       "      <th>Source</th>\n",
+       "      <th>DataElementConceptDef</th>\n",
+       "      <th>Example</th>\n",
+       "      <th>FormatConceptualDomain</th>\n",
+       "      <th>Mandatory/Optional</th>\n",
+       "      <th>FHIR OSIRIS</th>\n",
+       "      <th>web link</th>\n",
+       "      <th>CLB/CREATIS</th>\n",
+       "      <th>Unnamed: 10</th>\n",
+       "      <th>CURIE/LITO</th>\n",
+       "      <th>Unnamed: 12</th>\n",
+       "      <th>IGR</th>\n",
+       "      <th>Unnamed: 14</th>\n",
+       "      <th>ICO/Nantes</th>\n",
+       "      <th>Unnamed: 16</th>\n",
+       "      <th>BERGONIE/Bordeaux</th>\n",
+       "      <th>Unnamed: 18</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Standard utilisé</td>\n",
+       "      <td>Référence</td>\n",
+       "      <td>Standard utilisé</td>\n",
+       "      <td>Référence</td>\n",
+       "      <td>Standard utilisé</td>\n",
+       "      <td>Référence</td>\n",
+       "      <td>Standard utilisé</td>\n",
+       "      <td>Référence</td>\n",
+       "      <td>Standard utilisé</td>\n",
+       "      <td>Référence</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Observation</td>\n",
+       "      <td>Patient's Size</td>\n",
+       "      <td>dicomTag (0010,1020)</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>string</td>\n",
+       "      <td>Mandatory</td>\n",
+       "      <td>observation-bodyheight.valueQuantity.value</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Observation</td>\n",
+       "      <td>Patient’s Weight</td>\n",
+       "      <td>dicomTag (0010,1030)</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>string</td>\n",
+       "      <td>Mandatory</td>\n",
+       "      <td>observation-bodyweight.valueQuantity.value</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Equipment</td>\n",
+       "      <td>Manufacturer’s Model Name</td>\n",
+       "      <td>dicomTag (0008,1090)</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>string</td>\n",
+       "      <td>Optional</td>\n",
+       "      <td>Device.deviceName.name</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ObjectClass             ObjectProperty                 Source  \\\n",
+       "0          NaN                        NaN                    NaN   \n",
+       "1  Observation             Patient's Size   dicomTag (0010,1020)   \n",
+       "2  Observation           Patient’s Weight  dicomTag (0010,1030)    \n",
+       "3          NaN                        NaN                    NaN   \n",
+       "4    Equipment  Manufacturer’s Model Name   dicomTag (0008,1090)   \n",
+       "\n",
+       "  DataElementConceptDef Example FormatConceptualDomain Mandatory/Optional  \\\n",
+       "0                   NaN     NaN                    NaN                NaN   \n",
+       "1                   NaN     NaN                 string          Mandatory   \n",
+       "2                   NaN     NaN                 string          Mandatory   \n",
+       "3                   NaN     NaN                    NaN                NaN   \n",
+       "4                   NaN     NaN                 string           Optional   \n",
+       "\n",
+       "                                  FHIR OSIRIS web link       CLB/CREATIS  \\\n",
+       "0                                         NaN      NaN  Standard utilisé   \n",
+       "1  observation-bodyheight.valueQuantity.value      NaN               NaN   \n",
+       "2  observation-bodyweight.valueQuantity.value      NaN               NaN   \n",
+       "3                                         NaN      NaN               NaN   \n",
+       "4                      Device.deviceName.name      NaN               NaN   \n",
+       "\n",
+       "  Unnamed: 10        CURIE/LITO Unnamed: 12               IGR Unnamed: 14  \\\n",
+       "0   Référence  Standard utilisé   Référence  Standard utilisé   Référence   \n",
+       "1         NaN               NaN         NaN               NaN         NaN   \n",
+       "2         NaN               NaN         NaN               NaN         NaN   \n",
+       "3         NaN               NaN         NaN               NaN         NaN   \n",
+       "4         NaN               NaN         NaN               NaN         NaN   \n",
+       "\n",
+       "         ICO/Nantes Unnamed: 16 BERGONIE/Bordeaux Unnamed: 18  \n",
+       "0  Standard utilisé   Référence  Standard utilisé   Référence  \n",
+       "1               NaN         NaN               NaN         NaN  \n",
+       "2               NaN         NaN               NaN         NaN  \n",
+       "3               NaN         NaN               NaN         NaN  \n",
+       "4               NaN         NaN               NaN         NaN  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tags_file_osiris.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "8ae59cfa-f60b-43e0-a62d-d8d1c0dab5a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset.file_meta -------------------------------\n",
+      "(0002, 0000) File Meta Information Group Length  UL: 196\n",
+      "(0002, 0001) File Meta Information Version       OB: b'\\x00\\x01'\n",
+      "(0002, 0002) Media Storage SOP Class UID         UI: MR Image Storage\n",
+      "(0002, 0003) Media Storage SOP Instance UID      UI: 1.3.6.1.4.1.14519.5.2.1.4429.7055.169383040232904917519603822011\n",
+      "(0002, 0010) Transfer Syntax UID                 UI: Explicit VR Little Endian\n",
+      "(0002, 0012) Implementation Class UID            UI: 1.2.40.0.13.1.1.1\n",
+      "(0002, 0013) Implementation Version Name         SH: 'dcm4che-1.4.35'\n",
+      "-------------------------------------------------\n",
+      "(0008, 0005) Specific Character Set              CS: 'ISO_IR 100'\n",
+      "(0008, 0008) Image Type                          CS: ['ORIGINAL', 'PRIMARY', 'M', 'NORM', 'DIS2D']\n",
+      "(0008, 0012) Instance Creation Date              DA: '19920402'\n",
+      "(0008, 0013) Instance Creation Time              TM: '091823.406000'\n",
+      "(0008, 0016) SOP Class UID                       UI: MR Image Storage\n",
+      "(0008, 0018) SOP Instance UID                    UI: 1.3.6.1.4.1.14519.5.2.1.4429.7055.169383040232904917519603822011\n",
+      "(0008, 0020) Study Date                          DA: '19920402'\n",
+      "(0008, 0021) Series Date                         DA: '19920402'\n",
+      "(0008, 0022) Acquisition Date                    DA: '19920402'\n",
+      "(0008, 0023) Content Date                        DA: '19920402'\n",
+      "(0008, 0030) Study Time                          TM: '085321.125000'\n",
+      "(0008, 0031) Series Time                         TM: '091823.359000'\n",
+      "(0008, 0032) Acquisition Time                    TM: '091547.105000'\n",
+      "(0008, 0033) Content Time                        TM: '091823.406000'\n",
+      "(0008, 0050) Accession Number                    SH: '5686274134839343'\n",
+      "(0008, 0060) Modality                            CS: 'MR'\n",
+      "(0008, 0070) Manufacturer                        LO: 'SIEMENS'\n",
+      "(0008, 0090) Referring Physician's Name          PN: ''\n",
+      "(0008, 1030) Study Description                   LO: 'FH-HEAD^Brain Protocols'\n",
+      "(0008, 103e) Series Description                  LO: 'T1post'\n",
+      "(0008, 1090) Manufacturer's Model Name           LO: 'Verio'\n",
+      "(0008, 1140)  Referenced Image Sequence  3 item(s) ---- \n",
+      "   (0008, 1150) Referenced SOP Class UID            UI: MR Image Storage\n",
+      "   (0008, 1155) Referenced SOP Instance UID         UI: 1.3.6.1.4.1.14519.5.2.1.4429.7055.331846621914089755277982759291\n",
+      "   ---------\n",
+      "   (0008, 1150) Referenced SOP Class UID            UI: MR Image Storage\n",
+      "   (0008, 1155) Referenced SOP Instance UID         UI: 1.3.6.1.4.1.14519.5.2.1.4429.7055.119748453602968618792965389423\n",
+      "   ---------\n",
+      "   (0008, 1150) Referenced SOP Class UID            UI: MR Image Storage\n",
+      "   (0008, 1155) Referenced SOP Instance UID         UI: 1.3.6.1.4.1.14519.5.2.1.4429.7055.320230819474970716419167382910\n",
+      "   ---------\n",
+      "(0010, 0010) Patient's Name                      PN: 'PGBM-001'\n",
+      "(0010, 0020) Patient ID                          LO: 'PGBM-001'\n",
+      "(0010, 0030) Patient's Birth Date                DA: ''\n",
+      "(0010, 0040) Patient's Sex                       CS: 'M'\n",
+      "(0010, 1010) Patient's Age                       AS: '052Y'\n",
+      "(0010, 1030) Patient's Weight                    DS: '170.5507528152'\n",
+      "(0012, 0062) Patient Identity Removed            CS: 'YES'\n",
+      "(0012, 0063) De-identification Method            LO: 'Per DICOM PS 3.15 AnnexE. Details in 0012,0064'\n",
+      "(0012, 0064)  De-identification Method Code Sequence  8 item(s) ---- \n",
+      "   (0008, 0100) Code Value                          SH: '113100'\n",
+      "   (0008, 0102) Coding Scheme Designator            SH: 'DCM'\n",
+      "   (0008, 0104) Code Meaning                        LO: 'Basic Application Confidentiality Profile'\n",
+      "   ---------\n",
+      "   (0008, 0100) Code Value                          SH: '113101'\n",
+      "   (0008, 0102) Coding Scheme Designator            SH: 'DCM'\n",
+      "   (0008, 0104) Code Meaning                        LO: 'Clean Pixel Data Option'\n",
+      "   ---------\n",
+      "   (0008, 0100) Code Value                          SH: '113104'\n",
+      "   (0008, 0102) Coding Scheme Designator            SH: 'DCM'\n",
+      "   (0008, 0104) Code Meaning                        LO: 'Clean Structured Content Option'\n",
+      "   ---------\n",
+      "   (0008, 0100) Code Value                          SH: '113105'\n",
+      "   (0008, 0102) Coding Scheme Designator            SH: 'DCM'\n",
+      "   (0008, 0104) Code Meaning                        LO: 'Clean Descriptors Option'\n",
+      "   ---------\n",
+      "   (0008, 0100) Code Value                          SH: '113107'\n",
+      "   (0008, 0102) Coding Scheme Designator            SH: 'DCM'\n",
+      "   (0008, 0104) Code Meaning                        LO: 'Retain Longitudinal Temporal Information Modified Dates Option'\n",
+      "   ---------\n",
+      "   (0008, 0100) Code Value                          SH: '113108'\n",
+      "   (0008, 0102) Coding Scheme Designator            SH: 'DCM'\n",
+      "   (0008, 0104) Code Meaning                        LO: 'Retain Patient Characteristics Option'\n",
+      "   ---------\n",
+      "   (0008, 0100) Code Value                          SH: '113109'\n",
+      "   (0008, 0102) Coding Scheme Designator            SH: 'DCM'\n",
+      "   (0008, 0104) Code Meaning                        LO: 'Retain Device Identity Option'\n",
+      "   ---------\n",
+      "   (0008, 0100) Code Value                          SH: '113111'\n",
+      "   (0008, 0102) Coding Scheme Designator            SH: 'DCM'\n",
+      "   (0008, 0104) Code Meaning                        LO: 'Retain Safe Private Option'\n",
+      "   ---------\n",
+      "(0013, 0010) Private Creator                     LO: 'CTP'\n",
+      "(0013, 1010) Private tag data                    LO: 'Brain-Tumor-Progression'\n",
+      "(0013, 1013) Private tag data                    LO: '44297055'\n",
+      "(0018, 0010) Contrast/Bolus Agent                LO: '10cc Multihance'\n",
+      "(0018, 0015) Body Part Examined                  CS: 'BRAIN'\n",
+      "(0018, 0020) Scanning Sequence                   CS: 'SE'\n",
+      "(0018, 0021) Sequence Variant                    CS: 'SP'\n",
+      "(0018, 0022) Scan Options                        CS: ''\n",
+      "(0018, 0023) MR Acquisition Type                 CS: '2D'\n",
+      "(0018, 0024) Sequence Name                       SH: '*se2d1'\n",
+      "(0018, 0025) Angio Flag                          CS: 'N'\n",
+      "(0018, 0050) Slice Thickness                     DS: '5.0'\n",
+      "(0018, 0080) Repetition Time                     DS: '600.0'\n",
+      "(0018, 0081) Echo Time                           DS: '8.9'\n",
+      "(0018, 0083) Number of Averages                  DS: '1.0'\n",
+      "(0018, 0084) Imaging Frequency                   DS: '123.191547'\n",
+      "(0018, 0085) Imaged Nucleus                      SH: '1H'\n",
+      "(0018, 0086) Echo Number(s)                      IS: '1'\n",
+      "(0018, 0087) Magnetic Field Strength             DS: '3.0'\n",
+      "(0018, 0088) Spacing Between Slices              DS: '6.5'\n",
+      "(0018, 0089) Number of Phase Encoding Steps      IS: '260'\n",
+      "(0018, 0091) Echo Train Length                   IS: '1'\n",
+      "(0018, 0093) Percent Sampling                    DS: '100.0'\n",
+      "(0018, 0094) Percent Phase Field of View         DS: '81.25'\n",
+      "(0018, 0095) Pixel Bandwidth                     DS: '300.0'\n",
+      "(0018, 1020) Software Versions                   LO: 'syngo MR B17'\n",
+      "(0018, 1030) Protocol Name                       LO: 'B T1 AXIAL SE +'\n",
+      "(0018, 1041) Contrast/Bolus Volume               DS: '0.0'\n",
+      "(0018, 1044) Contrast/Bolus Total Dose           DS: '0.0'\n",
+      "(0018, 1048) Contrast/Bolus Ingredient           CS: ''\n",
+      "(0018, 1049) Contrast/Bolus Ingredient Concentra DS: '0.0'\n",
+      "(0018, 1251) Transmit Coil Name                  SH: 'Body'\n",
+      "(0018, 1310) Acquisition Matrix                  US: [0, 320, 260, 0]\n",
+      "(0018, 1312) In-plane Phase Encoding Direction   CS: 'ROW'\n",
+      "(0018, 1314) Flip Angle                          DS: '75.0'\n",
+      "(0018, 1315) Variable Flip Angle Flag            CS: 'N'\n",
+      "(0018, 1316) SAR                                 DS: '0.16400433528246'\n",
+      "(0018, 1318) dB/dt                               DS: '0.0'\n",
+      "(0018, 5100) Patient Position                    CS: 'HFS'\n",
+      "(0019, 0010) Private Creator                     LO: 'SIEMENS MR HEADER'\n",
+      "(0019, 100b) [SliceMeasurementDuration]          DS: '156025.0'\n",
+      "(0019, 100f) [GradientMode]                      SH: 'Fast'\n",
+      "(0019, 1011) [FlowCompensation]                  SH: 'No'\n",
+      "(0019, 1012) [TablePositionOrigin]               SL: [0, 0, -1055]\n",
+      "(0019, 1013) [ImaAbsTablePosition]               SL: [0, 0, -1056]\n",
+      "(0019, 1014) [ImaRelTablePosition]               IS: [0, 0, -1]\n",
+      "(0019, 1015) [SlicePosition_PCS]                 FD: [-83.30031811, -115.80639759, -60.45287992]\n",
+      "(0019, 1016) [TimeAfterStart]                    DS: '0.3025'\n",
+      "(0019, 1017) [SliceResolution]                   DS: '1.0'\n",
+      "(0019, 1018) [RealDwellTime]                     IS: '5200'\n",
+      "(0020, 000d) Study Instance UID                  UI: 1.3.6.1.4.1.14519.5.2.1.4429.7055.304625516276205756661744279896\n",
+      "(0020, 000e) Series Instance UID                 UI: 1.3.6.1.4.1.14519.5.2.1.4429.7055.197322826283777183783875680644\n",
+      "(0020, 0010) Study ID                            SH: ''\n",
+      "(0020, 0011) Series Number                       IS: '11'\n",
+      "(0020, 0012) Acquisition Number                  IS: '1'\n",
+      "(0020, 0013) Instance Number                     IS: '1'\n",
+      "(0020, 0032) Image Position (Patient)            DS: [-83.300318105973, -115.80639758755, -61.452879924303]\n",
+      "(0020, 0037) Image Orientation (Patient)         DS: [0.99808053354361, 0.01024095353456, 0.06107676671215, -1.68246e-011, 0.98623243279598, -0.1653650159536]\n",
+      "(0020, 0052) Frame of Reference UID              UI: 1.3.6.1.4.1.14519.5.2.1.4429.7055.234636093837714637328935526796\n",
+      "(0020, 1040) Position Reference Indicator        LO: ''\n",
+      "(0020, 1041) Slice Location                      DS: '-74.445321520837'\n",
+      "(0028, 0002) Samples per Pixel                   US: 1\n",
+      "(0028, 0004) Photometric Interpretation          CS: 'MONOCHROME2'\n",
+      "(0028, 0010) Rows                                US: 320\n",
+      "(0028, 0011) Columns                             US: 260\n",
+      "(0028, 0030) Pixel Spacing                       DS: [0.6875, 0.6875]\n",
+      "(0028, 0100) Bits Allocated                      US: 16\n",
+      "(0028, 0101) Bits Stored                         US: 12\n",
+      "(0028, 0102) High Bit                            US: 11\n",
+      "(0028, 0103) Pixel Representation                US: 0\n",
+      "(0028, 0106) Smallest Image Pixel Value          US: 0\n",
+      "(0028, 0107) Largest Image Pixel Value           US: 2476\n",
+      "(0028, 0303) Longitudinal Temporal Information M CS: 'MODIFIED'\n",
+      "(0028, 1050) Window Center                       DS: '1151.0'\n",
+      "(0028, 1051) Window Width                        DS: '1837.0'\n",
+      "(0028, 1055) Window Center & Width Explanation   LO: 'Algo1'\n",
+      "(0029, 0010) Private Creator                     LO: 'SIEMENS CSA HEADER'\n",
+      "(0029, 0011) Private Creator                     LO: 'SIEMENS MEDCOM HEADER2'\n",
+      "(0029, 1160) [Series Workflow Status]            LO: 'com'\n",
+      "(0032, 1060) Requested Procedure Description     LO: 'MR RCBV SEQUENCE FH'\n",
+      "(0040, 0244) Performed Procedure Step Start Date DA: '19920402'\n",
+      "(0040, 0245) Performed Procedure Step Start Time TM: '085321.203000'\n",
+      "(0040, 0254) Performed Procedure Step Descriptio LO: 'MR RCBV SEQUENCE FH'\n",
+      "(0051, 0010) Private Creator                     LO: 'SIEMENS MR HEADER'\n",
+      "(7fe0, 0010) Pixel Data                          OW: Array of 166400 elements\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(DICOM_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "926bd5b8-176f-49d4-9d1e-b26e48af42f3",
+   "metadata": {},
+   "source": [
+    "#### Extracting the data and building a data frame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "395c5919-c8c8-430c-9bf0-b2128437e607",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>tag</th>\n",
+       "      <th>name</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>(0010, 1030)</td>\n",
+       "      <td>Patient's Weight</td>\n",
+       "      <td>170.550753</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>(0008, 1090)</td>\n",
+       "      <td>Manufacturer's Model Name</td>\n",
+       "      <td>Verio</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>(0008, 0070)</td>\n",
+       "      <td>Manufacturer</td>\n",
+       "      <td>SIEMENS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>(0020, 000d)</td>\n",
+       "      <td>Study Instance UID</td>\n",
+       "      <td>1.3.6.1.4.1.14519.5.2.1.4429.7055.304625516276...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>(0008, 1030)</td>\n",
+       "      <td>Study Description</td>\n",
+       "      <td>FH-HEAD^Brain Protocols</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>(0020, 0011)</td>\n",
+       "      <td>Series Number</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>(0020, 000e)</td>\n",
+       "      <td>Series Instance UID</td>\n",
+       "      <td>1.3.6.1.4.1.14519.5.2.1.4429.7055.197322826283...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>(0018, 1020)</td>\n",
+       "      <td>Software Versions</td>\n",
+       "      <td>syngo MR B17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>(0018, 0010)</td>\n",
+       "      <td>Contrast/Bolus Agent</td>\n",
+       "      <td>10cc Multihance</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>(0008, 0018)</td>\n",
+       "      <td>SOP Instance UID</td>\n",
+       "      <td>1.3.6.1.4.1.14519.5.2.1.4429.7055.169383040232...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>(0018, 0050)</td>\n",
+       "      <td>Slice Thickness</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>(0028, 0030)</td>\n",
+       "      <td>Pixel Spacing</td>\n",
+       "      <td>[0.6875, 0.6875]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>(0028, 0010)</td>\n",
+       "      <td>Rows</td>\n",
+       "      <td>320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>(0028, 0011)</td>\n",
+       "      <td>Columns</td>\n",
+       "      <td>260</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>(0018, 0024)</td>\n",
+       "      <td>Sequence Name</td>\n",
+       "      <td>*se2d1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>(0018, 0087)</td>\n",
+       "      <td>Magnetic Field Strength</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>(0018, 0023)</td>\n",
+       "      <td>MR Acquisition Type</td>\n",
+       "      <td>2D</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>(0018, 0080)</td>\n",
+       "      <td>Repetition Time</td>\n",
+       "      <td>600.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>(0018, 0081)</td>\n",
+       "      <td>Echo Time</td>\n",
+       "      <td>8.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>(0018, 0084)</td>\n",
+       "      <td>Imaging Frequency</td>\n",
+       "      <td>123.191547</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>(0018, 0024)</td>\n",
+       "      <td>Sequence Name</td>\n",
+       "      <td>*se2d1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>(0008, 0060)</td>\n",
+       "      <td>Modality</td>\n",
+       "      <td>MR</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>(0018, 0010)</td>\n",
+       "      <td>Contrast/Bolus Agent</td>\n",
+       "      <td>10cc Multihance</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             tag                       name  \\\n",
+       "0   (0010, 1030)           Patient's Weight   \n",
+       "1   (0008, 1090)  Manufacturer's Model Name   \n",
+       "2   (0008, 0070)               Manufacturer   \n",
+       "3   (0020, 000d)         Study Instance UID   \n",
+       "4   (0008, 1030)          Study Description   \n",
+       "5   (0020, 0011)              Series Number   \n",
+       "6   (0020, 000e)        Series Instance UID   \n",
+       "7   (0018, 1020)          Software Versions   \n",
+       "8   (0018, 0010)       Contrast/Bolus Agent   \n",
+       "9   (0008, 0018)           SOP Instance UID   \n",
+       "10  (0018, 0050)            Slice Thickness   \n",
+       "11  (0028, 0030)              Pixel Spacing   \n",
+       "12  (0028, 0010)                       Rows   \n",
+       "13  (0028, 0011)                    Columns   \n",
+       "14  (0018, 0024)              Sequence Name   \n",
+       "15  (0018, 0087)    Magnetic Field Strength   \n",
+       "16  (0018, 0023)        MR Acquisition Type   \n",
+       "17  (0018, 0080)            Repetition Time   \n",
+       "18  (0018, 0081)                  Echo Time   \n",
+       "19  (0018, 0084)          Imaging Frequency   \n",
+       "20  (0018, 0024)              Sequence Name   \n",
+       "21  (0008, 0060)                   Modality   \n",
+       "22  (0018, 0010)       Contrast/Bolus Agent   \n",
+       "\n",
+       "                                                value  \n",
+       "0                                          170.550753  \n",
+       "1                                               Verio  \n",
+       "2                                             SIEMENS  \n",
+       "3   1.3.6.1.4.1.14519.5.2.1.4429.7055.304625516276...  \n",
+       "4                             FH-HEAD^Brain Protocols  \n",
+       "5                                                  11  \n",
+       "6   1.3.6.1.4.1.14519.5.2.1.4429.7055.197322826283...  \n",
+       "7                                        syngo MR B17  \n",
+       "8                                     10cc Multihance  \n",
+       "9   1.3.6.1.4.1.14519.5.2.1.4429.7055.169383040232...  \n",
+       "10                                                5.0  \n",
+       "11                                   [0.6875, 0.6875]  \n",
+       "12                                                320  \n",
+       "13                                                260  \n",
+       "14                                             *se2d1  \n",
+       "15                                                3.0  \n",
+       "16                                                 2D  \n",
+       "17                                              600.0  \n",
+       "18                                                8.9  \n",
+       "19                                         123.191547  \n",
+       "20                                             *se2d1  \n",
+       "21                                                 MR  \n",
+       "22                                    10cc Multihance  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list_tags = []\n",
+    "dicom_df = pd.DataFrame(columns=['tag','name','value'])\n",
+    "\n",
+    "for j in range(len(tags_file_osiris)):\n",
+    "    try:\n",
+    "        tag = tags_file_osiris['Source'][j]\n",
+    "\n",
+    "        tag_ext = re.search(r'\\((.*?)\\)',tag).group(1)\n",
+    "        tag_conv = \"0x\"+tag_ext[0:4]+tag_ext[5:]\n",
+    "        list_tags.append(tag_conv)\n",
+    "    except:\n",
+    "        pass\n",
+    "    \n",
+    "for i in list_tags:\n",
+    "    try:\n",
+    "        df_tag = str(DICOM_file[i].tag) \n",
+    "        df_name =  DICOM_file[i].name \n",
+    "        df_value = DICOM_file[i].value\n",
+    "        \n",
+    "        \n",
+    "        dicom_df = dicom_df.append({'tag':df_tag, 'name':df_name, 'value':df_value}, ignore_index=True)\n",
+    "        \n",
+    "    except:\n",
+    "        pass\n",
+    "    \n",
+    "    \n",
+    "dicom_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3a305ee-0a14-424e-8683-92c1ebc1c1ff",
+   "metadata": {},
+   "source": [
+    "#### Exporting to CSV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "180c8dff-aae7-490a-83fb-209e670f5958",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dicom_df.to_csv('dicom_data-osiris.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "183fa73e-0adb-410e-8560-33889d8a0089",
+   "metadata": {},
+   "source": [
+    "#### Just for fun!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "225ea97e-e261-46f2-ae57-67018c62c828",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "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": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.imshow(DICOM_file.pixel_array, cmap=plt.cm.gray)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5a925648-a379-4e35-958a-c5d7a0946ac8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/Dicom_API.ipynb b/notebooks/Dicom_API.ipynb
new file mode 100644
index 0000000..5885b72
--- /dev/null
+++ b/notebooks/Dicom_API.ipynb
@@ -0,0 +1,290 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b9f92452-b2df-4fa2-b014-bd95e51ae571",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Extracting data from DICOM files\n",
+    "### This notebook extract data from Dicom files using the dicomTags."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c857a38-eabe-497a-904e-39ad862dad9c",
+   "metadata": {},
+   "source": [
+    "#### Installing required packages:\n",
+    "\n",
+    "1. Create a new environment (https://docs.python.org/3/library/venv.html)\n",
+    "\n",
+    "```python\n",
+    "python3 -m venv /path/to/new/virtual/environment\n",
+    "```\n",
+    "2. Activate the new environment\n",
+    "\n",
+    "```python\n",
+    "source env/bin/activate\n",
+    "```\n",
+    "\n",
+    "3. Install required packages\n",
+    "\n",
+    "```python\n",
+    "pip install -r requirements.txt\n",
+    "```\n",
+    "\n",
+    "4. Run the notebook.  :)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "09335bfd-7719-4c49-ac02-ad28ad21f374",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from pydicom import dcmread\n",
+    "import re\n",
+    "import glob"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2409bef7-55d0-452b-a562-8903ec428e9b",
+   "metadata": {},
+   "source": [
+    "DICOM_file: The path containing the Dicom File.\n",
+    "tags_file_osiris: the file containing the dicomTags to be extracted.\n",
+    "For this example the original excel file of Christophe is used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "085dd51a-7840-4329-a47a-2e5bdc7220f3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DICOM_file = dcmread(\"dicom_file.dcm\")\n",
+    "DICOM_file = dcmread(\"files/1-01.dcm\")\n",
+    "tags_file_osiris = pd.read_excel(\n",
+    "    \"modele_osiris_radiomics.xlsx\", sheet_name=\"Feuil1\", header=0\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b8b6f69-5c99-46a8-b933-44474b65427a",
+   "metadata": {},
+   "source": [
+    "#### Display tags_file_osiris and Dicom File"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "74f1435d-e7e2-4a4c-965c-1028c77f1c26",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tags_file_osiris"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8ae59cfa-f60b-43e0-a62d-d8d1c0dab5a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(DICOM_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "926bd5b8-176f-49d4-9d1e-b26e48af42f3",
+   "metadata": {},
+   "source": [
+    "### Extracting the data and building a data frame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "395c5919-c8c8-430c-9bf0-b2128437e607",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list_tags = []\n",
+    "dicom_df = pd.DataFrame(columns=[\"tag\", \"name\", \"value\"])\n",
+    "\n",
+    "# extraction of dicomTags from the excel file\n",
+    "for j in range(len(tags_file_osiris)):\n",
+    "    try:\n",
+    "        tag_ext = re.search(r\"\\((.*?)\\)\", tags_file_osiris[\"Source\"][j]).group(1)\n",
+    "        tag_conv = \"0x\" + tag_ext[0:4] + tag_ext[5:]\n",
+    "        list_tags.append(tag_conv)\n",
+    "    except:\n",
+    "        pass\n",
+    "\n",
+    "# searching for the tags in the dicom file and building the dataframe\n",
+    "for i in list_tags:\n",
+    "    try:\n",
+    "        df_tag = str(DICOM_file[i].tag)\n",
+    "        df_name = DICOM_file[i].name\n",
+    "        df_value = DICOM_file[i].value\n",
+    "\n",
+    "        dicom_df = dicom_df.append(\n",
+    "            {\"tag\": df_tag, \"name\": df_name, \"value\": df_value}, ignore_index=True\n",
+    "        )\n",
+    "\n",
+    "    except:\n",
+    "        pass\n",
+    "\n",
+    "\n",
+    "dicom_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3a305ee-0a14-424e-8683-92c1ebc1c1ff",
+   "metadata": {},
+   "source": [
+    "#### Exporting to CSV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "180c8dff-aae7-490a-83fb-209e670f5958",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dicom_df.to_csv('dicom_data-osiris.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5d14377-3613-4e57-b5c8-8627421bc5b8",
+   "metadata": {},
+   "source": [
+    "### Working with file lists and building a transposed dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "89e65145-147b-40cc-a26d-6ed6d5d3bf40",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "dcm_files = (glob.glob(\"files/*.dcm\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d8752cb4-9ace-4798-ade6-a23034b2fa09",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list_tags = []\n",
+    "\n",
+    "\n",
+    "# extraction of dicomTags from the excel file\n",
+    "for j in range(len(tags_file_osiris)):\n",
+    "    try:\n",
+    "        tag_ext = re.search(r\"\\((.*?)\\)\", tags_file_osiris[\"Source\"][j]).group(1)\n",
+    "        tag_conv = \"0x\" + tag_ext[0:4] + tag_ext[5:]\n",
+    "        list_tags.append(tag_conv)\n",
+    "    except:\n",
+    "        pass\n",
+    "\n",
+    "dicom_df = pd.DataFrame(columns=[\"file\",\"tag\", \"value\"])\n",
+    "dicom_dict = {}\n",
+    "\n",
+    "# searching for the tags in the dicom file and building the dataframe\n",
+    "for i in dcm_files:\n",
+    "    DICOM_file = dcmread(i)\n",
+    "    dicom_dict[i] = {}\n",
+    "    for j in list_tags:\n",
+    "        try:\n",
+    "            tag = str(DICOM_file[j].tag)\n",
+    "            value = DICOM_file[j].value\n",
+    "            dicom_dict[i][tag] = value\n",
+    "        except:\n",
+    "            pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "71b5225c-dee9-46f5-920c-2d36ef0828d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_dicom = pd.DataFrame.from_dict(dicom_dict, orient='index')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "03d3864d-8f63-49d1-96e0-b4b741111a25",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_dicom.head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "307f6f7f-1b2e-4bd4-a7c4-b30ff5da7a7c",
+   "metadata": {},
+   "source": [
+    "#### Just for fun!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "225ea97e-e261-46f2-ae57-67018c62c828",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.imshow(DICOM_file.pixel_array, cmap=plt.cm.gray)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "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": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/dicom_file.dcm b/notebooks/dicom_file.dcm
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literal 0
HcmV?d00001

diff --git a/notebooks/requirements.txt b/notebooks/requirements.txt
new file mode 100644
index 0000000..18b2492
--- /dev/null
+++ b/notebooks/requirements.txt
@@ -0,0 +1,4 @@
+pandas==1.2.3
+numpy==1.20.2
+matplotlib==3.4.1
+pydicom==2.3.0.dev0

From b6f5caefd7849a3dff80ce01362e3e94f590f0be Mon Sep 17 00:00:00 2001
From: nriss <nicolas.riss22@gmail.com>
Date: Thu, 14 Apr 2022 15:13:30 +0200
Subject: [PATCH 2/2] remove .ipynb file

---
 .../Dicom_API-checkpoint.ipynb                | 819 ------------------
 1 file changed, 819 deletions(-)
 delete mode 100644 notebooks/.ipynb_checkpoints/Dicom_API-checkpoint.ipynb

diff --git a/notebooks/.ipynb_checkpoints/Dicom_API-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/Dicom_API-checkpoint.ipynb
deleted file mode 100644
index bdc2c18..0000000
--- a/notebooks/.ipynb_checkpoints/Dicom_API-checkpoint.ipynb
+++ /dev/null
@@ -1,819 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "b9f92452-b2df-4fa2-b014-bd95e51ae571",
-   "metadata": {},
-   "source": [
-    "# Extracting data from DICOM files\n",
-    "### This notebook allows to extract the data from Dicom files using the dicomTags."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "09335bfd-7719-4c49-ac02-ad28ad21f374",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from pydicom import dcmread\n",
-    "import re"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2409bef7-55d0-452b-a562-8903ec428e9b",
-   "metadata": {},
-   "source": [
-    "#### DICOM_file_path: The path containing the Dicom File, tags_file_osiris: the file containing the dicomTags to be extracted\n",
-    "##### For this example the original excel file of Christophe is used."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "085dd51a-7840-4329-a47a-2e5bdc7220f3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "DICOM_file_path = '/Users/elenamylonas/Desktop/Elena_Arkhn/osiris/example_imagery_2/manifest-1635157999631/Brain-Tumor-Progression/PGBM-001/04-02-1992-NA-FH-HEADBrain Protocols-79896/11.000000-T1post-80644/1-01.dcm'\n",
-    "tags_file_osiris = pd.read_excel(\"Modèle_OSIRIS_IMAGE_RADIOMIC.xlsx\", sheet_name='Feuil1', header = 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "adf57be1-0a6a-4e52-be03-132205e36f7a",
-   "metadata": {},
-   "source": [
-    "#### Reading the Dicom File"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "f18b72b6-01b8-4b20-8b82-0c698157b02c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "DICOM_file = dcmread(DICOM_file_path)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3b8b6f69-5c99-46a8-b933-44474b65427a",
-   "metadata": {},
-   "source": [
-    "#### Display tags_file_osiris and Dicom File"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "74f1435d-e7e2-4a4c-965c-1028c77f1c26",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ObjectClass</th>\n",
-       "      <th>ObjectProperty</th>\n",
-       "      <th>Source</th>\n",
-       "      <th>DataElementConceptDef</th>\n",
-       "      <th>Example</th>\n",
-       "      <th>FormatConceptualDomain</th>\n",
-       "      <th>Mandatory/Optional</th>\n",
-       "      <th>FHIR OSIRIS</th>\n",
-       "      <th>web link</th>\n",
-       "      <th>CLB/CREATIS</th>\n",
-       "      <th>Unnamed: 10</th>\n",
-       "      <th>CURIE/LITO</th>\n",
-       "      <th>Unnamed: 12</th>\n",
-       "      <th>IGR</th>\n",
-       "      <th>Unnamed: 14</th>\n",
-       "      <th>ICO/Nantes</th>\n",
-       "      <th>Unnamed: 16</th>\n",
-       "      <th>BERGONIE/Bordeaux</th>\n",
-       "      <th>Unnamed: 18</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
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-       "      <td>NaN</td>\n",
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-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Standard utilisé</td>\n",
-       "      <td>Référence</td>\n",
-       "      <td>Standard utilisé</td>\n",
-       "      <td>Référence</td>\n",
-       "      <td>Standard utilisé</td>\n",
-       "      <td>Référence</td>\n",
-       "      <td>Standard utilisé</td>\n",
-       "      <td>Référence</td>\n",
-       "      <td>Standard utilisé</td>\n",
-       "      <td>Référence</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Observation</td>\n",
-       "      <td>Patient's Size</td>\n",
-       "      <td>dicomTag (0010,1020)</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>string</td>\n",
-       "      <td>Mandatory</td>\n",
-       "      <td>observation-bodyheight.valueQuantity.value</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Observation</td>\n",
-       "      <td>Patient’s Weight</td>\n",
-       "      <td>dicomTag (0010,1030)</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>string</td>\n",
-       "      <td>Mandatory</td>\n",
-       "      <td>observation-bodyweight.valueQuantity.value</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Equipment</td>\n",
-       "      <td>Manufacturer’s Model Name</td>\n",
-       "      <td>dicomTag (0008,1090)</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>string</td>\n",
-       "      <td>Optional</td>\n",
-       "      <td>Device.deviceName.name</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   ObjectClass             ObjectProperty                 Source  \\\n",
-       "0          NaN                        NaN                    NaN   \n",
-       "1  Observation             Patient's Size   dicomTag (0010,1020)   \n",
-       "2  Observation           Patient’s Weight  dicomTag (0010,1030)    \n",
-       "3          NaN                        NaN                    NaN   \n",
-       "4    Equipment  Manufacturer’s Model Name   dicomTag (0008,1090)   \n",
-       "\n",
-       "  DataElementConceptDef Example FormatConceptualDomain Mandatory/Optional  \\\n",
-       "0                   NaN     NaN                    NaN                NaN   \n",
-       "1                   NaN     NaN                 string          Mandatory   \n",
-       "2                   NaN     NaN                 string          Mandatory   \n",
-       "3                   NaN     NaN                    NaN                NaN   \n",
-       "4                   NaN     NaN                 string           Optional   \n",
-       "\n",
-       "                                  FHIR OSIRIS web link       CLB/CREATIS  \\\n",
-       "0                                         NaN      NaN  Standard utilisé   \n",
-       "1  observation-bodyheight.valueQuantity.value      NaN               NaN   \n",
-       "2  observation-bodyweight.valueQuantity.value      NaN               NaN   \n",
-       "3                                         NaN      NaN               NaN   \n",
-       "4                      Device.deviceName.name      NaN               NaN   \n",
-       "\n",
-       "  Unnamed: 10        CURIE/LITO Unnamed: 12               IGR Unnamed: 14  \\\n",
-       "0   Référence  Standard utilisé   Référence  Standard utilisé   Référence   \n",
-       "1         NaN               NaN         NaN               NaN         NaN   \n",
-       "2         NaN               NaN         NaN               NaN         NaN   \n",
-       "3         NaN               NaN         NaN               NaN         NaN   \n",
-       "4         NaN               NaN         NaN               NaN         NaN   \n",
-       "\n",
-       "         ICO/Nantes Unnamed: 16 BERGONIE/Bordeaux Unnamed: 18  \n",
-       "0  Standard utilisé   Référence  Standard utilisé   Référence  \n",
-       "1               NaN         NaN               NaN         NaN  \n",
-       "2               NaN         NaN               NaN         NaN  \n",
-       "3               NaN         NaN               NaN         NaN  \n",
-       "4               NaN         NaN               NaN         NaN  "
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tags_file_osiris.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "8ae59cfa-f60b-43e0-a62d-d8d1c0dab5a9",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset.file_meta -------------------------------\n",
-      "(0002, 0000) File Meta Information Group Length  UL: 196\n",
-      "(0002, 0001) File Meta Information Version       OB: b'\\x00\\x01'\n",
-      "(0002, 0002) Media Storage SOP Class UID         UI: MR Image Storage\n",
-      "(0002, 0003) Media Storage SOP Instance UID      UI: 1.3.6.1.4.1.14519.5.2.1.4429.7055.169383040232904917519603822011\n",
-      "(0002, 0010) Transfer Syntax UID                 UI: Explicit VR Little Endian\n",
-      "(0002, 0012) Implementation Class UID            UI: 1.2.40.0.13.1.1.1\n",
-      "(0002, 0013) Implementation Version Name         SH: 'dcm4che-1.4.35'\n",
-      "-------------------------------------------------\n",
-      "(0008, 0005) Specific Character Set              CS: 'ISO_IR 100'\n",
-      "(0008, 0008) Image Type                          CS: ['ORIGINAL', 'PRIMARY', 'M', 'NORM', 'DIS2D']\n",
-      "(0008, 0012) Instance Creation Date              DA: '19920402'\n",
-      "(0008, 0013) Instance Creation Time              TM: '091823.406000'\n",
-      "(0008, 0016) SOP Class UID                       UI: MR Image Storage\n",
-      "(0008, 0018) SOP Instance UID                    UI: 1.3.6.1.4.1.14519.5.2.1.4429.7055.169383040232904917519603822011\n",
-      "(0008, 0020) Study Date                          DA: '19920402'\n",
-      "(0008, 0021) Series Date                         DA: '19920402'\n",
-      "(0008, 0022) Acquisition Date                    DA: '19920402'\n",
-      "(0008, 0023) Content Date                        DA: '19920402'\n",
-      "(0008, 0030) Study Time                          TM: '085321.125000'\n",
-      "(0008, 0031) Series Time                         TM: '091823.359000'\n",
-      "(0008, 0032) Acquisition Time                    TM: '091547.105000'\n",
-      "(0008, 0033) Content Time                        TM: '091823.406000'\n",
-      "(0008, 0050) Accession Number                    SH: '5686274134839343'\n",
-      "(0008, 0060) Modality                            CS: 'MR'\n",
-      "(0008, 0070) Manufacturer                        LO: 'SIEMENS'\n",
-      "(0008, 0090) Referring Physician's Name          PN: ''\n",
-      "(0008, 1030) Study Description                   LO: 'FH-HEAD^Brain Protocols'\n",
-      "(0008, 103e) Series Description                  LO: 'T1post'\n",
-      "(0008, 1090) Manufacturer's Model Name           LO: 'Verio'\n",
-      "(0008, 1140)  Referenced Image Sequence  3 item(s) ---- \n",
-      "   (0008, 1150) Referenced SOP Class UID            UI: MR Image Storage\n",
-      "   (0008, 1155) Referenced SOP Instance UID         UI: 1.3.6.1.4.1.14519.5.2.1.4429.7055.331846621914089755277982759291\n",
-      "   ---------\n",
-      "   (0008, 1150) Referenced SOP Class UID            UI: MR Image Storage\n",
-      "   (0008, 1155) Referenced SOP Instance UID         UI: 1.3.6.1.4.1.14519.5.2.1.4429.7055.119748453602968618792965389423\n",
-      "   ---------\n",
-      "   (0008, 1150) Referenced SOP Class UID            UI: MR Image Storage\n",
-      "   (0008, 1155) Referenced SOP Instance UID         UI: 1.3.6.1.4.1.14519.5.2.1.4429.7055.320230819474970716419167382910\n",
-      "   ---------\n",
-      "(0010, 0010) Patient's Name                      PN: 'PGBM-001'\n",
-      "(0010, 0020) Patient ID                          LO: 'PGBM-001'\n",
-      "(0010, 0030) Patient's Birth Date                DA: ''\n",
-      "(0010, 0040) Patient's Sex                       CS: 'M'\n",
-      "(0010, 1010) Patient's Age                       AS: '052Y'\n",
-      "(0010, 1030) Patient's Weight                    DS: '170.5507528152'\n",
-      "(0012, 0062) Patient Identity Removed            CS: 'YES'\n",
-      "(0012, 0063) De-identification Method            LO: 'Per DICOM PS 3.15 AnnexE. Details in 0012,0064'\n",
-      "(0012, 0064)  De-identification Method Code Sequence  8 item(s) ---- \n",
-      "   (0008, 0100) Code Value                          SH: '113100'\n",
-      "   (0008, 0102) Coding Scheme Designator            SH: 'DCM'\n",
-      "   (0008, 0104) Code Meaning                        LO: 'Basic Application Confidentiality Profile'\n",
-      "   ---------\n",
-      "   (0008, 0100) Code Value                          SH: '113101'\n",
-      "   (0008, 0102) Coding Scheme Designator            SH: 'DCM'\n",
-      "   (0008, 0104) Code Meaning                        LO: 'Clean Pixel Data Option'\n",
-      "   ---------\n",
-      "   (0008, 0100) Code Value                          SH: '113104'\n",
-      "   (0008, 0102) Coding Scheme Designator            SH: 'DCM'\n",
-      "   (0008, 0104) Code Meaning                        LO: 'Clean Structured Content Option'\n",
-      "   ---------\n",
-      "   (0008, 0100) Code Value                          SH: '113105'\n",
-      "   (0008, 0102) Coding Scheme Designator            SH: 'DCM'\n",
-      "   (0008, 0104) Code Meaning                        LO: 'Clean Descriptors Option'\n",
-      "   ---------\n",
-      "   (0008, 0100) Code Value                          SH: '113107'\n",
-      "   (0008, 0102) Coding Scheme Designator            SH: 'DCM'\n",
-      "   (0008, 0104) Code Meaning                        LO: 'Retain Longitudinal Temporal Information Modified Dates Option'\n",
-      "   ---------\n",
-      "   (0008, 0100) Code Value                          SH: '113108'\n",
-      "   (0008, 0102) Coding Scheme Designator            SH: 'DCM'\n",
-      "   (0008, 0104) Code Meaning                        LO: 'Retain Patient Characteristics Option'\n",
-      "   ---------\n",
-      "   (0008, 0100) Code Value                          SH: '113109'\n",
-      "   (0008, 0102) Coding Scheme Designator            SH: 'DCM'\n",
-      "   (0008, 0104) Code Meaning                        LO: 'Retain Device Identity Option'\n",
-      "   ---------\n",
-      "   (0008, 0100) Code Value                          SH: '113111'\n",
-      "   (0008, 0102) Coding Scheme Designator            SH: 'DCM'\n",
-      "   (0008, 0104) Code Meaning                        LO: 'Retain Safe Private Option'\n",
-      "   ---------\n",
-      "(0013, 0010) Private Creator                     LO: 'CTP'\n",
-      "(0013, 1010) Private tag data                    LO: 'Brain-Tumor-Progression'\n",
-      "(0013, 1013) Private tag data                    LO: '44297055'\n",
-      "(0018, 0010) Contrast/Bolus Agent                LO: '10cc Multihance'\n",
-      "(0018, 0015) Body Part Examined                  CS: 'BRAIN'\n",
-      "(0018, 0020) Scanning Sequence                   CS: 'SE'\n",
-      "(0018, 0021) Sequence Variant                    CS: 'SP'\n",
-      "(0018, 0022) Scan Options                        CS: ''\n",
-      "(0018, 0023) MR Acquisition Type                 CS: '2D'\n",
-      "(0018, 0024) Sequence Name                       SH: '*se2d1'\n",
-      "(0018, 0025) Angio Flag                          CS: 'N'\n",
-      "(0018, 0050) Slice Thickness                     DS: '5.0'\n",
-      "(0018, 0080) Repetition Time                     DS: '600.0'\n",
-      "(0018, 0081) Echo Time                           DS: '8.9'\n",
-      "(0018, 0083) Number of Averages                  DS: '1.0'\n",
-      "(0018, 0084) Imaging Frequency                   DS: '123.191547'\n",
-      "(0018, 0085) Imaged Nucleus                      SH: '1H'\n",
-      "(0018, 0086) Echo Number(s)                      IS: '1'\n",
-      "(0018, 0087) Magnetic Field Strength             DS: '3.0'\n",
-      "(0018, 0088) Spacing Between Slices              DS: '6.5'\n",
-      "(0018, 0089) Number of Phase Encoding Steps      IS: '260'\n",
-      "(0018, 0091) Echo Train Length                   IS: '1'\n",
-      "(0018, 0093) Percent Sampling                    DS: '100.0'\n",
-      "(0018, 0094) Percent Phase Field of View         DS: '81.25'\n",
-      "(0018, 0095) Pixel Bandwidth                     DS: '300.0'\n",
-      "(0018, 1020) Software Versions                   LO: 'syngo MR B17'\n",
-      "(0018, 1030) Protocol Name                       LO: 'B T1 AXIAL SE +'\n",
-      "(0018, 1041) Contrast/Bolus Volume               DS: '0.0'\n",
-      "(0018, 1044) Contrast/Bolus Total Dose           DS: '0.0'\n",
-      "(0018, 1048) Contrast/Bolus Ingredient           CS: ''\n",
-      "(0018, 1049) Contrast/Bolus Ingredient Concentra DS: '0.0'\n",
-      "(0018, 1251) Transmit Coil Name                  SH: 'Body'\n",
-      "(0018, 1310) Acquisition Matrix                  US: [0, 320, 260, 0]\n",
-      "(0018, 1312) In-plane Phase Encoding Direction   CS: 'ROW'\n",
-      "(0018, 1314) Flip Angle                          DS: '75.0'\n",
-      "(0018, 1315) Variable Flip Angle Flag            CS: 'N'\n",
-      "(0018, 1316) SAR                                 DS: '0.16400433528246'\n",
-      "(0018, 1318) dB/dt                               DS: '0.0'\n",
-      "(0018, 5100) Patient Position                    CS: 'HFS'\n",
-      "(0019, 0010) Private Creator                     LO: 'SIEMENS MR HEADER'\n",
-      "(0019, 100b) [SliceMeasurementDuration]          DS: '156025.0'\n",
-      "(0019, 100f) [GradientMode]                      SH: 'Fast'\n",
-      "(0019, 1011) [FlowCompensation]                  SH: 'No'\n",
-      "(0019, 1012) [TablePositionOrigin]               SL: [0, 0, -1055]\n",
-      "(0019, 1013) [ImaAbsTablePosition]               SL: [0, 0, -1056]\n",
-      "(0019, 1014) [ImaRelTablePosition]               IS: [0, 0, -1]\n",
-      "(0019, 1015) [SlicePosition_PCS]                 FD: [-83.30031811, -115.80639759, -60.45287992]\n",
-      "(0019, 1016) [TimeAfterStart]                    DS: '0.3025'\n",
-      "(0019, 1017) [SliceResolution]                   DS: '1.0'\n",
-      "(0019, 1018) [RealDwellTime]                     IS: '5200'\n",
-      "(0020, 000d) Study Instance UID                  UI: 1.3.6.1.4.1.14519.5.2.1.4429.7055.304625516276205756661744279896\n",
-      "(0020, 000e) Series Instance UID                 UI: 1.3.6.1.4.1.14519.5.2.1.4429.7055.197322826283777183783875680644\n",
-      "(0020, 0010) Study ID                            SH: ''\n",
-      "(0020, 0011) Series Number                       IS: '11'\n",
-      "(0020, 0012) Acquisition Number                  IS: '1'\n",
-      "(0020, 0013) Instance Number                     IS: '1'\n",
-      "(0020, 0032) Image Position (Patient)            DS: [-83.300318105973, -115.80639758755, -61.452879924303]\n",
-      "(0020, 0037) Image Orientation (Patient)         DS: [0.99808053354361, 0.01024095353456, 0.06107676671215, -1.68246e-011, 0.98623243279598, -0.1653650159536]\n",
-      "(0020, 0052) Frame of Reference UID              UI: 1.3.6.1.4.1.14519.5.2.1.4429.7055.234636093837714637328935526796\n",
-      "(0020, 1040) Position Reference Indicator        LO: ''\n",
-      "(0020, 1041) Slice Location                      DS: '-74.445321520837'\n",
-      "(0028, 0002) Samples per Pixel                   US: 1\n",
-      "(0028, 0004) Photometric Interpretation          CS: 'MONOCHROME2'\n",
-      "(0028, 0010) Rows                                US: 320\n",
-      "(0028, 0011) Columns                             US: 260\n",
-      "(0028, 0030) Pixel Spacing                       DS: [0.6875, 0.6875]\n",
-      "(0028, 0100) Bits Allocated                      US: 16\n",
-      "(0028, 0101) Bits Stored                         US: 12\n",
-      "(0028, 0102) High Bit                            US: 11\n",
-      "(0028, 0103) Pixel Representation                US: 0\n",
-      "(0028, 0106) Smallest Image Pixel Value          US: 0\n",
-      "(0028, 0107) Largest Image Pixel Value           US: 2476\n",
-      "(0028, 0303) Longitudinal Temporal Information M CS: 'MODIFIED'\n",
-      "(0028, 1050) Window Center                       DS: '1151.0'\n",
-      "(0028, 1051) Window Width                        DS: '1837.0'\n",
-      "(0028, 1055) Window Center & Width Explanation   LO: 'Algo1'\n",
-      "(0029, 0010) Private Creator                     LO: 'SIEMENS CSA HEADER'\n",
-      "(0029, 0011) Private Creator                     LO: 'SIEMENS MEDCOM HEADER2'\n",
-      "(0029, 1160) [Series Workflow Status]            LO: 'com'\n",
-      "(0032, 1060) Requested Procedure Description     LO: 'MR RCBV SEQUENCE FH'\n",
-      "(0040, 0244) Performed Procedure Step Start Date DA: '19920402'\n",
-      "(0040, 0245) Performed Procedure Step Start Time TM: '085321.203000'\n",
-      "(0040, 0254) Performed Procedure Step Descriptio LO: 'MR RCBV SEQUENCE FH'\n",
-      "(0051, 0010) Private Creator                     LO: 'SIEMENS MR HEADER'\n",
-      "(7fe0, 0010) Pixel Data                          OW: Array of 166400 elements\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(DICOM_file)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "926bd5b8-176f-49d4-9d1e-b26e48af42f3",
-   "metadata": {},
-   "source": [
-    "#### Extracting the data and building a data frame"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "395c5919-c8c8-430c-9bf0-b2128437e607",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>tag</th>\n",
-       "      <th>name</th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>(0010, 1030)</td>\n",
-       "      <td>Patient's Weight</td>\n",
-       "      <td>170.550753</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>(0008, 1090)</td>\n",
-       "      <td>Manufacturer's Model Name</td>\n",
-       "      <td>Verio</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>(0008, 0070)</td>\n",
-       "      <td>Manufacturer</td>\n",
-       "      <td>SIEMENS</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>(0020, 000d)</td>\n",
-       "      <td>Study Instance UID</td>\n",
-       "      <td>1.3.6.1.4.1.14519.5.2.1.4429.7055.304625516276...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>(0008, 1030)</td>\n",
-       "      <td>Study Description</td>\n",
-       "      <td>FH-HEAD^Brain Protocols</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>(0020, 0011)</td>\n",
-       "      <td>Series Number</td>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>(0020, 000e)</td>\n",
-       "      <td>Series Instance UID</td>\n",
-       "      <td>1.3.6.1.4.1.14519.5.2.1.4429.7055.197322826283...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>(0018, 1020)</td>\n",
-       "      <td>Software Versions</td>\n",
-       "      <td>syngo MR B17</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>(0018, 0010)</td>\n",
-       "      <td>Contrast/Bolus Agent</td>\n",
-       "      <td>10cc Multihance</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>(0008, 0018)</td>\n",
-       "      <td>SOP Instance UID</td>\n",
-       "      <td>1.3.6.1.4.1.14519.5.2.1.4429.7055.169383040232...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>(0018, 0050)</td>\n",
-       "      <td>Slice Thickness</td>\n",
-       "      <td>5.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>(0028, 0030)</td>\n",
-       "      <td>Pixel Spacing</td>\n",
-       "      <td>[0.6875, 0.6875]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>(0028, 0010)</td>\n",
-       "      <td>Rows</td>\n",
-       "      <td>320</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>(0028, 0011)</td>\n",
-       "      <td>Columns</td>\n",
-       "      <td>260</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>(0018, 0024)</td>\n",
-       "      <td>Sequence Name</td>\n",
-       "      <td>*se2d1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>(0018, 0087)</td>\n",
-       "      <td>Magnetic Field Strength</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>(0018, 0023)</td>\n",
-       "      <td>MR Acquisition Type</td>\n",
-       "      <td>2D</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>(0018, 0080)</td>\n",
-       "      <td>Repetition Time</td>\n",
-       "      <td>600.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>(0018, 0081)</td>\n",
-       "      <td>Echo Time</td>\n",
-       "      <td>8.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>(0018, 0084)</td>\n",
-       "      <td>Imaging Frequency</td>\n",
-       "      <td>123.191547</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>(0018, 0024)</td>\n",
-       "      <td>Sequence Name</td>\n",
-       "      <td>*se2d1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>(0008, 0060)</td>\n",
-       "      <td>Modality</td>\n",
-       "      <td>MR</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>(0018, 0010)</td>\n",
-       "      <td>Contrast/Bolus Agent</td>\n",
-       "      <td>10cc Multihance</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             tag                       name  \\\n",
-       "0   (0010, 1030)           Patient's Weight   \n",
-       "1   (0008, 1090)  Manufacturer's Model Name   \n",
-       "2   (0008, 0070)               Manufacturer   \n",
-       "3   (0020, 000d)         Study Instance UID   \n",
-       "4   (0008, 1030)          Study Description   \n",
-       "5   (0020, 0011)              Series Number   \n",
-       "6   (0020, 000e)        Series Instance UID   \n",
-       "7   (0018, 1020)          Software Versions   \n",
-       "8   (0018, 0010)       Contrast/Bolus Agent   \n",
-       "9   (0008, 0018)           SOP Instance UID   \n",
-       "10  (0018, 0050)            Slice Thickness   \n",
-       "11  (0028, 0030)              Pixel Spacing   \n",
-       "12  (0028, 0010)                       Rows   \n",
-       "13  (0028, 0011)                    Columns   \n",
-       "14  (0018, 0024)              Sequence Name   \n",
-       "15  (0018, 0087)    Magnetic Field Strength   \n",
-       "16  (0018, 0023)        MR Acquisition Type   \n",
-       "17  (0018, 0080)            Repetition Time   \n",
-       "18  (0018, 0081)                  Echo Time   \n",
-       "19  (0018, 0084)          Imaging Frequency   \n",
-       "20  (0018, 0024)              Sequence Name   \n",
-       "21  (0008, 0060)                   Modality   \n",
-       "22  (0018, 0010)       Contrast/Bolus Agent   \n",
-       "\n",
-       "                                                value  \n",
-       "0                                          170.550753  \n",
-       "1                                               Verio  \n",
-       "2                                             SIEMENS  \n",
-       "3   1.3.6.1.4.1.14519.5.2.1.4429.7055.304625516276...  \n",
-       "4                             FH-HEAD^Brain Protocols  \n",
-       "5                                                  11  \n",
-       "6   1.3.6.1.4.1.14519.5.2.1.4429.7055.197322826283...  \n",
-       "7                                        syngo MR B17  \n",
-       "8                                     10cc Multihance  \n",
-       "9   1.3.6.1.4.1.14519.5.2.1.4429.7055.169383040232...  \n",
-       "10                                                5.0  \n",
-       "11                                   [0.6875, 0.6875]  \n",
-       "12                                                320  \n",
-       "13                                                260  \n",
-       "14                                             *se2d1  \n",
-       "15                                                3.0  \n",
-       "16                                                 2D  \n",
-       "17                                              600.0  \n",
-       "18                                                8.9  \n",
-       "19                                         123.191547  \n",
-       "20                                             *se2d1  \n",
-       "21                                                 MR  \n",
-       "22                                    10cc Multihance  "
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list_tags = []\n",
-    "dicom_df = pd.DataFrame(columns=['tag','name','value'])\n",
-    "\n",
-    "for j in range(len(tags_file_osiris)):\n",
-    "    try:\n",
-    "        tag = tags_file_osiris['Source'][j]\n",
-    "\n",
-    "        tag_ext = re.search(r'\\((.*?)\\)',tag).group(1)\n",
-    "        tag_conv = \"0x\"+tag_ext[0:4]+tag_ext[5:]\n",
-    "        list_tags.append(tag_conv)\n",
-    "    except:\n",
-    "        pass\n",
-    "    \n",
-    "for i in list_tags:\n",
-    "    try:\n",
-    "        df_tag = str(DICOM_file[i].tag) \n",
-    "        df_name =  DICOM_file[i].name \n",
-    "        df_value = DICOM_file[i].value\n",
-    "        \n",
-    "        \n",
-    "        dicom_df = dicom_df.append({'tag':df_tag, 'name':df_name, 'value':df_value}, ignore_index=True)\n",
-    "        \n",
-    "    except:\n",
-    "        pass\n",
-    "    \n",
-    "    \n",
-    "dicom_df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b3a305ee-0a14-424e-8683-92c1ebc1c1ff",
-   "metadata": {},
-   "source": [
-    "#### Exporting to CSV"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "180c8dff-aae7-490a-83fb-209e670f5958",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dicom_df.to_csv('dicom_data-osiris.csv')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "183fa73e-0adb-410e-8560-33889d8a0089",
-   "metadata": {},
-   "source": [
-    "#### Just for fun!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "225ea97e-e261-46f2-ae57-67018c62c828",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "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": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "plt.imshow(DICOM_file.pixel_array, cmap=plt.cm.gray)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5a925648-a379-4e35-958a-c5d7a0946ac8",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "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": "3.8.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}