Musser_DICOM_BIDS_final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/emusser/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:6: UserWarning: The DICOM readers are highly experimental, unstable, and only work for Siemens time-series at the moment\n",
      "Please use with caution.  We would be grateful for your help in improving them\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# Import necessary modules\n",
    "# glob allows unix style pathname pattern expansion\n",
    "from glob import glob\n",
    "# pydicom is the python dicom reader\n",
    "import pydicom as dicom\n",
    "import nibabel.nicom.csareader as csareader\n",
    "import os\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5-T1w_MPR_vNav -- MPRAGE structrual dicoms\n",
    "# 6-fMRI_DistortionMap_PA -- fmri field map dicoms\n",
    "# 7-fMRI_DistortionMap_AP -- fmri field map dicoms opposite phase encode direction\n",
    "# 9-fMRI_REVL_ROI_loc_2 -- fmri localizer dicoms run1\n",
    "# 10-fMRI_REVL_Study_1 -- fmri task dicoms run1\n",
    "# 14-dMRI_DistortionMap_AP_dMRI_REVL -- dwi field map dicoms\n",
    "# 16-dMRI_AP_REVL -- diffusion weighted dicoms\n",
    "\n",
    "# directories will have to be specific to your computer\n",
    "base_dir = '/Users/emusser/Documents/GitHub'\n",
    "proj_dir = '/mattfeld_2020/code'\n",
    "subj_dir = '/Mattfeld_REVL-000-vCAT-021-S1/scans/10-fMRI_REVL_Study_1/resources/DICOM/files'\n",
    "\n",
    "# with strings I can concatenate them with simple addition lines\n",
    "data_dir = base_dir + proj_dir + subj_dir\n",
    "\n",
    "# Here I use glob to grab the dicom files\n",
    "# Why would I use glob?  What does it give me?\n",
    "data_files = glob(data_dir + '/*')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/Users/emusser/Documents/GitHub/mattfeld_2020/code/Mattfeld_REVL-000-vCAT-021-S1/scans/10-fMRI_REVL_Study_1/resources/DICOM/files/1.3.12.2.1107.5.2.43.166003.30000019061912563618000000017-10-6-1j8kpi7.dcm',\n",
       " '/Users/emusser/Documents/GitHub/mattfeld_2020/code/Mattfeld_REVL-000-vCAT-021-S1/scans/10-fMRI_REVL_Study_1/resources/DICOM/files/1.3.12.2.1107.5.2.43.166003.30000019061912563618000000017-10-292-x6mb7t.dcm',\n",
       " '/Users/emusser/Documents/GitHub/mattfeld_2020/code/Mattfeld_REVL-000-vCAT-021-S1/scans/10-fMRI_REVL_Study_1/resources/DICOM/files/1.3.12.2.1107.5.2.43.166003.30000019061912563618000000017-10-288-v6og2m.dcm',\n",
       " '/Users/emusser/Documents/GitHub/mattfeld_2020/code/Mattfeld_REVL-000-vCAT-021-S1/scans/10-fMRI_REVL_Study_1/resources/DICOM/files/1.3.12.2.1107.5.2.43.166003.30000019061912563618000000017-10-33-kymxgv.dcm',\n",
       " '/Users/emusser/Documents/GitHub/mattfeld_2020/code/Mattfeld_REVL-000-vCAT-021-S1/scans/10-fMRI_REVL_Study_1/resources/DICOM/files/1.3.12.2.1107.5.2.43.166003.30000019061912563618000000017-10-286-1krvknx.dcm']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_files[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here I am using the python dicom reader to read in a dicom header\n",
    "# What should go between the square brackets []\n",
    "data_set = dicom.dcmread(data_files[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "(0008, 0022) Acquisition Date                    DA: '20190619'\n",
       "(0008, 0023) Content Date                        DA: '20190619'\n",
       "(0008, 0030) Study Time                          TM: '150037.662000'\n",
       "(0008, 0031) Series Time                         TM: '154835.373000'\n",
       "(0008, 0032) Acquisition Time                    TM: '154758.855000'\n",
       "(0008, 0033) Content Time                        TM: '154835.406000'\n",
       "(0008, 0050) Accession Number                    SH: ''\n",
       "(0008, 0060) Modality                            CS: 'MR'\n",
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       "(0008, 0081) Institution Address                 ST: 'SW 8TH ST, OE116 11200,MIAMI,Med-Lab,US,33199'\n",
       "(0008, 0090) Referring Physician's Name          PN: ''\n",
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       "   ---------\n",
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       "   ---------\n",
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       "   ---------\n",
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       "   ---------\n",
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       "   ---------\n",
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       "   ---------\n",
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       "   ---------\n",
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       "   ---------\n",
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       "   ---------\n",
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       "   ---------\n",
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       "   ---------\n",
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       "   ---------\n",
       "   (0008, 1150) Referenced SOP Class UID            UI: MR Image Storage\n",
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       "   ---------\n",
       "   (0008, 1150) Referenced SOP Class UID            UI: MR Image Storage\n",
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       "   ---------\n",
       "   (0008, 1150) Referenced SOP Class UID            UI: MR Image Storage\n",
       "   (0008, 1155) Referenced SOP Instance UID         UI: 1.3.12.2.1107.5.2.43.166003.20190619154800826037183\n",
       "   ---------\n",
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       "   ---------\n",
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       "   ---------\n",
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       "   ---------\n",
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       "   ---------\n",
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       "   ---------\n",
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       "   ---------\n",
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       "   ---------\n",
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       "   ---------\n",
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       "   ---------\n",
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       "   ---------\n",
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       "   ---------\n",
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       "   ---------\n",
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       "   ---------\n",
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       "   ---------\n",
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       "   ---------\n",
       "   (0008, 1150) Referenced SOP Class UID            UI: MR Image Storage\n",
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       "   ---------\n",
       "   (0008, 1150) Referenced SOP Class UID            UI: MR Image Storage\n",
       "   (0008, 1155) Referenced SOP Instance UID         UI: 1.3.12.2.1107.5.2.43.166003.2019061915480010154437233\n",
       "   ---------\n",
       "   (0008, 1150) Referenced SOP Class UID            UI: MR Image Storage\n",
       "   (0008, 1155) Referenced SOP Instance UID         UI: 1.3.12.2.1107.5.2.43.166003.2019061915480010818137236\n",
       "   ---------\n",
       "   (0008, 1150) Referenced SOP Class UID            UI: MR Image Storage\n",
       "   (0008, 1155) Referenced SOP Instance UID         UI: 1.3.12.2.1107.5.2.43.166003.2019061915480011337937239\n",
       "   ---------\n",
       "   (0008, 1150) Referenced SOP Class UID            UI: MR Image Storage\n",
       "   (0008, 1155) Referenced SOP Instance UID         UI: 1.3.12.2.1107.5.2.43.166003.2019061915480011998437242\n",
       "   ---------\n",
       "   (0008, 1150) Referenced SOP Class UID            UI: MR Image Storage\n",
       "   (0008, 1155) Referenced SOP Instance UID         UI: 1.3.12.2.1107.5.2.43.166003.2019061915480012603537245\n",
       "   ---------\n",
       "   (0008, 1150) Referenced SOP Class UID            UI: MR Image Storage\n",
       "   (0008, 1155) Referenced SOP Instance UID         UI: 1.3.12.2.1107.5.2.43.166003.2019061915480013187037248\n",
       "   ---------\n",
       "   (0008, 1150) Referenced SOP Class UID            UI: MR Image Storage\n",
       "   (0008, 1155) Referenced SOP Instance UID         UI: 1.3.12.2.1107.5.2.43.166003.2019061915480013665037251\n",
       "   ---------\n",
       "   (0008, 1150) Referenced SOP Class UID            UI: MR Image Storage\n",
       "   (0008, 1155) Referenced SOP Instance UID         UI: 1.3.12.2.1107.5.2.43.166003.2019061915480014205237254\n",
       "   ---------\n",
       "   (0008, 1150) Referenced SOP Class UID            UI: MR Image Storage\n",
       "   (0008, 1155) Referenced SOP Instance UID         UI: 1.3.12.2.1107.5.2.43.166003.2019061915480014823537257\n",
       "   ---------\n",
       "   (0008, 1150) Referenced SOP Class UID            UI: MR Image Storage\n",
       "   (0008, 1155) Referenced SOP Instance UID         UI: 1.3.12.2.1107.5.2.43.166003.2019061915480015459337260\n",
       "   ---------\n",
       "   (0008, 1150) Referenced SOP Class UID            UI: MR Image Storage\n",
       "   (0008, 1155) Referenced SOP Instance UID         UI: 1.3.12.2.1107.5.2.43.166003.2019061915480015996837263\n",
       "   ---------\n",
       "   (0008, 1150) Referenced SOP Class UID            UI: MR Image Storage\n",
       "   (0008, 1155) Referenced SOP Instance UID         UI: 1.3.12.2.1107.5.2.43.166003.2019061915480016401237266\n",
       "   ---------\n",
       "   (0008, 1150) Referenced SOP Class UID            UI: MR Image Storage\n",
       "   (0008, 1155) Referenced SOP Instance UID         UI: 1.3.12.2.1107.5.2.43.166003.2019061915480016976337269\n",
       "   ---------\n",
       "   (0008, 1150) Referenced SOP Class UID            UI: MR Image Storage\n",
       "   (0008, 1155) Referenced SOP Instance UID         UI: 1.3.12.2.1107.5.2.43.166003.2019061915480017522037272\n",
       "   ---------\n",
       "(0010, 0010) Patient's Name                      PN: 'Mattfeld_REVL-000-vCAT-021'\n",
       "(0010, 0020) Patient ID                          LO: 'Mattfeld_REVL-000-vCAT-021-S1'\n",
       "(0010, 0030) Patient's Birth Date                DA: '19770101'\n",
       "(0010, 0040) Patient's Sex                       CS: 'M'\n",
       "(0010, 1010) Patient's Age                       AS: '042Y'\n",
       "(0010, 1020) Patient's Size                      DS: \"1.82880366\"\n",
       "(0010, 1030) Patient's Weight                    DS: \"84.3681915522\"\n",
       "(0010, 4000) Patient Comments                    LT: 'Project: Mattfeld_REVL;Mattfeld_REVL-000-vCAT-021 ; Session: Mattfeld_REVL-000-vCAT-021-S1'\n",
       "(0012, 0064)  De-identification Method Code Sequence   1 item(s) ---- \n",
       "   (0008, 0100) Code Value                          SH: '2'\n",
       "   (0008, 0102) Coding Scheme Designator            SH: 'XNAT'\n",
       "   (0008, 0103) Coding Scheme Version               SH: '0.1'\n",
       "   (0008, 0104) Code Meaning                        LO: 'XNAT Edit Script'\n",
       "   ---------\n",
       "(0018, 0015) Body Part Examined                  CS: 'BRAIN'\n",
       "(0018, 0020) Scanning Sequence                   CS: 'EP'\n",
       "(0018, 0021) Sequence Variant                    CS: 'SK'\n",
       "(0018, 0022) Scan Options                        CS: 'FS'\n",
       "(0018, 0023) MR Acquisition Type                 CS: '2D'\n",
       "(0018, 0024) Sequence Name                       SH: 'epfid2d1_90'\n",
       "(0018, 0025) Angio Flag                          CS: 'N'\n",
       "(0018, 0050) Slice Thickness                     DS: \"2.0\"\n",
       "(0018, 0080) Repetition Time                     DS: \"1760.0\"\n",
       "(0018, 0081) Echo Time                           DS: \"35.0\"\n",
       "(0018, 0083) Number of Averages                  DS: \"1.0\"\n",
       "(0018, 0084) Imaging Frequency                   DS: \"123.249153\"\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: \"2.0000000381972\"\n",
       "(0018, 0089) Number of Phase Encoding Steps      IS: \"90\"\n",
       "(0018, 0091) Echo Train Length                   IS: \"90\"\n",
       "(0018, 0093) Percent Sampling                    DS: \"90.0\"\n",
       "(0018, 0094) Percent Phase Field of View         DS: \"100.0\"\n",
       "(0018, 0095) Pixel Bandwidth                     DS: \"2780.0\"\n",
       "(0018, 1000) Device Serial Number                LO: '166003'\n",
       "(0018, 1020) Software Versions                   LO: 'syngo MR E11'\n",
       "(0018, 1030) Protocol Name                       LO: 'fMRI_REVL_Study_1'\n",
       "(0018, 1251) Transmit Coil Name                  SH: 'Body'\n",
       "(0018, 1310) Acquisition Matrix                  US: [100, 0, 0, 90]\n",
       "(0018, 1312) In-plane Phase Encoding Direction   CS: 'COL'\n",
       "(0018, 1314) Flip Angle                          DS: \"52.0\"\n",
       "(0018, 1315) Variable Flip Angle Flag            CS: 'N'\n",
       "(0018, 1316) SAR                                 DS: \"0.04013631633872\"\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, 1008) [CSA Image Header Type]             CS: 'IMAGE NUM 4'\n",
       "(0019, 1009) [CSA Image Header Version ??]       LO: '1.0'\n",
       "(0019, 100a) [NumberOfImagesInMosaic]            US: 66\n",
       "(0019, 100b) [SliceMeasurementDuration]          DS: \"630000.0\"\n",
       "(0019, 100f) [GradientMode]                      SH: 'Fast*'\n",
       "(0019, 1011) [FlowCompensation]                  SH: 'No'\n",
       "(0019, 1012) [TablePositionOrigin]               SL: [0, 0, -1306]\n",
       "(0019, 1013) [ImaAbsTablePosition]               SL: [0, 0, -1306]\n",
       "(0019, 1014) [ImaRelTablePosition]               IS: [0, 0, 0]\n",
       "(0019, 1015) [SlicePosition_PCS]                 FD: [-901.7710135, -821.05534252, 406.27493547]\n",
       "(0019, 1016) [TimeAfterStart]                    DS: \"15.315\"\n",
       "(0019, 1017) [SliceResolution]                   DS: \"1.0\"\n",
       "(0019, 1018) [RealDwellTime]                     IS: \"1800\"\n",
       "(0019, 1028) [BandwidthPerPixelPhaseEncode]      FD: 16.393\n",
       "(0019, 1029) [MosaicRefAcqTimes]                 FD: Array of 66 elements\n",
       "(0020, 000d) Study Instance UID                  UI: 1.3.12.2.1107.5.2.43.166003.30000019061912563618000000017\n",
       "(0020, 000e) Series Instance UID                 UI: 1.3.12.2.1107.5.2.43.166003.2019061915471170406436072.0.0.0\n",
       "(0020, 0010) Study ID                            SH: '1'\n",
       "(0020, 0011) Series Number                       IS: \"10\"\n",
       "(0020, 0012) Acquisition Number                  IS: \"6\"\n",
       "(0020, 0013) Instance Number                     IS: \"6\"\n",
       "(0020, 0032) Image Position (Patient)            DS: [-901.77101349831, -821.0553425197, 406.27493547262]\n",
       "(0020, 0037) Image Orientation (Patient)         DS: [1, 0, 0, 0, 0.86602540210929, -0.5000000029014]\n",
       "(0020, 0052) Frame of Reference UID              UI: 1.3.12.2.1107.5.2.43.166003.1.20190619151307196.0.0.0\n",
       "(0020, 1040) Position Reference Indicator        LO: ''\n",
       "(0020, 1041) Slice Location                      DS: \"-58.683259282487\"\n",
       "(0028, 0002) Samples per Pixel                   US: 1\n",
       "(0028, 0004) Photometric Interpretation          CS: 'MONOCHROME2'\n",
       "(0028, 0010) Rows                                US: 900\n",
       "(0028, 0011) Columns                             US: 900\n",
       "(0028, 0030) Pixel Spacing                       DS: [2, 2]\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: 1350\n",
       "(0028, 1050) Window Center                       DS: \"374.0\"\n",
       "(0028, 1051) Window Width                        DS: \"814.0\"\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 HEADER'\n",
       "(0029, 0012) Private Creator                     LO: 'SIEMENS MEDCOM HEADER2'\n",
       "(0029, 1008) [CSA Image Header Type]             CS: 'IMAGE NUM 4'\n",
       "(0029, 1009) [CSA Image Header Version]          LO: '20190619'\n",
       "(0029, 1010) [CSA Image Header Info]             OB: Array of 13572 elements\n",
       "(0029, 1018) [CSA Series Header Type]            CS: 'MR'\n",
       "(0029, 1019) [CSA Series Header Version]         LO: '20190619'\n",
       "(0029, 1020) [CSA Series Header Info]            OB: Array of 147536 elements\n",
       "(0029, 1120) [MedCom History Information]        OB: Array of 308 elements\n",
       "(0029, 1260) [Series Workflow Status]            LO: 'com'\n",
       "(0040, 0244) Performed Procedure Step Start Date DA: '20190619'\n",
       "(0040, 0245) Performed Procedure Step Start Time TM: '150037.732000'\n",
       "(0040, 0253) Performed Procedure Step ID         SH: 'MR20190619150037'\n",
       "(0040, 0254) Performed Procedure Step Descriptio LO: 'Mattfeld_REVL^Mattfeld_REVL'\n",
       "(0040, 0280) Comments on the Performed Procedure ST: ''\n",
       "(0051, 0010) Private Creator                     LO: 'SIEMENS MR HEADER'\n",
       "(0051, 1008) [CSA Image Header Type]             CS: 'IMAGE NUM 4'\n",
       "(0051, 1009) [CSA Image Header Version ??]       LO: '1.0'\n",
       "(0051, 100a) [Unknown]                           LO: 'TA 10:30'\n",
       "(0051, 100b) [AcquisitionMatrixText]             LO: '90*100'\n",
       "(0051, 100c) [Unknown]                           LO: 'FoV 1800*1800'\n",
       "(0051, 100e) [Unknown]                           LO: 'Tra>Cor(-30.0)'\n",
       "(0051, 100f) [CoilString]                        LO: 'HEA;HEP'\n",
       "(0051, 1011) [PATModeText]                       LO: 's3'\n",
       "(0051, 1012) [Unknown]                           SH: 'TP 0'\n",
       "(0051, 1013) [PositivePCSDirections]             SH: '+LPH'\n",
       "(0051, 1016) [Unknown]                           LO: 's3 M/ND/MOSAIC'\n",
       "(0051, 1017) [Unknown]                           SH: 'SL 2.0'\n",
       "(0051, 1019) [Unknown]                           LO: 'A1/FS'\n",
       "(7fe0, 0010) Pixel Data                          OW: Array of 1620000 elements"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of TRs collected equals: 355\n",
      "The timing of the TR was: 1760 ms\n",
      "The timing of the TE was: 35 ms\n",
      "The flip angle in degrees was: 52\n",
      "The name of the scan was: 52\n",
      "The phase encode direction was: COL\n",
      "COL = AP or PA; ROW = RL or LR\n",
      "The slice timing was: [1265.00000001, 0.0, 870.0, 77.50000001, 947.50000001, 157.5, 1027.5, 237.50000002, 1107.50000001, 315.0, 1185.0, 475.00000001, 1345.0, 552.49999999, 1422.50000002, 632.50000001, 1502.5, 709.99999999, 1580.00000002, 790.00000001, 1660.0, 394.99999999, 1265.00000001, 0.0, 870.0, 77.50000001, 947.50000001, 157.5, 1027.5, 237.50000002, 1107.50000001, 315.0, 1185.0, 475.00000001, 1345.0, 552.49999999, 1422.50000002, 632.50000001, 1502.5, 709.99999999, 1580.00000002, 790.00000001, 1660.0, 394.99999999, 1265.00000001, 0.0, 870.0, 77.50000001, 947.50000001, 157.5, 1027.5, 237.50000002, 1107.50000001, 315.0, 1185.0, 475.00000001, 1345.0, 552.49999999, 1422.50000002, 632.50000001, 1502.5, 709.99999999, 1580.00000002, 790.00000001, 1660.0, 394.99999999]\n",
      "The field of view - FOV was: FoV 1800*1800\n",
      "The phase encode direction was: 0\n",
      "1 = -; 0 = +\n",
      "COL, 1 = PA; COL, 0 = AP\n"
     ]
    }
   ],
   "source": [
    "# Elements we're interested in\n",
    "# Repetition Time\n",
    "# Echo Time\n",
    "# Acquisition Matrix \n",
    "# Flip Angle\n",
    "# Acquisition Number\n",
    "# Protocol Name\n",
    "# Slice times = data_set[0x00191029].value\n",
    "\n",
    "print('The number of TRs collected equals: {0}'.format(len(data_files)))\n",
    "print('The timing of the TR was: {0} ms'.format(data_set.RepetitionTime))\n",
    "print('The timing of the TE was: {0} ms'.format(data_set.EchoTime))\n",
    "print('The flip angle in degrees was: {0}'.format(data_set.FlipAngle))\n",
    "print('The name of the scan was: {0}'.format(data_set.FlipAngle))\n",
    "print('The phase encode direction was: {0}'.format(data_set[int('00181312', 16)].value))\n",
    "print('COL = AP or PA; ROW = RL or LR')\n",
    "print('The slice timing was: {0}'.format(data_set[0x00191029].value))\n",
    "print('The field of view - FOV was: {0}'.format(data_set[int('0051100c', 16)].value))\n",
    "\n",
    "# Why use the following format to access information in the header?\n",
    "#print(data_set[int('0051100c', 16)].value) # data_set[0x0051100c].value\n",
    "# Allows us to access information that is private in the dicom header.\n",
    "\n",
    "# How do I find phase encoding direction information?\n",
    "\n",
    "csa_str = data_set[int('00291010', 16)].value\n",
    "csa_tr = csareader.read(csa_str)\n",
    "print('The phase encode direction was: {0}'.format(csa_tr['tags']['PhaseEncodingDirectionPositive']['items'][0]))\n",
    "print('1 = -; 0 = +')\n",
    "print('COL, 1 = PA; COL, 0 = AP')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using heudiconv to convert dicoms into BIDs format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>series_id</th>\n",
       "      <th>series_files</th>\n",
       "      <th>dim1</th>\n",
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       "      <th>dim3</th>\n",
       "      <th>dim4</th>\n",
       "      <th>series_description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1-localizer_32ch</td>\n",
       "      <td>3</td>\n",
       "      <td>512</td>\n",
       "      <td>512</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>localizer_32ch</td>\n",
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       "      <td>1</td>\n",
       "      <td>2-T1w_MPR_vNav_setter</td>\n",
       "      <td>32</td>\n",
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       "      <td>1</td>\n",
       "      <td>T1w_MPR_vNav_setter</td>\n",
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       "      <td>2</td>\n",
       "      <td>3-T1w_MPR_vNav</td>\n",
       "      <td>145</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>145</td>\n",
       "      <td>T1w_MPR_vNav_setter</td>\n",
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       "      <td>3</td>\n",
       "      <td>4-T1w_MPR_vNav</td>\n",
       "      <td>176</td>\n",
       "      <td>256</td>\n",
       "      <td>256</td>\n",
       "      <td>176</td>\n",
       "      <td>1</td>\n",
       "      <td>T1w_MPR_vNav</td>\n",
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       "      <td>4</td>\n",
       "      <td>5-T1w_MPR_vNav</td>\n",
       "      <td>176</td>\n",
       "      <td>256</td>\n",
       "      <td>256</td>\n",
       "      <td>176</td>\n",
       "      <td>1</td>\n",
       "      <td>T1w_MPR_vNav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>6-fMRI_DistortionMap_PA</td>\n",
       "      <td>66</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>fMRI_DistortionMap_PA</td>\n",
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       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>7-fMRI_DistortionMap_AP</td>\n",
       "      <td>66</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>fMRI_DistortionMap_AP</td>\n",
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       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>8-fMRI_REVL_ROI_loc_1</td>\n",
       "      <td>304</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>66</td>\n",
       "      <td>304</td>\n",
       "      <td>fMRI_REVL_ROI_loc_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>9-fMRI_REVL_ROI_loc_2</td>\n",
       "      <td>304</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>66</td>\n",
       "      <td>304</td>\n",
       "      <td>fMRI_REVL_ROI_loc_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>10-fMRI_REVL_Study_1</td>\n",
       "      <td>355</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>66</td>\n",
       "      <td>355</td>\n",
       "      <td>fMRI_REVL_Study_1</td>\n",
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       "      <td>10</td>\n",
       "      <td>11-fMRI_REVL_Study_2</td>\n",
       "      <td>355</td>\n",
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       "      <td>100</td>\n",
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       "      <td>355</td>\n",
       "      <td>fMRI_REVL_Study_2</td>\n",
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       "      <td>11</td>\n",
       "      <td>12-fMRI_REVL_Study_3</td>\n",
       "      <td>355</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>66</td>\n",
       "      <td>355</td>\n",
       "      <td>fMRI_REVL_Study_3</td>\n",
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       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>13-fMRI_REVL_Study_4</td>\n",
       "      <td>355</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>66</td>\n",
       "      <td>355</td>\n",
       "      <td>fMRI_REVL_Study_4</td>\n",
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       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>14-dMRI_DistortionMap_AP_dMRI_REVL</td>\n",
       "      <td>81</td>\n",
       "      <td>140</td>\n",
       "      <td>140</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>dMRI_DistortionMap_AP_dMRI_REVL</td>\n",
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       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>15-dMRI_DistortionMap_PA_dMRI_REVL</td>\n",
       "      <td>81</td>\n",
       "      <td>140</td>\n",
       "      <td>140</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>dMRI_DistortionMap_PA_dMRI_REVL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>16-dMRI_AP_REVL</td>\n",
       "      <td>103</td>\n",
       "      <td>140</td>\n",
       "      <td>140</td>\n",
       "      <td>81</td>\n",
       "      <td>103</td>\n",
       "      <td>dMRI_AP_REVL</td>\n",
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      ],
      "text/plain": [
       "                             series_id  series_files  dim1  dim2  dim3  dim4  \\\n",
       "0                     1-localizer_32ch             3   512   512     3     1   \n",
       "1                2-T1w_MPR_vNav_setter            32    32    32    32     1   \n",
       "2                       3-T1w_MPR_vNav           145    32    32    32   145   \n",
       "3                       4-T1w_MPR_vNav           176   256   256   176     1   \n",
       "4                       5-T1w_MPR_vNav           176   256   256   176     1   \n",
       "5              6-fMRI_DistortionMap_PA            66   100   100    66     1   \n",
       "6              7-fMRI_DistortionMap_AP            66   100   100    66     1   \n",
       "7                8-fMRI_REVL_ROI_loc_1           304   100   100    66   304   \n",
       "8                9-fMRI_REVL_ROI_loc_2           304   100   100    66   304   \n",
       "9                 10-fMRI_REVL_Study_1           355   100   100    66   355   \n",
       "10                11-fMRI_REVL_Study_2           355   100   100    66   355   \n",
       "11                12-fMRI_REVL_Study_3           355   100   100    66   355   \n",
       "12                13-fMRI_REVL_Study_4           355   100   100    66   355   \n",
       "13  14-dMRI_DistortionMap_AP_dMRI_REVL            81   140   140    81     1   \n",
       "14  15-dMRI_DistortionMap_PA_dMRI_REVL            81   140   140    81     1   \n",
       "15                     16-dMRI_AP_REVL           103   140   140    81   103   \n",
       "\n",
       "                 series_description  \n",
       "0                    localizer_32ch  \n",
       "1               T1w_MPR_vNav_setter  \n",
       "2               T1w_MPR_vNav_setter  \n",
       "3                      T1w_MPR_vNav  \n",
       "4                      T1w_MPR_vNav  \n",
       "5             fMRI_DistortionMap_PA  \n",
       "6             fMRI_DistortionMap_AP  \n",
       "7               fMRI_REVL_ROI_loc_1  \n",
       "8               fMRI_REVL_ROI_loc_2  \n",
       "9                 fMRI_REVL_Study_1  \n",
       "10                fMRI_REVL_Study_2  \n",
       "11                fMRI_REVL_Study_3  \n",
       "12                fMRI_REVL_Study_4  \n",
       "13  dMRI_DistortionMap_AP_dMRI_REVL  \n",
       "14  dMRI_DistortionMap_PA_dMRI_REVL  \n",
       "15                     dMRI_AP_REVL  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 1 - run heudiconv\n",
    "#Run via terminal  heudiconv -d '/Users/emusser/Documents/GitHub/mattfeld_2020/code/Mattfeld_REVL-000-vCAT-{subject}-S1/*/*/*/*/*/*' -s 021 -c none -f convertall -o /Users/emusser/Documents/GitHub/mattfeld_2020/dset/.heudiconv/021/info\n",
    "\n",
    "#/Users/emusser/Documents/GitHub/mattfeld_2020/dset/.heudiconv/021/info\n",
    "path2info_file = os.path.join(base_dir, 'mattfeld_2020', 'dset', '.heudiconv/021/info')\n",
    "dicominfo_df = pd.read_csv(path2info_file + '/.heudiconv/021/info/dicominfo.tsv', sep=\"\\t\")\n",
    "limited_dicominfo_df = dicominfo_df[['series_id', 'series_files', 'dim1', 'dim2', 'dim3', 'dim4', 'series_description']]\n",
    "\n",
    "limited_dicominfo_df\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import os\n",
    "\n",
    "\n",
    "def create_key(template, outtype=('nii.gz',), annotation_classes=None):\n",
    "    if template is None or not template:\n",
    "        raise ValueError('Template must be a valid format string')\n",
    "    return template, outtype, annotation_classes\n",
    "\n",
    "\n",
    "def infotodict(seqinfo):\n",
    "    \"\"\"Heuristic evaluator for determining which runs belong where\n",
    "\n",
    "    allowed template fields - follow python string module:\n",
    "\n",
    "    item: index within category\n",
    "    subject: participant id\n",
    "    seqitem: run number during scanning\n",
    "    subindex: sub index within group\n",
    "    session: sess-[sessionID]\n",
    "    bids_subject_session_dir: BIDS subject/session directory\n",
    "    bids_subject_session_prefix: BIDS subject/session prefix\n",
    "    \"\"\"\n",
    "\n",
    "    t1w = create_key('sub-{subject}/anat/sub-{subject}_run-{item}_T1w')\n",
    "    dwi = create_key('sub-{subject}/dwi/sub-{subject}_run-{item}_dwi')\n",
    "    loc1_task = create_key('sub-{subject}/func/sub-{subject}_task-loc_run-1_bold')\n",
    "    loc2_task = create_key('sub-{subject}/func/sub-{subject}_task-loc_run-2_bold')\n",
    "    study1_task = create_key('sub-{subject}/func/sub-{subject}_task-study_run-1_bold')\n",
    "    study2_task = create_key('sub-{subject}/func/sub-{subject}_task-study_run-2_bold')\n",
    "    study3_task = create_key('sub-{subject}/func/sub-{subject}_task-study_run-3_bold')\n",
    "    study4_task = create_key('sub-{subject}/func/sub-{subject}_task-study_run-4_bold')\n",
    "    task_fmap = create_key('sub-{subject}/fmap/sub-{subject}_acq-func_dir-{dir}_run{item}_epi')\n",
    "    dwi_fmap = create_key('sub-{subject}/fmap/sub-{subject}_acq-dwi_dir-{dir}_run{item}_epi')\n",
    "    \n",
    "    info = {twi : [],\n",
    "           dwi : [],\n",
    "           loc1_task : [],\n",
    "           loc2_task :[],\n",
    "           study1_task : [],\n",
    "           study2_task : [],\n",
    "           study3_task : [],\n",
    "           study4_task : [],\n",
    "           task_fmap : [],\n",
    "           dwi_fmap : []}\n",
    "    last_run = len(seqinfo)\n",
    "\n",
    "    for s in seqinfo:\n",
    "        xdim, ydim, slice_num, timepoints=(s[6], s[7], s[8], s[9])\n",
    "        if (slice_num==176) and (timepoints==1) and (\"T1w_MPR_vNav\" in s.series_description):\n",
    "            info[t1w].append(s[2])\n",
    "        elif (slice_num>1) and (timepoints==103) and (\"dMRI\" in s[12]):\n",
    "            info[dwi].append(s[2])\n",
    "        elif (timepoints==304) and (\"ROI_loc_1\" in s[12]):\n",
    "            info[loc1_task].append(s[2])\n",
    "        elif (timepoints==355) and (\"Study_1\" in s[12]):\n",
    "            info[study1_task].append(s[2])\n",
    "        elif (timepoints==355) and (\"Study_2\" in s[12]):\n",
    "            info[study2_task].append(s[2])\n",
    "        elif (timepoints==355) and (\"Study_3\" in s[12]):\n",
    "            info[study3_task].append(s[2])\n",
    "        elif (timepoints==355) and (\"Study_4\" in s[12]):\n",
    "            info[study4_task].append(s[2])\n",
    "        elif (\"dMRI_DistortionMap_AP\" in s.series_description):\n",
    "            info[dwi_fmap].append({\"item\": s[2], \"dir\": \"AP\"})\n",
    "        elif (\"dMRI_DistortionMap_PA\" in s.series_description):\n",
    "            info[dwi_fmap].append({\"item\": s[2], \"dir\": \"PA\"})\n",
    "        elif (\"fMRI_DistortionMap_AP\" in s.series_description):\n",
    "           info[task_fmap].append({\"item\": s[2], \"dir\": \"AP\"})\n",
    "        elif (\"fMRI_DistortionMap_PA\" in s.series_description):\n",
    "           info[task_fmap].append({\"item\": s[2], \"dir\": \"PA\"})\n",
    "        else:\n",
    "            pass\n",
    "    return info\n",
    "      \n",
    "    \n",
    "   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n     The namedtuple `s` contains the following fields:\\n\\n    * total_files_till_now\\n      * example_dcm_file\\n    * series_id\\n     * dcm_dir_name\\n     * unspecified2\\n       * unspecified3\\n       * dim1\\n        * dim2\\n        * dim3\\n        * dim4\\n        * TR\\n        * TE\\n        * protocol_name\\n        * is_motion_corrected\\n        * is_derived\\n        * patient_id\\n        * study_description\\n        * referring_physician_name\\n        * series_description\\n        * image_type\\n        '"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "     The namedtuple `s` contains the following fields:\n",
    "\n",
    "    * total_files_till_now\n",
    "      * example_dcm_file\n",
    "    * series_id\n",
    "     * dcm_dir_name\n",
    "     * unspecified2\n",
    "       * unspecified3\n",
    "       * dim1\n",
    "        * dim2\n",
    "        * dim3\n",
    "        * dim4\n",
    "        * TR\n",
    "        * TE\n",
    "        * protocol_name\n",
    "        * is_motion_corrected\n",
    "        * is_derived\n",
    "        * patient_id\n",
    "        * study_description\n",
    "        * referring_physician_name\n",
    "        * series_description\n",
    "        * image_type\n",
    "        \"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "heudiconv -d \"/Users/amattfel/Documents/GitHub/mattfeld_2020/sourcedata/Mattfeld_REVL-000-vCAT-{subject}-S1/*/*/*/D*/*/*\" -s 021 -f psb6351_heuristic.py -b -o /Users/amattfel/Documents/GitHub/mattfeld_2020/dset"
   ]
  }
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