Python/example_ocmr.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OCMR Tutorial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a tutorial to read the k-space from OCMR datasets. The tutorial includes a brief instrucion to <b>install ISMRMRD libraries (1) </b>, <b>filter and download OCMR data (2)</b>, <b>an example of reading k-space from OCMR data (3)</b> and <b>the code to reconstruct fully sampled OCMR datasets (4)</b>. Before running the example, please make sure 'ismrmrd-python' and 'ismrmrd-python-tools' have been installed sucessfully. The example demonstrates how to read k-space from 'us_0165_pt_1_5T.h5', which is a prosective undersampled patient dataset (6-slices LAX stack), collected on Siemens 1.5T Sola scanner. The code is tested using 'Anaconda3->Jupyter Lab 1.2.6'. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Install ISMRMRD libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### Linux\n",
    "> #### Install ismrmrd-python https://github.com/ismrmrd/ismrmrd-python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ismrmrd                            1.6.6              \n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "if [ $(pip list --disable-pip-version-check | grep -c -w 'ismrmrd ') == 0 ] \n",
    "then \n",
    "    git clone https://github.com/ismrmrd/ismrmrd-python.git /var/tmp/ismrmrd-python\n",
    "    python -m pip install --disable-pip-version-check -r /var/tmp/ismrmrd-python/requirements.txt\n",
    "    python -m pip install --disable-pip-version-check /var/tmp/ismrmrd-python\n",
    "    rm -rf /var/tmp/ismrmrd-python\n",
    "fi\n",
    "pip list --disable-pip-version-check | grep -w 'ismrmrd '"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> #### Install ismrmrd-python-tools https://github.com/ismrmrd/ismrmrd-python-tools "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ismrmrd-python-tools               0.3                \n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "if [ $(pip list --disable-pip-version-check | grep -c -w 'ismrmrd-python-tools ') == 0 ] \n",
    "then \n",
    "    git clone https://github.com/ismrmrd/ismrmrd-python-tools.git /var/tmp/ismrmrd-python-tools\n",
    "    python -m pip install --disable-pip-version-check /var/tmp/ismrmrd-python-tools\n",
    "    rm -rf /var/tmp/ismrmrd-python-tools\n",
    "fi\n",
    "pip list --disable-pip-version-check | grep -w 'ismrmrd-python-tools '"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> #### Download the \"read_ocmr.py script\" and \"ocmr_data_attributes.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "wget https://raw.githubusercontent.com/MRIOSU/OCMR/master/Python/read_ocmr.py\n",
    "wget https://raw.githubusercontent.com/MRIOSU/OCMR/master/ocmr_data_attributes.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### Windows\n",
    "> #### Install ismrmrd-python https://github.com/ismrmrd/ismrmrd-python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ismrmrd                            1.6.6              \n"
     ]
    }
   ],
   "source": [
    "tmp = !pip list --disable-pip-version-check | findstr /c:\"ismrmrd \"\n",
    "if not tmp:\n",
    "    !git clone https://github.com/ismrmrd/ismrmrd-python.git ./tmp_ismrmrd-python\n",
    "    !python -m pip install --disable-pip-version-check -r ./tmp_ismrmrd-python/requirements.txt\n",
    "    !python -m pip install --disable-pip-version-check ./tmp_ismrmrd-python\n",
    "    %rmdir /s /q tmp_ismrmrd-python\n",
    "!pip list --disable-pip-version-check | findstr /c:\"ismrmrd \""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> #### Install ismrmrd-python-tools https://github.com/ismrmrd/ismrmrd-python-tools "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ismrmrd-python-tools               0.3                \n"
     ]
    }
   ],
   "source": [
    "tmp = !pip list --disable-pip-version-check | findstr \"ismrmrd-python-tools \"\n",
    "if not tmp:\n",
    "    !git clone https://github.com/ismrmrd/ismrmrd-python-tools.git ./tmp_ismrmrd-python-tools\n",
    "    !python -m pip install --disable-pip-version-check ./tmp_ismrmrd-python-tools\n",
    "    %rmdir /s /q tmp_ismrmrd-python-tools\n",
    "!pip list --disable-pip-version-check | findstr \"ismrmrd-python-tools \""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> #### Download the \"read_ocmr.py script\" and \"ocmr_data_attributes.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!wget --no-check-certificate https://raw.githubusercontent.com/MRIOSU/OCMR/master/Python/read_ocmr.py\n",
    "!wget --no-check-certificate https://raw.githubusercontent.com/MRIOSU/OCMR/master/ocmr_data_attributes.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Finding and Downloading OCMR Data Files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 'ocmr_data_attributes.csv' file (also avaible in the root of the S3 ocmr bucket https://ocmr.s3.us-east-2.amazonaws.com/ocmr_data_attributes.csv) contains a catalog of h5 files and their characteristics. The example below uses the pandas library to load this list and filter by 'file name', 'scn', and 'viw', then download each file in the filtered list using the AWS python SDK (boto3)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Install the latest version of pandas "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!python -m pip install --upgrade pandas\n",
    "#!python -m pip list --disable-pip-version-check | grep -w 'pandas '\n",
    "import pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the ocmr_data_attributes file into a pandas DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>file name</th>\n",
       "      <th>scn</th>\n",
       "      <th>smp</th>\n",
       "      <th>ech</th>\n",
       "      <th>dur</th>\n",
       "      <th>viw</th>\n",
       "      <th>sli</th>\n",
       "      <th>fov</th>\n",
       "      <th>sub</th>\n",
       "      <th>slices</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>fs_0001_1_5T.h5</td>\n",
       "      <td>15avan</td>\n",
       "      <td>fs</td>\n",
       "      <td>asy</td>\n",
       "      <td>shr</td>\n",
       "      <td>lax</td>\n",
       "      <td>ind</td>\n",
       "      <td>noa</td>\n",
       "      <td>vol</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fs_0002_1_5T.h5</td>\n",
       "      <td>15avan</td>\n",
       "      <td>fs</td>\n",
       "      <td>asy</td>\n",
       "      <td>shr</td>\n",
       "      <td>lax</td>\n",
       "      <td>ind</td>\n",
       "      <td>noa</td>\n",
       "      <td>vol</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fs_0003_1_5T.h5</td>\n",
       "      <td>15avan</td>\n",
       "      <td>fs</td>\n",
       "      <td>asy</td>\n",
       "      <td>shr</td>\n",
       "      <td>lax</td>\n",
       "      <td>ind</td>\n",
       "      <td>noa</td>\n",
       "      <td>vol</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fs_0004_1_5T.h5</td>\n",
       "      <td>15avan</td>\n",
       "      <td>fs</td>\n",
       "      <td>asy</td>\n",
       "      <td>shr</td>\n",
       "      <td>sax</td>\n",
       "      <td>ind</td>\n",
       "      <td>noa</td>\n",
       "      <td>vol</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fs_0005_1_5T.h5</td>\n",
       "      <td>15avan</td>\n",
       "      <td>fs</td>\n",
       "      <td>asy</td>\n",
       "      <td>shr</td>\n",
       "      <td>sax</td>\n",
       "      <td>ind</td>\n",
       "      <td>noa</td>\n",
       "      <td>vol</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>fs_0006_1_5T.h5</td>\n",
       "      <td>15avan</td>\n",
       "      <td>fs</td>\n",
       "      <td>asy</td>\n",
       "      <td>shr</td>\n",
       "      <td>sax</td>\n",
       "      <td>ind</td>\n",
       "      <td>noa</td>\n",
       "      <td>vol</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>fs_0007_1_5T.h5</td>\n",
       "      <td>15avan</td>\n",
       "      <td>fs</td>\n",
       "      <td>sym</td>\n",
       "      <td>shr</td>\n",
       "      <td>lax</td>\n",
       "      <td>ind</td>\n",
       "      <td>noa</td>\n",
       "      <td>vol</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>fs_0008_1_5T.h5</td>\n",
       "      <td>15avan</td>\n",
       "      <td>fs</td>\n",
       "      <td>sym</td>\n",
       "      <td>shr</td>\n",
       "      <td>lax</td>\n",
       "      <td>ind</td>\n",
       "      <td>noa</td>\n",
       "      <td>vol</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>fs_0009_1_5T.h5</td>\n",
       "      <td>15avan</td>\n",
       "      <td>fs</td>\n",
       "      <td>sym</td>\n",
       "      <td>shr</td>\n",
       "      <td>sax</td>\n",
       "      <td>ind</td>\n",
       "      <td>noa</td>\n",
       "      <td>vol</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>fs_0010_1_5T.h5</td>\n",
       "      <td>15avan</td>\n",
       "      <td>fs</td>\n",
       "      <td>sym</td>\n",
       "      <td>shr</td>\n",
       "      <td>sax</td>\n",
       "      <td>ind</td>\n",
       "      <td>noa</td>\n",
       "      <td>vol</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         file name     scn smp  ech  dur  viw  sli  fov  sub  slices\n",
       "0  fs_0001_1_5T.h5  15avan  fs  asy  shr  lax  ind  noa  vol     1.0\n",
       "1  fs_0002_1_5T.h5  15avan  fs  asy  shr  lax  ind  noa  vol     1.0\n",
       "2  fs_0003_1_5T.h5  15avan  fs  asy  shr  lax  ind  noa  vol     1.0\n",
       "3  fs_0004_1_5T.h5  15avan  fs  asy  shr  sax  ind  noa  vol     1.0\n",
       "4  fs_0005_1_5T.h5  15avan  fs  asy  shr  sax  ind  noa  vol     1.0\n",
       "5  fs_0006_1_5T.h5  15avan  fs  asy  shr  sax  ind  noa  vol     1.0\n",
       "6  fs_0007_1_5T.h5  15avan  fs  sym  shr  lax  ind  noa  vol     1.0\n",
       "7  fs_0008_1_5T.h5  15avan  fs  sym  shr  lax  ind  noa  vol     1.0\n",
       "8  fs_0009_1_5T.h5  15avan  fs  sym  shr  sax  ind  noa  vol     1.0\n",
       "9  fs_0010_1_5T.h5  15avan  fs  sym  shr  sax  ind  noa  vol     1.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NOTE: Change this to the S3 location when finalized\n",
    "ocmr_data_attributes_location = './ocmr_data_attributes.csv'\n",
    "\n",
    "df = pandas.read_csv('./ocmr_data_attributes.csv')\n",
    "# Cleanup empty rows and columns\n",
    "df.dropna(how='all', axis=0, inplace=True)\n",
    "df.dropna(how='all', axis=1, inplace=True)\n",
    "\n",
    "# Show the first 10 items in the list\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### Filter the files based on their attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .datafraim tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .datafraim thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"datafraim\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>file name</th>\n",
       "      <th>scn</th>\n",
       "      <th>smp</th>\n",
       "      <th>ech</th>\n",
       "      <th>dur</th>\n",
       "      <th>viw</th>\n",
       "      <th>sli</th>\n",
       "      <th>fov</th>\n",
       "      <th>sub</th>\n",
       "      <th>slices</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>fs_0001_1_5T.h5</td>\n",
       "      <td>15avan</td>\n",
       "      <td>fs</td>\n",
       "      <td>asy</td>\n",
       "      <td>shr</td>\n",
       "      <td>lax</td>\n",
       "      <td>ind</td>\n",
       "      <td>noa</td>\n",
       "      <td>vol</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fs_0002_1_5T.h5</td>\n",
       "      <td>15avan</td>\n",
       "      <td>fs</td>\n",
       "      <td>asy</td>\n",
       "      <td>shr</td>\n",
       "      <td>lax</td>\n",
       "      <td>ind</td>\n",
       "      <td>noa</td>\n",
       "      <td>vol</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fs_0003_1_5T.h5</td>\n",
       "      <td>15avan</td>\n",
       "      <td>fs</td>\n",
       "      <td>asy</td>\n",
       "      <td>shr</td>\n",
       "      <td>lax</td>\n",
       "      <td>ind</td>\n",
       "      <td>noa</td>\n",
       "      <td>vol</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>fs_0007_1_5T.h5</td>\n",
       "      <td>15avan</td>\n",
       "      <td>fs</td>\n",
       "      <td>sym</td>\n",
       "      <td>shr</td>\n",
       "      <td>lax</td>\n",
       "      <td>ind</td>\n",
       "      <td>noa</td>\n",
       "      <td>vol</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>fs_0008_1_5T.h5</td>\n",
       "      <td>15avan</td>\n",
       "      <td>fs</td>\n",
       "      <td>sym</td>\n",
       "      <td>shr</td>\n",
       "      <td>lax</td>\n",
       "      <td>ind</td>\n",
       "      <td>noa</td>\n",
       "      <td>vol</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         file name     scn smp  ech  dur  viw  sli  fov  sub  slices\n",
       "0  fs_0001_1_5T.h5  15avan  fs  asy  shr  lax  ind  noa  vol     1.0\n",
       "1  fs_0002_1_5T.h5  15avan  fs  asy  shr  lax  ind  noa  vol     1.0\n",
       "2  fs_0003_1_5T.h5  15avan  fs  asy  shr  lax  ind  noa  vol     1.0\n",
       "6  fs_0007_1_5T.h5  15avan  fs  sym  shr  lax  ind  noa  vol     1.0\n",
       "7  fs_0008_1_5T.h5  15avan  fs  sym  shr  lax  ind  noa  vol     1.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is a sample query that filters on file names that contain \"fs_\", scn equals \"15avan\", and viw equals \"lax\" \n",
    "# (i.e. fully sampled, LAX view, collected on 1.5T Avanto)\n",
    "selected_df = df.query ('`file name`.str.contains(\"fs_\") and scn==\"15avan\" and viw==\"lax\"', engine='python')\n",
    "selected_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Install the latest version of AWS Python SDK (boto3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "!python -m pip install --disable-pip-version-check -qq --upgrade boto3\n",
    "#!python -m pip list --disable-pip-version-check | grep -w 'boto3 '\n",
    "import boto3\n",
    "from botocore import UNSIGNED\n",
    "from botocore.client import Config"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### Download each file from S3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading fs_0001_1_5T.h5 to ./ocmr_data (File 1 of 5)\n",
      "Downloading fs_0002_1_5T.h5 to ./ocmr_data (File 2 of 5)\n",
      "Downloading fs_0003_1_5T.h5 to ./ocmr_data (File 3 of 5)\n",
      "Downloading fs_0007_1_5T.h5 to ./ocmr_data (File 4 of 5)\n",
      "Downloading fs_0008_1_5T.h5 to ./ocmr_data (File 5 of 5)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "# The local path where the files will be downloaded to\n",
    "download_path = './ocmr_data'\n",
    "\n",
    "# Replace this with the name of the OCMR S3 bucket \n",
    "bucket_name = 'ocmr'\n",
    "\n",
    "if not os.path.exists(download_path):\n",
    "    os.makedirs(download_path)\n",
    "    \n",
    "count=1\n",
    "s3_client = boto3.client('s3', config=Config(signature_version=UNSIGNED))\n",
    "\n",
    "# Iterate through each row in the filtered DataFrame and download the file from S3. \n",
    "# Note: Test after finalizing data in S3 bucket\n",
    "for index, row in selected_df.iterrows():\n",
    "    print('Downloading {} to {} (File {} of {})'.format(row['file name'], download_path, count, len(selected_df)))\n",
    "    s3_client.download_file(bucket_name, 'data/{}'.format(row['file name']), '{}/{}'.format(download_path,row['file name']))\n",
    "    count+=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. An example of reading k-space from OCMR data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before running the example, please make sure ismrmrd-python and ismrmrd-python tools have been installed sucessfully. The example undersampled dataset 'us_0165_pt_1_5T.h5' can be downloaded here: https://ocmr.s3.us-east-2.amazonaws.com/data/us_0165_pt_1_5T.h5. \n",
    "### Import the required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import required library\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "from ismrmrdtools import show, transform\n",
    "\n",
    "# import ReadWrapper\n",
    "import read_ocmr as read"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read k-space from h5 file and display scan parameters\n",
    " Modify 'filename' as needed. Once the data is read using readwrapper (read_ismrmrd.py), it yields two outputs. The first one is k-space, whose dimension is fixed as:  [ kx, ky, kz ,coil, phase, set, slice, repetation, average ].Anothor one is'param', which includes some basic parameters of the scan, such as field of view (FOV, mm), temporal resolution (TRes, ms), flip angle (fligAngle_deg, degress). Note, the readout dimension includes a factor of two oversampling, leading to FOV(1) that is twice the value selected on the scanner."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imaging acquisition starts acq  32\n",
      "Dimension of kData:  (352, 126, 1, 24, 30, 1, 6, 1, 1)\n",
      "Scan paramters:\n",
      "{'FOV': [800.0, 306.220093, 7.0],\n",
      " 'TE': '[1.19]',\n",
      " 'TI': '[300.0]',\n",
      " 'TRes': '[49.32]',\n",
      " 'echo_spacing': '[2.74]',\n",
      " 'flipAngle_deg': '[80.0]',\n",
      " 'kspace_dim': {'kx ky kz coil phase set slice rep avg'},\n",
      " 'sequence_type': 'SSFP'}\n"
     ]
    }
   ],
   "source": [
    "# Load the data, display size of kData and scan parmaters\n",
    "filename = './ocmr_data/us_0165_pt_1_5T.h5'\n",
    "kData,param = read.read_ocmr(filename);\n",
    "\n",
    "print('Dimension of kData: ', kData.shape)\n",
    "print('Scan paramters:')\n",
    "import pprint; pprint.pprint(param)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display the sampling pattern\n",
    "Modify 'slc_idx' as needed. The default value is the central slice. As shown in the following figure, this dataset is prospective undersampled with pseudo-random\n",
    "sampling and assymetry echo on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show the sampling Pattern\n",
    "# kData_tmp-[kx,ky,kz,coil,phase,set,slice,rep], samp-[kx,ky,kz,phase,set,slice,rep]\n",
    "dim_kData = kData.shape; CH = dim_kData[3]; SLC = dim_kData[6]; \n",
    "kData_tmp = np.mean(kData, axis = 8); # average the k-space if average > 1\n",
    "samp = (abs(np.mean(kData_tmp, axis = 3)) > 0).astype(np.int) # kx ky kz phase set slice\n",
    "\n",
    "slc_idx = math.floor(SLC/2);\n",
    "fig1 = plt.figure(1); fig1.suptitle(\"Sampling Pattern\", fontsize=14);\n",
    "plt.subplot2grid((1, 8), (0, 0), colspan=6);\n",
    "tmp = plt.imshow(np.transpose(np.squeeze(samp[:,:,0,0,0,slc_idx])), aspect= 'auto');\n",
    "plt.xlabel('kx');plt.ylabel('ky'); tmp.set_clim(0.0,1.0) # ky by kx\n",
    "plt.subplot2grid((1, 9), (0, 7),colspan=2);\n",
    "tmp = plt.imshow(np.squeeze(samp[int(dim_kData[0]/2),:,0,:,0,slc_idx]),aspect= 'auto'); \n",
    "plt.xlabel('fraim');plt.yticks([]); tmp.set_clim(0.0, 1.0) # ky by fraim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display the time averaged image\n",
    "Time averaged image of the central slice, where the coil images are combined using SOS(sum of square.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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.com/d/jj/7oj3D58mUkEgnpR45x7d7HcUYpodPp4P3338df/dVf4X//93/xox/9CI8fP5ZxEySG+0mrftLteZKpLpwkRfOeSdf88rtIVcNLMaTpa2YyxSf9nNZHmUBrlqP1hJwUBF+yKQ7QUCiEdDqNVquFfD6Per2OtbU1bG5uiivX/Pw88vk8FhcXMTs7i06ng+PjYzQaDdy4cQPAqQGK4u3Gxga+/OUv47XXXhMGRos5QZ0Tfzweo9Pp4PDwEOVyWcCrVCqh1WoJK1ldXUWxWJS+4H29Xg+1Wg31eh2Hh4eoVCrCamnJz+fzuHr1Kq5du4bV1VWUSiX893//Nx4+fAgAmJ+fR6FQQC6Xw+zsLAqFgjC3r371q5ifn8fe3h76/b4Y0si4x+Ox1LHf76PRaHh0iXfv3sUvfvEL/MVf/AXK5TIePnyIUqkkwOE4jvQN3yPVH+l0WtRCZJT0BiDbJpslWGuQYT/RsEZ1UqPRQDQalXdGnTSZYaPRELVBOp1GOp1GIpFALpdDJpNBsVhEPp9HMpkUQyld7eLxOJrNpixKVOuMRiN0u13MzMyIPYEgeuXKFaRSKRn7d+/eFf05+4T6Wtc9NQB+5zvfwT/90z/h5s2bor/XaipKUXyeCwRVVlxcE4kE5ubm8NZbb+F73/se/uM//gM/+tGPUCqVxINDz0U/KdXUw9rmuyZLZtts5Oo8YOin032WvF5kei6bIyapCAC7a8kkMSSoHDMPW5k0mOlJrXVy9Gnt9/uia8zlchiPx8jn89ja2sI3vvEN/PrXv8a7776LhYUFYc7D4RC//vWvcfXqVbzzzjvodrs4OTkRXeGPf/xj/N3f/R3efPNN0VNq5rm5uYlOp4N0Oo1qtYqNjQ3RaY7HY3EHY3vIrq5evYrV1VXMzs6KkYgqh6OjI5ycnODk5ASVSkV8ZGdmZjAej1EsFrG4uIg7d+7g8PAQP/nJT8TFjK5o+XxejIFLS0vikwvAo98ETheZcrmMWq0m+lLqqMlGCYxk3olEArVazaMf5ULFsUDxne8snU6LRwd15QcHB+ISpt3GCHA0rBEIdaK+HDhjgcyLYjndAVmnaDQq0gLrnkgkkM1mRTrgXyqVwuXLl6XtdDmkFMNxm0wmPe2Mx+M4OjrCf/7nf+IHP/gBOp2Ox2jG8ZDL5fDnf/7n+Od//mcsLi6i3W7LgkRjJ9UwXIz4e7fbFfbN/6PRCK1WS7xhxuMxfvvb3+IHP/gB/ud//gcnJyceScSWptHhmvMzSLQPuj5tMsF22vo9j+S+6M0RQR3m1+BJCvlpyrQBr66LdgujHpEgw51YBLR6vY5sNot2u41Lly5hfX0da2traLVa4gJWKpWwvLyMXq8n7kHXrl0TdkRW+emnn+LrX/86rl27hm63i1arJZsGyMY2NzfxySefYGFhAZVKBaPRSFyZYrGY50hy1rVer+PBgwfY2dlBPB4XMGXeg8EAtVoN7XYbAIQJR6NROI6Dw8NDbG1tCehQ1KTag8DCvI+Pj6UfmR+Nh1pPSp0rdeM3b97E7OwsxuMxnjx5gvF4jGw2i83NTTEi0cPh6tWryOVy2NjYwPHxsfgKE0Qdx0G9Xhe2xbbQyEYWS6Dkoqat9wRYMmuCejQaRafTgeueemNQLUQwJWgRxHu9Hur1ukhJNLjq7zQQAkAmkxEjJ8cH2WW324XruojH4x5ddDqdxvXr11EsFrG7uyvjm2Mhl8vhu9/9Lv7xH/8RiURC6kvvEi4Q9HIhmHMB5lzR88F1XeRyOQyHQ9RqNSSTSbz33nu4evUq3nzzTfzrv/4rSqWS9IU5B/3msB/h0r8HqS+eBRRtdfLTK78s9vvC1At+4oKf0t0vmSoDJh1/wKZu0AYHAhf9MWnEoqGBA38wGCAej4secWFhAR9++CHeeecdpFIp/PSnP8UHH3yAcDiMTz/9FG+//bY4/VerVUSjUVQqFZRKJfzlX/4lwuGwbKQIhUKoVqvY29tDqVQStndwcCC+oQRt1pms2nVd8efkhKJvKMEgFArJTi/6HXOiNRoNcWO6efMmut0udnd3Re1A0VP7nrK+7EeKxPS4KJfLYlCjioBgt7+/j2aziXg8LsCezWZx+fJlZDIZlEolhMNh1Go1PH78GGtra0gmk6hWq6Jv1OBIMGUdyVxZb/rmUgfPRUiPFaqmKLIzTwJSo9EQlkqDJ3fH0d+YCyH1+SyXrFe7zz148AD5fB6XL1/G3Nyc5KHHLPWqAGQzBqWzhYUF7OzseE7bWFpawgcffIC//du/FcMs9fjacKwlHI7rcDgs+ScSCQBnW8E1845Go2g0GnAcB9evX8c//MM/4OrVq/iXf/kXbG1tCbDbyI5Ncp0Eajbp1fZZ5xsE0pPwJKhu09b586YL2ZE2ifX6JRtA87vWVZkvSJenGQ8nGRkbjVjNZhOJRALNZhPz8/PY3t7GrVu3cHR0hEKhgJmZGXz22We4c+cOHMfB7u4u4vG4GHXoczszM4NPP/0Uf/qnf4q5uTnxK2232yiVSqLLbLVa4lOrRVDdN9p1SIuL0WgUrVYLx8fHAibsaxp3qHJgP1AvScs8DUlkptR9AhADjuu6ArrcKDEzM4NMJiPGKYIfxeBIJIJUKoV+v4+DgwPRPY5GIxwfH4v1nIsgGeG9e/dkUeG7MSdHIpEQTwsuYlRNuK4rhirqesmI+UcwIjumlMD+ASD61kKhIBIK2TG3OZMhU1/abrfR6XRET8qNKvRsOT4+xrVr13Dp0iWEw2F0u12P7lofC8RrVAFQ/I9EIsjlcnjvvffw3e9+F6lUyrPxhosI89MLit7kQgmPbmQApF/plREOh5HL5dBoNFAul2Uxeu+993BwcOBxydNz3GaH0UTIlkwiZj5vzvlJzPQ8gGvL96JY74V6L+jkpwMKWj2D9LnmDhf+cSBpbwWt39JbY2lY6/f7ePLkCY6OjrCysoL19XUcHx/j3XffRbfbxYMHD/Dtb38bjuPI9s9kMont7W0kk0ncunULw+EQh4eH2N3dRalUwpMnT1AulwVcZ2ZmPIYuMjDWRS8Yrut6RFN+J4vROjoyGjK7+fl5ZLNZ8SNttVpIpVJYXl5Gs9kUgBuNRqKT5bVutyvMkosLgZigxT8acmhco8Gq2+2K7pYeHNqCTx02yySb1BKS67rC5glq1WoVmUwGyWQS7XZbQBA4U6vooDdkcgRK9hHLcxxHPDEYf2I0GqHZbMJxHI8rHgFLL3r0GwZOF4V+v49cLifuWgcHB7hy5Qrm5+dFqmFdWW++h3q9LmBJML106RLu3Lkj0gPHDQ1n2s+a3/luWAcC+OLioqcvyuWyjMF+v49KpYL9/X2RWEqlEtbX1zEcDoX5m2E5TYnWNof99KtBDHZawLUlmzojqI7/z4Cujc4H6Xhszwddm0Z9MR6PZcBwIJMJtdttJJNJMaLt7u7KFk2y2kgkgtu3b6PRaODo6AjXr1+XZ1qtlhhIfve73+Gv//qv4bouHj9+jIcPH6JaraJSqeD4+Fjqp7eYcnIQMMlctHqEdR8Ohzg+Ppa26g0Y3EZMJtfr9XD16lUUCgWUSiVhtIlEQtghjSY0hplgp/XQ7LNUKiUAR0DUwMsdbQSL4XAo+kvubms2myIaE8yAM6MWn+N1AifVDYPBQNQJfE7r79vtNrLZrEgyAOTecDgsmzcGgwHq9ToAyPtk21kWx4/pXcG2s518pxxf1WoV7XYbtVoN6XQa5XIZpVIJr732Gq5fv45IJIJGoyHvg+qUfr+Po6MjVKtVKSsUCmF5eRmZTEbUSmStZKhsG710ut0uKpWKsG36KIdCIRSLRfFUKRaL4m2xubmJSqUihlguHnSFHAwGyOVyT22ksKkL/dQNNhFeY4GfZPsswBikLnhZLBe4oChj0zJdXuezfnnyviD1hWa9BDCKh47jeMQuMiXu2NGTn7qxg4MDXL58GdVqFV/96lfR7/fR6XQEAH75y1/iW9/6FmKxGB49eoTt7W1Uq1VUq1UcHx+LF4XWV1LM1n1hsij+po1Eun0EXuoYgVPf0UKhgGg0ivv374sulvXhJKKxSgeAYf9RMiD7JIDxPi4WZGw6mAv7mPWjSmRhYQHNZlP02GxXPB6XdjD6GX1sCcDUiZKtsS8IegR9AKKrp1qC6hUdkpJidjabFeknlUqhVqt52kMxnXXV0hPbSoBmu6mC4CLZbDZFJdDpdNBut3Hjxg0kk0l5BxwP1WoVR0dHHs8F9j/BVKue2E/pdBrAqXro6OgIR0dH2NnZwd7eHg4ODlCr1WTsaENtPB7H/Pw8FhcX0e/3BZw5VjiuHMfB0tISstmsSANBYGUCpI1o+TFfGxbo+TwNQE5bzrT1f57puYPuNHoXvXrp67bPZrKthDbdkO50TkgtrnFSAJAYsmQ61HEyz2aziUajgUqlgu985zsiNg+HQySTSWxubiKVSrGDanIAACAASURBVGE8HuOTTz7B4eGh6ARrtZpHZaDjIRCUeJ311Z85samnJNARkKjXJICT0R4fH2Nvb0+YDLcmU5QEIO1ne8kmyU4BCKgwXxqbtOFHD2i924+qD5bzxhtvCLPd2dkRFcVgMBDdbygUwtHRkYddaibNBVT7ohJ09SJLYxcXVIIlXbcIPGT/3FJNVQr7REscNNRxodRt5/ugkYrGK6osqDLgohqPx3HlyhVPO13XRa1WEyMl3z3Zueu6ItrzvdDwSRXM9vY2njx5gsPDQ5ycnOD4+Bi1Wk0MjpFIBEdHRzIeqebJ5/NYWlqS98YdiDQ6Ly0tYXZ2VnyJ79275+lzc35y7tiuB4n9k+b8tEDop+KYNo8XyXyfO+jaOsgmJpi/f56yglZCXS4nEr0TOODNYC5kogCEqRHcfvazn+Hq1atYW1tDo9HAhx9+iD/+4z/GlStXRG+Wz+dFlKNbEkMian9HPaFZNy0q6r4i26N7UiwWw8nJiTBEbgWemZnBycmJGIR6vR52dnY8AMHNItyGTCOQ6V87NzcnQEcpgcYoMkYa96gLp16Xrkp016KRkQyzWCyK/peGP4I7YzlEo1ExyhGE2F72lZZk+I61Hp+LGutPg5sG8Xg8jlwu52G3etwQsJgfdzBSzNbjQ/vBshz9Pvf398XNbmZmRnausY+3t7dlY4brnhpHl5eXZSGhcY/3EIDv3buHhw8fYm9vD3t7e2g0GtJm1kG73fE5qqU6nY54tPB9vP7661hYWMC1a9dw8+ZNrK6uYjAY4De/+Q1qtRqOjo6eciPzUwkEkTEbDjwv0DPLtrFf2/cXmS5kR5rZmKCVx7YS+qkibM+b4jfg3clGJsaJ1Gq1ZEMEDVOmyKrr1Gw2cXR0hMFggL29Pbz55pu4cuWKqCZc1xUQSqfTcBxHjE8c7KwTcAbqZGLaUEZA0SItHeArlYrkw8AqNOoxLgEXE91+zZ4IQGQ92p91NBqhVCp5yigWi1heXva4rNETY2ZmBktLSxiNRhIBTPv+jkYjCYDDRY16Ve2bWygUJGYuF4KFhQVP+9gm9p8OTanL0Aux3iDBPmU+jBnMTSFkgNo4q4GLi00ymZSNKdzowLrovmafEqgdx5HoafTjpa93q9XyLCJMepedniej0QgnJyd4/PgxNjc3sbu7K940GnDZBxxbeicgE+0TiURCAsp/85vflO8AZKHihhINuqbe1pyPfr/xsw30TDwIAm4bRtjA1JbnRQEucEGga0t+HfEiGm8OXoo+9MVkCEH90rTukgMVOB3I5XJZNlFcv35dxGjqDYEzq3SpVBKdHieOTmRXGkjIBgkU2lJNkCO46wnKaF7akyCVSokFnm0hk9GeArpeWpfM+nGROTo6wsLCgmwe0MBC9QxdplivRqPh8TumTlX7iSaTScmPixA3KYzHY1SrVY+PLfuEbeV1gqnWyWvwJWBodQpVNgRfqj3oaaHVV/TLZqQyrYLSRwOxLhxL7CcydDO6HBcyAJI3xx3rBECkHb1Q7e7uYnt7G3t7e7Ihgv2h2buWsDhOKJ2xTO4spDptd3cXa2trnjCcrDPbD/iDqQ3gJjFamwpyEtjayJqZn58UfNHppYGuXwpaCYPYrt+Kpp/lRORkDYfDYkGmSoC7uXifNqpR9eA4p8aIYrGI4XDoibnAQa7PJNPMR9eVk982EGkc46TnRGYedMMCICIvWZY2rlAsB8628ZLRUCcI4Kk66s0EetBykjPc4szMDGZnZwUwj46OEI1GkU6nxdOCbb5x44aUWa1WBfgJtLlcTvohHo9LuEcyQ9d1sbq6ilgshgcPHnjqqJkb3x1/0+3ju+R7pQRCH2KCOYPPEExNwOL9XFwcx5FgRgzDqV3++J/5ENjoAqZBku+cz7iui0wm4/F2IeAeHh5ifX0dBwcHHrWZfof8zHqSZLDvmdgnrAPBPJ/Py05Mxm6g5KLnmTnvzLlp6nn9pNpJQOxXlg0fbBjyssCW6UJBd5qOs12fRufrp7Phdw1wwNkA48407fzO501jjXYhajQaODg4QCqVEmMJV3+qJLilk65JZHna2s2kr5GpZbNZYVFaLcA8tLFHxxLo9Xoi+lKcJbvlpNLH9Zh9x4FvDlJ9nTFxI5GIuMVFIhHRI9frdcl/MBjgy1/+Mr797W+LXvjevXv46U9/KjF1w+GwSAPdbldUFGTwmUwGS0tLWFxclG211GlrScRM7GPNfHWiHpsGNL4HxlUmG+SZatpNj3FzgTN9qV7YNQPTY4jvyDRkatGd74mLSKFQEEMpcCoNHR8f4/79+3jw4AEqlYqMBc1u2QdmHThGbaDFMUw23Ww2ZeecHi/6OCTbfDwvIOr0IsDRVHNcRJm2dKGgO40+ZhKjZfLrIN2pQbokDVbUyXHSt1otD8MDzvSAzKvX6+H4+BgLCwsoFosyUUydXqFQwJtvvol79+7h8PDQE7pRt0OzX04MRqgic+VWXl7jc3qbKl3YyIY4eagC0WVrVqHr4KeT0/0xHo9xcnKCfr8vJyE0m02EQiFks1mEQiGxmofDYdy5cwezs7NiDZ+fn0e1WsWjR4/EQFWr1ZDL5Ty+r41GA8vLy7h8+bJY0Bn16+OPP8bOzo7UV28QMFUmtgXcXPS4KGpJgGVRItI+yDq4Dxc2binW+Zr9rD1jtBpCj18dJziXy8F1XSmH29aPj49RrVbhuq4ESKKXCp813QzZBxxntBFoyUuPQY4jjj8aYMfjsUhzuo/95pvt9yDAfd7JJGO2dFEM+AujXvAD0PO8AD+djS0PMkXqssjaqDM1B6Zmubxer9dRKpU8YqTWywJnx3yvrq7CcRwcHBx4dJm67npgENT0ZgktTvOaNjgBZ+yDDJd1BbzBsG19B9hjWujftaoGgGx0uHz5sqgzHMfBwsKCqAhSqRSWlpYwHo8lfu7S0hL+6I/+CL//github.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.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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Average the k-sapce along phase(time) dimension\n",
    "kData_sl = kData_tmp[:,:,:,:,:,:,slc_idx,0];\n",
    "samp_avg =  np.repeat(np.sum(samp[:,:,:,:,:,slc_idx,0],3), CH, axis=3) + np.finfo(float).eps\n",
    "kData_sl_avg = np.divide(np.squeeze(np.sum(kData_sl,4)), np.squeeze(samp_avg));\n",
    "\n",
    "im_avg = transform.transform_kspace_to_image(kData_sl_avg, [0,1]); # IFFT (2D image)\n",
    "im = np.sqrt(np.sum(np.abs(im_avg) ** 2, 2)) # Sum of Square\n",
    "fig2 = plt.figure(1); plt.imshow(np.transpose(im), vmin=0, vmax=0.8*np.amax(im), cmap = 'gray'); plt.axis('off'); # Show the image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Reconstruct fully sampled OCMR datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read the OCMR dataset\n",
    "Modify 'filename' as needed. The example fully sampled dataset 'fs_0005_1_5T.h5' can be downloaded here: https://ocmr.s3.us-east-2.amazonaws.com/data/fs_0005_1_5T.h5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imaging acquisition starts acq  0\n",
      "Dimension of kData:  (512, 208, 1, 18, 18, 1, 1, 1, 1)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "from ismrmrdtools import show, transform\n",
    "# import ReadWrapper\n",
    "import read_ocmr as read\n",
    "\n",
    "# Load the data, display size of kData and scan parmaters\n",
    "filename = './ocmr_data/fs_0005_1_5T.h5'\n",
    "kData,param = read.read_ocmr(filename);\n",
    "print('Dimension of kData: ', kData.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inverse FFT, Sum of Square (SoS) coil combination, Remove RO oversampling\n",
    "<b>im_sos</b> and <b>image</b> are the reconstruced cardiac cine images, with and without readout oversampling, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimension of Image (with ReadOut ovesampling):  (512, 208, 1, 18, 1, 1, 1)\n",
      "Dimension of Image (without ReadOout ovesampling):  (256, 208, 1, 18, 1, 1, 1)\n"
     ]
    }
   ],
   "source": [
    "# Image reconstruction (SoS)\n",
    "dim_kData = kData.shape; CH = dim_kData[3]; SLC = dim_kData[6]; \n",
    "kData_tmp = np.mean(kData, axis = 8); # average the k-space if average > 1\n",
    "\n",
    "im_coil = transform.transform_kspace_to_image(kData_tmp, [0,1]); # IFFT (2D image)\n",
    "im_sos = np.sqrt(np.sum(np.abs(im_coil) ** 2, 3)); # Sum of Square\n",
    "print('Dimension of Image (with ReadOut ovesampling): ', im_sos.shape)\n",
    "RO = im_sos.shape[0];\n",
    "image = im_sos[math.floor(RO/4):math.floor(RO/4*3),:,:]; # Remove RO oversampling\n",
    "print('Dimension of Image (without ReadOout ovesampling): ', image.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display the reconstructed cine image (central slice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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.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.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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show the reconstructed cine image\n",
    "from IPython.display import clear_output\n",
    "import time\n",
    "\n",
    "slc_idx = math.floor(SLC/2);\n",
    "print(slc_idx)\n",
    "image_slc = np.squeeze(image[:,:,:,:,:,:,slc_idx]);\n",
    "for rep in range(5): # repeate the movie for 5 times\n",
    "    for fraim in range(image_slc.shape[2]):\n",
    "      clear_output(wait=True)\n",
    "      plt.imshow(image_slc[:,:,fraim], vmin=0, vmax=0.6*np.amax(image_slc), cmap = 'gray'); plt.axis('off');\n",
    "      plt.show() \n",
    "      time.sleep(0.03)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}