notebooks/MR/a_fully_sampled.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MR reconstruction of fully sampled k-space data\n",
    "\n",
    "This demonstration shows how to reconstruct MR images from fully sampled \n",
    "Cartesian k-space data.\n",
    "Additional information on the MR raw data format is provided.\n",
    "\n",
    "This demo is a 'script', i.e. intended to be run step by step in a\n",
    "Python notebook such as Jupyter. It is organised in 'cells'. Jupyter displays these\n",
    "cells nicely and allows you to run each cell on its own."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First version: 27th of May 2017  \n",
    "Updated: 26nd of June 2021  \n",
    "Author: Christoph Kolbitsch, Johannes Mayer\n",
    "\n",
    "CCP SyneRBI Synergistic Image Reconstruction Framework (SIRF).  \n",
    "Copyright 2015 - 2017 Rutherford Appleton Laboratory STFC.  \n",
    "Copyright 2015 - 2017 University College London.  \n",
    "Copyright 2015 - 2017, 2019, 2021 Physikalisch-Technische Bundesanstalt.\n",
    "\n",
    "This is software developed for the Collaborative Computational\n",
    "Project in Positron Emission Tomography and Magnetic Resonance imaging\n",
    "(http://www.ccppetmr.ac.uk/).\n",
    "\n",
    "SPDX-License-Identifier: Apache-2.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fully Sampled Reconstruction\n",
    "#### Goals of this notebook:\n",
    "- Make sure everybody has a working version of SIRF.\n",
    "- Achieve mastery in running Jupyter notebookes.\n",
    "- Schematic knowledge of ISMRMRD format.\n",
    "- Schematic knowledge of MR reconstruction with SIRF.\n",
    "- Minimalistic introduction into Python.\n",
    "- Reconstruct our first MR image.\n"
   ]
  },
  {
   "attachments": {
    "what_is_sirf_baby_dont_hurt_me.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Synergistic Image Reconstruction Framework (SIRF) for MRI\n",
    "Now to the question everybody has been asking themself: __[What is SIRF?](https://www.youtube.com/watch?v=HEXWRTEbj1I])__  \n",
    "The __Synergistic Image Reconstruction Framework__ is:\n",
    "- an open source reconstruction framework for PET-MR.\n",
    "- has a Matlab and Python interface.\n",
    "- coupled to the MR open source reconstruction engine '__The Gadgetron__' (a whole universe on its own).\n",
    "\n",
    "Of course, nobody expects you to already know any of the functions and classes we will see today.\n",
    "If you want to use SIRF there is a [software documentation](https://github.com/CCPPETMR/SIRF/wiki/Software-Documentation) where there is a complete list of what you want to know. More on this in a bit.\n",
    "\n",
    "\n",
    "![what_is_sirf_baby_dont_hurt_me.png](attachment:what_is_sirf_baby_dont_hurt_me.png)\n",
    "\n",
    "### Reasons to use an open-source reconstruction framework:\n",
    "- be __independent__ of what your vendor provides as image output.\n",
    "- __profit from__ the latest developments in the field which are often shared.\n",
    "- __share data__ with others more easily.\n",
    "- __others will continue to use your work!__\n",
    "\n",
    "\n",
    "#### We will use SIRF to \n",
    "- send data to The Gadgetron and get reconstructions back.\n",
    "- extract MRI data and process it on our own using SIRF functionality."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hence we need to start The Gadgetron!\n",
    "\n",
    "If you are using Jupyter from a Docker service container, gadgetron is already running in the background and does not need starting. Please skip this step.\n",
    "\n",
    "Open a new terminal. On Linux etc you can do this with `Ctrl + Alt + t`. If you use the jupyter notebook, use `New->terminal` from its home screen.\n",
    "Type the word `gadgetron` and press enter.\n",
    "Afterwards come back here to the notebook.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ISMRM Raw Data (ISMRMRD) Format\n",
    "\n",
    "MR raw- (or acquisition-) data is stored in a multitude of different data formats depending on the system vendor ( Siemens, GE, Philips, Bruker etc.). However, there is a community-supported, open-source magnetric resonance raw data or acquisition data format called \"ISMRMRD\" which carries the extension \".h5\".\n",
    "\n",
    "A wholesome explanation and introduction into the file format is provided on the [ISMRMRD website](http://ismrmrd.github.io/).\n",
    "\n",
    "Of course to make the file format feasible, MR raw data formats from different vendors can be transformed to the ISMRMRD file format using siemens_to_ismrmrd, philips_to_ismrmrd or bruker_to_ismrmrd available on the [ISMRMRD Github]( https://github.com/ismrmrd/ ).\n",
    "\n"
   ]
  },
  {
   "attachments": {
    "format_scheme.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ISMRMRD Schematic Overview \n",
    "#### Raw and Metadata\n",
    "To have a self-contained acquisition data output from the scanner the files contain one data portion and one header portion.\n",
    "\n",
    "![format_scheme.png](attachment:format_scheme.png)\n",
    "![rawdata_details.png](attachment:rawdata_details.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash \n",
    "# Run this script to make sure the data is downloaded.\n",
    "bash ../../scripts/download_data.sh -m\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%% make sure figures appears inline and animations works\n",
    "%matplotlib widget\n",
    "\n",
    "# Setup the working directory for the notebook\n",
    "import notebook_setup\n",
    "from sirf_exercises import cd_to_working_dir\n",
    "cd_to_working_dir('MR', 'a_fully_sampled')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "__version__ = '0.1.0'\n",
    "\n",
    "# import engine module\n",
    "import sirf.Gadgetron as pMR\n",
    "from sirf.Utilities import examples_data_path\n",
    "\n",
    "# import further modules\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "from sirf_exercises import exercises_data_path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A note on Python programming \n",
    "\n",
    "After the above `import` statement, the functionalities of the Synergistic Image Reconstruction Framework (SIRF) on the MR side are available in the package `sirf.Gadgetron` which in the code we will refer to as `pMR`. So whenever we want to call anything from this package we must preceed the call with `pMR.` (the dot is important)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%% LOAD RAW MR DATA\n",
    "data_path = exercises_data_path('MR', 'PTB_ACRPhantom_GRAPPA')\n",
    "filename = os.path.join(data_path, 'ptb_resolutionphantom_fully_ismrmrd.h5')\n",
    "acq_data = pMR.AcquisitionData(filename)\n",
    "\n",
    "print(type(acq_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A second note on Python programming \n",
    "\n",
    "As you might be familiar with from other languages you can create objects in Python which cluster together data and functionality. This clustering is called a __class__ . \n",
    "\n",
    "In this case the object is a variable named `acq_data` and is of __class__ `AcquisitionData`. As you can see the prefix `pMR.` appears because `AcquisitionData` is defined in `pMR`.\n",
    "\n",
    "The object `acq_data` is  generated by calling the __constructor__ of the class. The constructor is the name of the class itself. As you can see the constructor of `AcquisitionData` takes an argument, namely a string, which is the name of the .h5 file with the acquisition data. \n",
    "\n",
    "__Please remember this syntax you will need it later in small programming tasks.__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing MR Acquisition Data \n",
    "\n",
    "Prior to image reconstruction several pre-processing steps must be undertaken.  \n",
    "These include\n",
    "- asymmetric echo compensation\n",
    "- noise decorrelation for multi-coil data\n",
    "- removal of oversampling along frequency encoding ( also called \"readout\" or \"kx\" -direction).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%% PRE-PROCESSING CELL\n",
    "# preprocessed_data = acq_data \n",
    "preprocessed_data = pMR.preprocess_acquisition_data(acq_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Yet another note on Python programming \n",
    "\n",
    "As you can see, we now used a function call from the `pMR` package to act on the object we created before.\n",
    "(Just as in many other programming languages) The syntax is of the form:\n",
    "```python\n",
    "output = function_name(argument)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%% SETUP MR RECONSTRUCTION\n",
    "# create a reconstruction object using 2D inverse Fourier transform \n",
    "recon = pMR.FullySampledReconstructor()\n",
    "\n",
    "#%% PASS RAW DATA TO RECONSTRUCTOR\n",
    "recon.set_input(preprocessed_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A last note on Python programming\n",
    "The last two lines of code contained the syntax of how to call the functionality of an object.  \n",
    "We see that a variable called `recon` is created and assigned the call to the constructor of the class `FullySampledReconstructor`. This constructor does not take any argument.  \n",
    "\n",
    "Since classes cluster together data and the functionality of how to act on the data, this functionality must be accessible somehow. The functions which are associated to a class are called __methods__. \n",
    "You can call them by writing:\n",
    "```python\n",
    "output = variable_name.method_name(arguments)\n",
    "```\n",
    "The object `recon` possesses the __method__ `set_input` which takes as argument `AcquisitionData`. It does not return anything.  \n",
    "__Please remember how to call methods of an object, you will need it later on.__\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Yeah, but now what?\n",
    "We mentioned software documentation earlier. Since nobody knows what functionality these objects have we can call for help!\n",
    "\n",
    "#### Programming exercise\n",
    "Please write the following code in the next cell:\n",
    "- Call the built-in `help()` function with the argument `recon` to view the documentation of `FullySampledReconstructor`.\n",
    "\n",
    "Afterwards run the cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# write your code here and run the cell\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So now we see that there is a list of methods we can use. We used `set_input()` already. Since we already set the input we will use `process()` next.  \n",
    "\n",
    "### Programming task\n",
    "Please write code executing the following task:\n",
    "- call the `process()` method of the variable `recon`.  \n",
    "__Hint:__ Don't forget empty parentheses if you don't pass any arguments to signalize Python you are calling a function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write your code here and run the cell.\n",
    "# It is just a one-liner!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%% SOLUTION CELL: DON'T VIEW UNLESS YOU TRIED!\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# as promised just one single line.\n",
    "# calling the method using the dot and empty parentheses since we don't supply arguments.\n",
    "recon.process()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%% RETRIEVE AND VIEW IMAGE\n",
    "image_data = recon.get_output()\n",
    "image_array = abs(image_data.as_array())\n",
    "image_array = image_array/image_array.max()\n",
    "\n",
    "plt.figure(1)\n",
    "plt.set_cmap('gray')\n",
    "plt.imshow(image_array[0,:,:], vmin=0, vmax=0.7)\n",
    "plt.title('Reconstructed image data (magnitude)') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recap\n",
    "#### We just learned how\n",
    "- to run a Jupyter notebook.\n",
    "- data is stored in the ISMRMRD format.\n",
    "- to call Python functionality from the pMR (sirf.Gadgtetron) package.\n",
    "- wrote our first lines of code! Bam!\n",
    "- to reconstruct fully sampled MR kspace data using SIRF.\n"
   ]
  }
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