{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Between-volumes Motion Correction on DWI datasets\n\n## Overview\nDuring a dMRI acquisition, the subject motion inevitable. This motion implies\na misalignment between N volumes on a dMRI dataset. A common way to solve this\nissue is to perform a registration on each acquired volume to a\nreference b = 0. [JenkinsonSmith01]_\n\nThis preprocessing is an highly recommended step that should be executed before\nany dMRI dataset analysis.\n\n\nLet's import some essential functions.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "from dipy.align import motion_correction\nfrom dipy.core.gradients import gradient_table\nfrom dipy.data import get_fnames\nfrom dipy.io.image import load_nifti, save_nifti\nfrom dipy.io.gradients import read_bvals_bvecs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We choose one of the data from the datasets in dipy_. However, you can\nreplace the following line with the path of your image.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "dwi_fname, dwi_bval_fname, dwi_bvec_fname = get_fnames('sherbrooke_3shell')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We load the image and the affine of the image. The affine is the transformation\nmatrix which maps image coordinates to world (mm) coordinates. We also load the\nb-values and b-vectors.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "data, affine = load_nifti(dwi_fname)\nbvals, bvecs = read_bvals_bvecs(dwi_bval_fname, dwi_bvec_fname)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "This data has 193 volumes. For this demo purpose, we decide to reduce the\nnumber of volumes to 5. However, we do not recommended to perform a motion\ncorrection with less than 10 volumes.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "data_small = data[..., :3]\nbvals_small = bvals[:3]\nbvecs_small = bvecs[:3]\ngtab = gradient_table(bvals_small, bvecs_small)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Start motion correction of our reduced DWI dataset(between-volumes motion\ncorrection).\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "data_corrected, reg_affines = motion_correction(data_small, gtab, affine)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Save our DWI dataset corrected to a new Nifti file.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "save_nifti('motion_correction.nii.gz', data_corrected.get_fdata(),\n           data_corrected.affine)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## References\n\n.. [JenkinsonSmith01] Jenkinson, M., Smith, S., 2001. A global optimisation\n   method for robust affine registration of brain images. Med Image Anal 5\n   (2), 143\u201356.\n\n.. include:: ../links_names.inc\n\n"
      ]
    }
  ],
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      "display_name": "Python 3",
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      "file_extension": ".py",
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