colab-imoco-demo.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled0.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Phillistan16/imoco_recon/blob/colab_demo/colab-imoco-demo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "REKipt04s_R_",
        "colab_type": "text"
      },
      "source": [
        "# iterative Motion Compensation (iMoCo) Reconstruction Demo\n",
        "This CoLab notebook is a demo of the iMoCo method\n",
        "https://onlinelibrary.wiley.com/doi/full/10.1002/mrm.27998\n",
        "https://github.com/PulmonaryMRI/imoco_recon\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YGOlMnLD8oxb",
        "colab_type": "text"
      },
      "source": [
        "# Setup\n",
        "\n",
        "<!-- By default, this colab notebook should have a GPU runtime environment. To check this, go to **Runtime > Change runtime type** and see if **GPU** is selected as **Hardware accelerator**. -->\n",
        "\n",
        "To setup, we will install [SigPy](http://sigpy.readthedocs.io), [ANTs](https://github.com/ANTsX/ANTs) and clone the iMoCo github repo.\n",
        "\n",
        "To activate GPU environment in Colab, go to Runtime -> Change Runtime -> GPU"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qbJolU3s8_d6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "2b13c541-2e83-4e6b-b78a-9e01083b8d55"
      },
      "source": [
        "# install dependent packages sigpy, antspy\n",
        "!pip install sigpy==0.1.16\n",
        "#!pip install antspyx # enable after moving from ANTs to [ANTsPy](https://github.com/ANTsX/ANTsPy)\n",
        "\n",
        "# clone imoco recon repo\n",
        "! git clone -b colab_demo https://github.com/PulmonaryMRI/imoco_recon"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting sigpy==0.1.16\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/6b/95/3b7e167811ceb2e65d9de3767f5999014e19c98b88dcd40476af666048c0/sigpy-0.1.16-py3-none-any.whl (84kB)\n",
            "\r\u001b[K     |███▉                            | 10kB 21.0MB/s eta 0:00:01\r\u001b[K     |███████▊                        | 20kB 2.1MB/s eta 0:00:01\r\u001b[K     |███████████▋                    | 30kB 2.7MB/s eta 0:00:01\r\u001b[K     |███████████████▌                | 40kB 2.0MB/s eta 0:00:01\r\u001b[K     |███████████████████▍            | 51kB 2.4MB/s eta 0:00:01\r\u001b[K     |███████████████████████▎        | 61kB 2.6MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▏    | 71kB 3.0MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████ | 81kB 3.3MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 92kB 2.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from sigpy==0.1.16) (4.41.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from sigpy==0.1.16) (1.18.5)\n",
            "Requirement already satisfied: numba in /usr/local/lib/python3.6/dist-packages (from sigpy==0.1.16) (0.48.0)\n",
            "Requirement already satisfied: pywavelets in /usr/local/lib/python3.6/dist-packages (from sigpy==0.1.16) (1.1.1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from sigpy==0.1.16) (1.4.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from numba->sigpy==0.1.16) (49.6.0)\n",
            "Requirement already satisfied: llvmlite<0.32.0,>=0.31.0dev0 in /usr/local/lib/python3.6/dist-packages (from numba->sigpy==0.1.16) (0.31.0)\n",
            "Installing collected packages: sigpy\n",
            "Successfully installed sigpy-0.1.16\n",
            "Cloning into 'imoco_recon'...\n",
            "remote: Enumerating objects: 149, done.\u001b[K\n",
            "remote: Counting objects: 100% (149/149), done.\u001b[K\n",
            "remote: Compressing objects: 100% (113/113), done.\u001b[K\n",
            "remote: Total 149 (delta 55), reused 106 (delta 30), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (149/149), 206.52 KiB | 3.38 MiB/s, done.\n",
            "Resolving deltas: 100% (55/55), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5VsbK_UjLlq4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# install ANTs\n",
        "!git clone https://github.com/ANTsX/ANTs.git\n",
        "!mkdir build install\n",
        "%cd build\n",
        "!cmake /content/ANTs\n",
        "!make -j 2 2>&1\n",
        "%cd ANTS-build\n",
        "!make install 2>&1\n",
        "import os\n",
        "os.environ['PATH'] += \":/opt/ANTs/bin\"\n",
        "%cd ../.."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wVsgmnpg2Rma",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# import packages\n",
        "import imoco_recon.imoco_py.sigpy_e.cfl as cfl\n",
        "import numpy as np\n",
        "import sigpy.plot as pl"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oi3yyTTi8oxn",
        "colab_type": "text"
      },
      "source": [
        "# Download dataset\n",
        "\n",
        "Now, let's download a sample Lung UTE dataset. The dataset is hosted on Zenodo: https://zenodo.org/record/3733776#.XoJlNC2ZPOQ.\n",
        "\n",
        "<!-- The `download_lung_dataset()` function will download the datasets if it cannot find them under `data/lung/`. It takes around 8 minutes to download the datasets. -->"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mn1mqZ38YEPo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "3730169c-41b0-40a5-87c2-a5ce9e1c7ec5"
      },
      "source": [
        "!mkdir lung_mri\n",
        "!pip install zenodo-get\n",
        "!zenodo_get -d 10.5281/zenodo.3733775 -o ./lung_mri"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting zenodo-get\n",
            "  Downloading https://files.pythonhosted.org/packages/b1/93/7bb7fff9ee485dcd3fa679984c5cb06147fcc567930a3b3549d8053a2fe8/zenodo_get-1.3.2-py2.py3-none-any.whl\n",
            "Collecting wget\n",
            "  Downloading https://files.pythonhosted.org/packages/47/6a/62e288da7bcda82b935ff0c6cfe542970f04e29c756b0e147251b2fb251f/wget-3.2.zip\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from zenodo-get) (2.23.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->zenodo-get) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->zenodo-get) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->zenodo-get) (2020.6.20)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->zenodo-get) (3.0.4)\n",
            "Building wheels for collected packages: wget\n",
            "  Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for wget: filename=wget-3.2-cp36-none-any.whl size=9682 sha256=e48ea730abb1228c36bd707a08f237e40524099e29c230881869afb94cda8707\n",
            "  Stored in directory: /root/.cache/pip/wheels/40/15/30/7d8f7cea2902b4db79e3fea550d7d7b85ecb27ef992b618f3f\n",
            "Successfully built wget\n",
            "Installing collected packages: wget, zenodo-get\n",
            "Successfully installed wget-3.2 zenodo-get-1.3.2\n",
            "Title: Free Breathing Lung MRI Dataset at 3T\n",
            "Keywords: lung MRI, motion correction\n",
            "Publication date: 2020-03-30\n",
            "DOI: 10.5281/zenodo.3733776\n",
            "Total size: 4800.4 MB\n",
            "\n",
            "Link: https://zenodo.org/api/files/4e2fa077-5616-489c-b70f-3464b7f2ec44/cfl.py   size: 0.0 MB\n",
            "\n",
            "Checksum is correct. (54d868ad54b8a2b35606d5771f53c0e0)\n",
            "\n",
            "Link: https://zenodo.org/api/files/4e2fa077-5616-489c-b70f-3464b7f2ec44/MRI_Raw_datam.cfl   size: 3074.7 MB\n",
            "\n",
            "Checksum is correct. (ec2e8d5f885147873ce386d99dc90343)\n",
            "\n",
            "Link: https://zenodo.org/api/files/4e2fa077-5616-489c-b70f-3464b7f2ec44/MRI_Raw_datam.hdr   size: 0.0 MB\n",
            "\n",
            "Checksum is correct. (f2bf52eb84dd2bc8299c8f058306b765)\n",
            "\n",
            "Link: https://zenodo.org/api/files/4e2fa077-5616-489c-b70f-3464b7f2ec44/MRI_Raw_dcf2m.cfl   size: 384.3 MB\n",
            "\n",
            "Checksum is correct. (8de658fd47d671d0c2aa43b191920ea3)\n",
            "\n",
            "Link: https://zenodo.org/api/files/4e2fa077-5616-489c-b70f-3464b7f2ec44/MRI_Raw_dcf2m.hdr   size: 0.0 MB\n",
            "\n",
            "Checksum is correct. (e5b9482eb8bec83aa5ab3bd2a77a725c)\n",
            "\n",
            "Link: https://zenodo.org/api/files/4e2fa077-5616-489c-b70f-3464b7f2ec44/MRI_Raw_imoco.cfl   size: 188.3 MB\n",
            "\n",
            "Checksum is correct. (159ab4e01eca5fdf5a5882bf406d60a8)\n",
            "\n",
            "Link: https://zenodo.org/api/files/4e2fa077-5616-489c-b70f-3464b7f2ec44/MRI_Raw_imoco.hdr   size: 0.0 MB\n",
            "\n",
            "Checksum is correct. (3dd4add15fab2e5cf9828d63ca66337f)\n",
            "\n",
            "Link: https://zenodo.org/api/files/4e2fa077-5616-489c-b70f-3464b7f2ec44/MRI_Raw_trajm.cfl   size: 1153.0 MB\n",
            "\n",
            "Checksum is correct. (43f6821e1bfc82450266897670296b12)\n",
            "\n",
            "Link: https://zenodo.org/api/files/4e2fa077-5616-489c-b70f-3464b7f2ec44/MRI_Raw_trajm.hdr   size: 0.0 MB\n",
            "\n",
            "Checksum is correct. (e72dd67525d1169dc11919ad5afa9906)\n",
            "\n",
            "Link: https://zenodo.org/api/files/4e2fa077-5616-489c-b70f-3464b7f2ec44/readcfl.m   size: 0.0 MB\n",
            "\n",
            "Checksum is correct. (cd339c0ebb279f174e44026b05b0d1f2)\n",
            "\n",
            "Link: https://zenodo.org/api/files/4e2fa077-5616-489c-b70f-3464b7f2ec44/writecfl.m   size: 0.0 MB\n",
            "\n",
            "Checksum is correct. (00741deb8a416f79c6330ec8b95ce4a5)\n",
            "All files have been downloaded.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q3UOypnejXgN",
        "colab_type": "text"
      },
      "source": [
        "# Crop Data (Optional)\n",
        "decrease number of frequency encoding points to adapt for Colab RAM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YgQKP77Cdh_1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "num_ro = 200\n",
        "\n",
        "datam = cfl.read_cfl('./lung_mri/MRI_Raw_datam')\n",
        "datam = datam[:,:,:,:,:num_ro,:]\n",
        "cfl.write_cfl('./lung_mri/MRI_Raw_datam', datam)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GflI5joSej0M",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dcf2m = cfl.read_cfl('./lung_mri/MRI_Raw_dcf2m')\n",
        "dcf2m = dcf2m[:,:,:,:,:num_ro,:]\n",
        "cfl.write_cfl('./lung_mri/MRI_Raw_dcf2m', dcf2m)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2KJuLNcrexYz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "trajm = cfl.read_cfl('./lung_mri/MRI_Raw_trajm')\n",
        "trajm = trajm[:,:,:,:,:num_ro,:]\n",
        "cfl.write_cfl('./lung_mri/MRI_Raw_trajm', trajm)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S-WUQQJ2iTsN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# release RAM\n",
        "del datam\n",
        "del dcf2m\n",
        "del trajm"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XnZVUcLm2_kX",
        "colab_type": "text"
      },
      "source": [
        "# iMoCo Reconstruction\n",
        "\n",
        "## step 1. XD-GRASP, motion-resolved reconstruction \n",
        "## step 2. iMoCo reconstruction\n",
        "\n",
        "When running iMoCo for the first time, use flag --reg_flag 1, which generates the motion field needed for iMoCo reconstruction \n",
        "\n",
        "If the motion field exists already (e.g. from previous iMoco recon), no flag is needed"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bpJQxShIhivb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "e4266976-c203-4efb-ea47-7f9e1aca82c4"
      },
      "source": [
        "# xd-grasp recon\n",
        "!python imoco_recon/imoco_py/recon_xdgrasp.py ./lung_mri/MRI_Raw --device 0"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "JsenseRecon: 100% 10/10 [00:15<00:00,  1.52s/it]\n",
            "outer iter:0, res:1.0\n",
            "outer iter:1, res:0.6153555512428284\n",
            "outer iter:2, res:0.4152965843677521\n",
            "outer iter:3, res:0.2897503972053528\n",
            "outer iter:4, res:0.20213954150676727\n",
            "outer iter:5, res:0.13695521652698517\n",
            "outer iter:6, res:0.08666440844535828\n",
            "outer iter:7, res:0.04738427698612213\n",
            "outer iter:8, res:0.017541415989398956\n",
            "outer iter:9, res:0.010017949156463146\n",
            "outer iter:10, res:0.024471217766404152\n",
            "outer iter:11, res:0.03403307497501373\n",
            "outer iter:12, res:0.037935711443424225\n",
            "outer iter:13, res:0.03687838092446327\n",
            "outer iter:14, res:0.03199930116534233\n",
            "outer iter:15, res:0.024720804765820503\n",
            "outer iter:16, res:0.016543323174118996\n",
            "outer iter:17, res:0.008854111656546593\n",
            "outer iter:18, res:0.003330372041091323\n",
            "outer iter:19, res:0.004226641729474068\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BPRB7njKExJv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "3374223a-8405-41db-f04d-3f791f648cfa"
      },
      "source": [
        "# imoco recon with motion field derivation\n",
        "!python imoco_recon/imoco_py/recon_imoco.py ./lung_mri/MRI_Raw --reg_flag 1 --device 0"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Calibration...\n",
            "JsenseRecon: 100% 10/10 [00:11<00:00,  1.16s/it]\n",
            "Registration...\n",
            "All_Command_lines_OK\n",
            "Using double precision for computations.\n",
            "  number of levels = 3\n",
            "  fixed image: tmp_Im.nii\n",
            "  moving image: tmp_If.nii\n",
            "Dimension = 3\n",
            "Number of stages = 1\n",
            "Use Histogram Matching true\n",
            "Winsorize image intensities true\n",
            "Lower quantile = 0.1\n",
            "Upper quantile = 1\n",
            "Stage 1 State\n",
            "   Image metric = Demons\n",
            "     Fixed image = Image (0x55729c496000)\n",
            "  RTTI typeinfo:   itk::Image<double, 3u>\n",
            "  Reference Count: 2\n",
            "  Modified Time: 722\n",
            "  Debug: Off\n",
            "  Object Name: \n",
            "  Observers: \n",
            "    none\n",
            "  Source: (none)\n",
            "  Source output name: (none)\n",
            "  Release Data: Off\n",
            "  Data Released: False\n",
            "  Global Release Data: Off\n",
            "  PipelineMTime: 0\n",
            "  UpdateMTime: 508\n",
            "  RealTimeStamp: 0 seconds \n",
            "  LargestPossibleRegion: \n",
            "    Dimension: 3\n",
            "    Index: [0, 0, 0]\n",
            "    Size: [140, 93, 95]\n",
            "  BufferedRegion: \n",
            "    Dimension: 3\n",
            "    Index: [0, 0, 0]\n",
            "    Size: [140, 93, 95]\n",
            "  RequestedRegion: \n",
            "    Dimension: 3\n",
            "    Index: [0, 0, 0]\n",
            "    Size: [140, 93, 95]\n",
            "  Spacing: [1, 1, 1]\n",
            "  Origin: [0, 0, 0]\n",
            "  Direction: \n",
            "-1 0 0\n",
            "0 -1 0\n",
            "0 0 1\n",
            "\n",
            "  IndexToPointMatrix: \n",
            "-1 0 0\n",
            "0 -1 0\n",
            "0 0 1\n",
            "\n",
            "  PointToIndexMatrix: \n",
            "-1 0 0\n",
            "0 -1 0\n",
            "0 0 1\n",
            "\n",
            "  Inverse Direction: \n",
            "-1 0 0\n",
            "0 -1 0\n",
            "0 0 1\n",
            "\n",
            "  PixelContainer: \n",
            "    ImportImageContainer (0x55729bf8f5d0)\n",
            "      RTTI typeinfo:   itk::ImportImageContainer<unsigned long, double>\n",
            "      Reference Count: 1\n",
            "      Modified Time: 505\n",
            "      Debug: Off\n",
            "      Object Name: \n",
            "      Observers: \n",
            "        none\n",
            "      Pointer: 0x55729c4ac000\n",
            "      Container manages memory: true\n",
            "      Size: 1236900\n",
            "      Capacity: 1236900\n",
            "\n",
            "     Moving image = Image (0x55729c4962c0)\n",
            "  RTTI typeinfo:   itk::Image<double, 3u>\n",
            "  Reference Count: 2\n",
            "  Modified Time: 723\n",
            "  Debug: Off\n",
            "  Object Name: \n",
            "  Observers: \n",
            "    none\n",
            "  Source: (none)\n",
            "  Source output name: (none)\n",
            "  Release Data: Off\n",
            "  Data Released: False\n",
            "  Global Release Data: Off\n",
            "  PipelineMTime: 0\n",
            "  UpdateMTime: 720\n",
            "  RealTimeStamp: 0 seconds \n",
            "  LargestPossibleRegion: \n",
            "    Dimension: 3\n",
            "    Index: [0, 0, 0]\n",
            "    Size: [140, 93, 95]\n",
            "  BufferedRegion: \n",
            "    Dimension: 3\n",
            "    Index: [0, 0, 0]\n",
            "    Size: [140, 93, 95]\n",
            "  RequestedRegion: \n",
            "    Dimension: 3\n",
            "    Index: [0, 0, 0]\n",
            "    Size: [140, 93, 95]\n",
            "  Spacing: [1, 1, 1]\n",
            "  Origin: [0, 0, 0]\n",
            "  Direction: \n",
            "-1 0 0\n",
            "0 -1 0\n",
            "0 0 1\n",
            "\n",
            "  IndexToPointMatrix: \n",
            "-1 0 0\n",
            "0 -1 0\n",
            "0 0 1\n",
            "\n",
            "  PointToIndexMatrix: \n",
            "-1 0 0\n",
            "0 -1 0\n",
            "0 0 1\n",
            "\n",
            "  Inverse Direction: \n",
            "-1 0 0\n",
            "0 -1 0\n",
            "0 0 1\n",
            "\n",
            "  PixelContainer: \n",
            "    ImportImageContainer (0x55729bf8fb80)\n",
            "      RTTI typeinfo:   itk::ImportImageContainer<unsigned long, double>\n",
            "      Reference Count: 1\n",
            "      Modified Time: 717\n",
            "      Debug: Off\n",
            "      Object Name: \n",
            "      Observers: \n",
            "        none\n",
            "      Pointer: 0x55729ce1c000\n",
            "      Container manages memory: true\n",
            "      Size: 1236900\n",
            "      Capacity: 1236900\n",
            "\n",
            "     Weighting = 1\n",
            "     Sampling strategy = none\n",
            "     Number of bins = 32\n",
            "     Radius = 4\n",
            "     Sampling percentage  = 1\n",
            "   Transform = SyN\n",
            "     Gradient step = 0.1\n",
            "     Update field sigma (voxel space) = 5\n",
            "     Total field sigma (voxel space) = 3\n",
            "     Update field time sigma = 0\n",
            "     Total field time sigma  = 0\n",
            "     Number of time indices = 0\n",
            "     Number of time point samples = 0\n",
            "Registration using 1 total stages.\n",
            "\n",
            "Stage 0\n",
            "  iterations = 100x100x40\n",
            "  convergence threshold = 1e-06\n",
            "  convergence window size = 10\n",
            "  number of levels = 3\n",
            "  using the Demons metric (weight = 1)\n",
            "  preprocessing:  winsorizing the image intensities\n",
            "  preprocessing:  histogram matching the images\n",
            "  Shrink factors (level 1 out of 3): [8, 8, 8]\n",
            "  Shrink factors (level 2 out of 3): [4, 4, 4]\n",
            "  Shrink factors (level 3 out of 3): [2, 2, 2]\n",
            "  smoothing sigmas per level: [2, 2, 1]\n",
            "  Using default NONE metricSamplingStrategy \n",
            "\n",
            "*** Running SyN registration (varianceForUpdateField = 5, varianceForTotalField = 3) ***\n",
            "\n",
            "XXDIAGNOSTIC,Iteration,metricValue,convergenceValue,ITERATION_TIME_INDEX,SINCE_LAST\n",
            " 1DIAGNOSTIC,     1, 2.887525842113e-06, 1.797693134862e+308, 1.5065e-01, 1.5065e-01, \n",
            " 1DIAGNOSTIC,     2, 1.733459650851e-06, 1.797693134862e+308, 1.8404e-01, 3.3386e-02, \n",
            " 1DIAGNOSTIC,     3, 4.565126680607e-06, 1.797693134862e+308, 2.2488e-01, 4.0842e-02, \n",
            " 1DIAGNOSTIC,     4, 1.047017105755e-05, 1.797693134862e+308, 2.5924e-01, 3.4357e-02, \n",
            " 1DIAGNOSTIC,     5, 1.095354356219e-05, 1.797693134862e+308, 2.9616e-01, 3.6925e-02, \n",
            " 1DIAGNOSTIC,     6, 1.392934704703e-05, 1.797693134862e+308, 3.2848e-01, 3.2314e-02, \n",
            " 1DIAGNOSTIC,     7, 1.569578373651e-05, 1.797693134862e+308, 3.6000e-01, 3.1523e-02, \n",
            " 1DIAGNOSTIC,     8, 9.271296525865e-06, 1.797693134862e+308, 3.9240e-01, 3.2396e-02, \n",
            " 1DIAGNOSTIC,     9, 1.342023042700e-05, 1.797693134862e+308, 4.2487e-01, 3.2471e-02, \n",
            " 1DIAGNOSTIC,    10, 1.226306465505e-05, -7.709497397857e-02, 4.5713e-01, 3.2260e-02, \n",
            "XXDIAGNOSTIC,Iteration,metricValue,convergenceValue,ITERATION_TIME_INDEX,SINCE_LAST\n",
            " 1DIAGNOSTIC,     1, 2.325351697206e-05, 1.797693134862e+308, 7.4666e-01, 2.8954e-01, \n",
            " 1DIAGNOSTIC,     2, 3.824859521975e-06, 1.797693134862e+308, 9.2144e-01, 1.7477e-01, \n",
            " 1DIAGNOSTIC,     3, 1.803948364349e-06, 1.797693134862e+308, 1.0979e+00, 1.7650e-01, \n",
            " 1DIAGNOSTIC,     4, 1.952695268031e-06, 1.797693134862e+308, 1.2806e+00, 1.8268e-01, \n",
            " 1DIAGNOSTIC,     5, 1.675157437822e-06, 1.797693134862e+308, 1.4524e+00, 1.7174e-01, \n",
            " 1DIAGNOSTIC,     6, 1.901330772273e-06, 1.797693134862e+308, 1.6322e+00, 1.7985e-01, \n",
            " 1DIAGNOSTIC,     7, 1.525842929810e-06, 1.797693134862e+308, 1.8167e+00, 1.8454e-01, \n",
            " 1DIAGNOSTIC,     8, 2.202874032874e-06, 1.797693134862e+308, 1.9902e+00, 1.7342e-01, \n",
            " 1DIAGNOSTIC,     9, 1.430790112579e-06, 1.797693134862e+308, 2.1637e+00, 1.7350e-01, \n",
            " 1DIAGNOSTIC,    10, 2.404812923046e-06, 1.545195118559e-01, 2.3457e+00, 1.8204e-01, \n",
            " 1DIAGNOSTIC,    11, 1.433833657150e-06, 1.341037784623e-02, 2.5203e+00, 1.7458e-01, \n",
            " 1DIAGNOSTIC,    12, 2.387316099208e-06, -3.320727245941e-03, 2.7030e+00, 1.8272e-01, \n",
            "XXDIAGNOSTIC,Iteration,metricValue,convergenceValue,ITERATION_TIME_INDEX,SINCE_LAST\n",
            " 1DIAGNOSTIC,     1, 3.178610770222e-06, 1.797693134862e+308, 3.8092e+00, 1.1062e+00, \n",
            " 1DIAGNOSTIC,     2, 1.312963083776e-06, 1.797693134862e+308, 4.7938e+00, 9.8457e-01, \n",
            " 1DIAGNOSTIC,     3, 9.121545099232e-07, 1.797693134862e+308, 5.7582e+00, 9.6443e-01, \n",
            " 1DIAGNOSTIC,     4, 8.638371824821e-07, 1.797693134862e+308, 6.7613e+00, 1.0031e+00, \n",
            " 1DIAGNOSTIC,     5, 8.387137253920e-07, 1.797693134862e+308, 7.7163e+00, 9.5502e-01, \n",
            " 1DIAGNOSTIC,     6, 8.204328439696e-07, 1.797693134862e+308, 8.6749e+00, 9.5863e-01, \n",
            " 1DIAGNOSTIC,     7, 8.105585819377e-07, 1.797693134862e+308, 9.6371e+00, 9.6215e-01, \n",
            " 1DIAGNOSTIC,     8, 8.044385942575e-07, 1.797693134862e+308, 1.0617e+01, 9.7953e-01, \n",
            " 1DIAGNOSTIC,     9, 8.014959999725e-07, 1.797693134862e+308, 1.1581e+01, 9.6462e-01, \n",
            " 1DIAGNOSTIC,    10, 7.972874820075e-07, 7.478782869043e-02, 1.2544e+01, 9.6231e-01, \n",
            " 1DIAGNOSTIC,    11, 7.946326900708e-07, 1.619249001479e-02, 1.3497e+01, 9.5347e-01, \n",
            " 1DIAGNOSTIC,    12, 7.951782294154e-07, 4.828556537217e-03, 1.4469e+01, 9.7229e-01, \n",
            " 1DIAGNOSTIC,    13, 7.944290067584e-07, 2.776943610228e-03, 1.5434e+01, 9.6467e-01, \n",
            " 1DIAGNOSTIC,    14, 7.945708633459e-07, 1.644248204859e-03, 1.6401e+01, 9.6662e-01, \n",
            " 1DIAGNOSTIC,    15, 7.919883031182e-07, 9.918817319194e-04, 1.7378e+01, 9.7704e-01, \n",
            " 1DIAGNOSTIC,    16, 7.950443114473e-07, 5.790614790092e-04, 1.8348e+01, 9.7018e-01, \n",
            " 1DIAGNOSTIC,    17, 7.929179229017e-07, 3.564138016900e-04, 1.9319e+01, 9.7127e-01, \n",
            " 1DIAGNOSTIC,    18, 7.954347496973e-07, 1.781391212827e-04, 2.0273e+01, 9.5368e-01, \n",
            " 1DIAGNOSTIC,    19, 7.953103646909e-07, 4.529479659762e-05, 2.1229e+01, 9.5643e-01, \n",
            " 1DIAGNOSTIC,    20, 7.946043786180e-07, -9.176623795890e-06, 2.2210e+01, 9.8122e-01, \n",
            "  Elapsed time (stage 0): 22.5402\n",
            "\n",
            "\n",
            "Total elapsed time: 22.5427\n",
            "All_Command_lines_OK\n",
            "Using double precision for computations.\n",
            "  number of levels = 3\n",
            "  fixed image: tmp_Im.nii\n",
            "  moving image: tmp_If.nii\n",
            "Dimension = 3\n",
            "Number of stages = 1\n",
            "Use Histogram Matching true\n",
            "Winsorize image intensities true\n",
            "Lower quantile = 0.1\n",
            "Upper quantile = 1\n",
            "Stage 1 State\n",
            "   Image metric = Demons\n",
            "     Fixed image = Image (0x55875ca58000)\n",
            "  RTTI typeinfo:   itk::Image<double, 3u>\n",
            "  Reference Count: 2\n",
            "  Modified Time: 722\n",
            "  Debug: Off\n",
            "  Object Name: \n",
            "  Observers: \n",
            "    none\n",
            "  Source: (none)\n",
            "  Source output name: (none)\n",
            "  Release Data: Off\n",
            "  Data Released: False\n",
            "  Global Release Data: Off\n",
            "  PipelineMTime: 0\n",
            "  UpdateMTime: 508\n",
            "  RealTimeStamp: 0 seconds \n",
            "  LargestPossibleRegion: \n",
            "    Dimension: 3\n",
            "    Index: [0, 0, 0]\n",
            "    Size: [140, 93, 95]\n",
            "  BufferedRegion: \n",
            "    Dimension: 3\n",
            "    Index: [0, 0, 0]\n",
            "    Size: [140, 93, 95]\n",
            "  RequestedRegion: \n",
            "    Dimension: 3\n",
            "    Index: [0, 0, 0]\n",
            "    Size: [140, 93, 95]\n",
            "  Spacing: [1, 1, 1]\n",
            "  Origin: [0, 0, 0]\n",
            "  Direction: \n",
            "-1 0 0\n",
            "0 -1 0\n",
            "0 0 1\n",
            "\n",
            "  IndexToPointMatrix: \n",
            "-1 0 0\n",
            "0 -1 0\n",
            "0 0 1\n",
            "\n",
            "  PointToIndexMatrix: \n",
            "-1 0 0\n",
            "0 -1 0\n",
            "0 0 1\n",
            "\n",
            "  Inverse Direction: \n",
            "-1 0 0\n",
            "0 -1 0\n",
            "0 0 1\n",
            "\n",
            "  PixelContainer: \n",
            "    ImportImageContainer (0x55875c5515d0)\n",
            "      RTTI typeinfo:   itk::ImportImageContainer<unsigned long, double>\n",
            "      Reference Count: 1\n",
            "      Modified Time: 505\n",
            "      Debug: Off\n",
            "      Object Name: \n",
            "      Observers: \n",
            "        none\n",
            "      Pointer: 0x55875ca6e000\n",
            "      Container manages memory: true\n",
            "      Size: 1236900\n",
            "      Capacity: 1236900\n",
            "\n",
            "     Moving image = Image (0x55875ca582c0)\n",
            "  RTTI typeinfo:   itk::Image<double, 3u>\n",
            "  Reference Count: 2\n",
            "  Modified Time: 723\n",
            "  Debug: Off\n",
            "  Object Name: \n",
            "  Observers: \n",
            "    none\n",
            "  Source: (none)\n",
            "  Source output name: (none)\n",
            "  Release Data: Off\n",
            "  Data Released: False\n",
            "  Global Release Data: Off\n",
            "  PipelineMTime: 0\n",
            "  UpdateMTime: 720\n",
            "  RealTimeStamp: 0 seconds \n",
            "  LargestPossibleRegion: \n",
            "    Dimension: 3\n",
            "    Index: [0, 0, 0]\n",
            "    Size: [140, 93, 95]\n",
            "  BufferedRegion: \n",
            "    Dimension: 3\n",
            "    Index: [0, 0, 0]\n",
            "    Size: [140, 93, 95]\n",
            "  RequestedRegion: \n",
            "    Dimension: 3\n",
            "    Index: [0, 0, 0]\n",
            "    Size: [140, 93, 95]\n",
            "  Spacing: [1, 1, 1]\n",
            "  Origin: [0, 0, 0]\n",
            "  Direction: \n",
            "-1 0 0\n",
            "0 -1 0\n",
            "0 0 1\n",
            "\n",
            "  IndexToPointMatrix: \n",
            "-1 0 0\n",
            "0 -1 0\n",
            "0 0 1\n",
            "\n",
            "  PointToIndexMatrix: \n",
            "-1 0 0\n",
            "0 -1 0\n",
            "0 0 1\n",
            "\n",
            "  Inverse Direction: \n",
            "-1 0 0\n",
            "0 -1 0\n",
            "0 0 1\n",
            "\n",
            "  PixelContainer: \n",
            "    ImportImageContainer (0x55875c551b80)\n",
            "      RTTI typeinfo:   itk::ImportImageContainer<unsigned long, double>\n",
            "      Reference Count: 1\n",
            "      Modified Time: 717\n",
            "      Debug: Off\n",
            "      Object Name: \n",
            "      Observers: \n",
            "        none\n",
            "      Pointer: 0x55875d3de000\n",
            "      Container manages memory: true\n",
            "      Size: 1236900\n",
            "      Capacity: 1236900\n",
            "\n",
            "     Weighting = 1\n",
            "     Sampling strategy = none\n",
            "     Number of bins = 32\n",
            "     Radius = 4\n",
            "     Sampling percentage  = 1\n",
            "   Transform = SyN\n",
            "     Gradient step = 0.1\n",
            "     Update field sigma (voxel space) = 5\n",
            "     Total field sigma (voxel space) = 3\n",
            "     Update field time sigma = 0\n",
            "     Total field time sigma  = 0\n",
            "     Number of time indices = 0\n",
            "     Number of time point samples = 0\n",
            "Registration using 1 total stages.\n",
            "\n",
            "Stage 0\n",
            "  iterations = 100x100x40\n",
            "  convergence threshold = 1e-06\n",
            "  convergence window size = 10\n",
            "  number of levels = 3\n",
            "  using the Demons metric (weight = 1)\n",
            "  preprocessing:  winsorizing the image intensities\n",
            "  preprocessing:  histogram matching the images\n",
            "  Shrink factors (level 1 out of 3): [8, 8, 8]\n",
            "  Shrink factors (level 2 out of 3): [4, 4, 4]\n",
            "  Shrink factors (level 3 out of 3): [2, 2, 2]\n",
            "  smoothing sigmas per level: [2, 2, 1]\n",
            "  Using default NONE metricSamplingStrategy \n",
            "\n",
            "*** Running SyN registration (varianceForUpdateField = 5, varianceForTotalField = 3) ***\n",
            "\n",
            "XXDIAGNOSTIC,Iteration,metricValue,convergenceValue,ITERATION_TIME_INDEX,SINCE_LAST\n",
            " 1DIAGNOSTIC,     1, 0.000000000000e+00, 1.797693134862e+308, 1.5095e-01, 1.5095e-01, \n",
            " 1DIAGNOSTIC,     2, 0.000000000000e+00, 1.797693134862e+308, 1.8308e-01, 3.2131e-02, \n",
            " 1DIAGNOSTIC,     3, 0.000000000000e+00, 1.797693134862e+308, 2.2032e-01, 3.7238e-02, \n",
            " 1DIAGNOSTIC,     4, 0.000000000000e+00, 1.797693134862e+308, 2.5131e-01, 3.0989e-02, \n",
            " 1DIAGNOSTIC,     5, 0.000000000000e+00, 1.797693134862e+308, 2.8433e-01, 3.3014e-02, \n",
            " 1DIAGNOSTIC,     6, 0.000000000000e+00, 1.797693134862e+308, 3.1793e-01, 3.3606e-02, \n",
            " 1DIAGNOSTIC,     7, 0.000000000000e+00, 1.797693134862e+308, 3.5039e-01, 3.2457e-02, \n",
            " 1DIAGNOSTIC,     8, 0.000000000000e+00, 1.797693134862e+308, 3.8306e-01, 3.2672e-02, \n",
            " 1DIAGNOSTIC,     9, 0.000000000000e+00, 1.797693134862e+308, 4.1583e-01, 3.2764e-02, \n",
            " 1DIAGNOSTIC,    10, 0.000000000000e+00, -0.000000000000e+00, 4.4704e-01, 3.1211e-02, \n",
            "XXDIAGNOSTIC,Iteration,metricValue,convergenceValue,ITERATION_TIME_INDEX,SINCE_LAST\n",
            " 1DIAGNOSTIC,     1, 0.000000000000e+00, 1.797693134862e+308, 7.2759e-01, 2.8055e-01, \n",
            " 1DIAGNOSTIC,     2, 0.000000000000e+00, 1.797693134862e+308, 9.0450e-01, 1.7691e-01, \n",
            " 1DIAGNOSTIC,     3, 0.000000000000e+00, 1.797693134862e+308, 1.0849e+00, 1.8039e-01, \n",
            " 1DIAGNOSTIC,     4, 0.000000000000e+00, 1.797693134862e+308, 1.2555e+00, 1.7061e-01, \n",
            " 1DIAGNOSTIC,     5, 0.000000000000e+00, 1.797693134862e+308, 1.4288e+00, 1.7330e-01, \n",
            " 1DIAGNOSTIC,     6, 0.000000000000e+00, 1.797693134862e+308, 1.6037e+00, 1.7489e-01, \n",
            " 1DIAGNOSTIC,     7, 0.000000000000e+00, 1.797693134862e+308, 1.7731e+00, 1.6937e-01, \n",
            " 1DIAGNOSTIC,     8, 0.000000000000e+00, 1.797693134862e+308, 1.9669e+00, 1.9388e-01, \n",
            " 1DIAGNOSTIC,     9, 0.000000000000e+00, 1.797693134862e+308, 2.1479e+00, 1.8093e-01, \n",
            " 1DIAGNOSTIC,    10, 0.000000000000e+00, -0.000000000000e+00, 2.3206e+00, 1.7272e-01, \n",
            "XXDIAGNOSTIC,Iteration,metricValue,convergenceValue,ITERATION_TIME_INDEX,SINCE_LAST\n",
            " 1DIAGNOSTIC,     1, 0.000000000000e+00, 1.797693134862e+308, 3.3989e+00, 1.0783e+00, \n",
            " 1DIAGNOSTIC,     2, 0.000000000000e+00, 1.797693134862e+308, 4.3410e+00, 9.4211e-01, \n",
            " 1DIAGNOSTIC,     3, 0.000000000000e+00, 1.797693134862e+308, 5.2703e+00, 9.2936e-01, \n",
            " 1DIAGNOSTIC,     4, 0.000000000000e+00, 1.797693134862e+308, 6.1999e+00, 9.2956e-01, \n",
            " 1DIAGNOSTIC,     5, 0.000000000000e+00, 1.797693134862e+308, 7.1452e+00, 9.4527e-01, \n",
            " 1DIAGNOSTIC,     6, 0.000000000000e+00, 1.797693134862e+308, 8.0753e+00, 9.3018e-01, \n",
            " 1DIAGNOSTIC,     7, 0.000000000000e+00, 1.797693134862e+308, 8.9998e+00, 9.2449e-01, \n",
            " 1DIAGNOSTIC,     8, 0.000000000000e+00, 1.797693134862e+308, 9.9293e+00, 9.2944e-01, \n",
            " 1DIAGNOSTIC,     9, 0.000000000000e+00, 1.797693134862e+308, 1.0848e+01, 9.1864e-01, \n",
            " 1DIAGNOSTIC,    10, 0.000000000000e+00, -0.000000000000e+00, 1.1781e+01, 9.3300e-01, \n",
            "  Elapsed time (stage 0): 12.0335\n",
            "\n",
            "\n",
            "Total elapsed time: 12.0346\n",
            "Motion Field scaling...\n",
            "Prep...\n",
            "Preconditioner calculation...\n",
            "TV dim:[3, 187, 124, 126]\n",
            "Recon...\n",
            "alpha:0.0025444072671234608\n",
            "outer iter:0, res:6.016482353210449\n",
            "outer iter:1, res:1.4748955965042114\n",
            "outer iter:2, res:0.6894619464874268\n",
            "outer iter:3, res:0.40070945024490356\n",
            "outer iter:4, res:0.24960081279277802\n",
            "outer iter:5, res:0.15603721141815186\n",
            "outer iter:6, res:0.0925200954079628\n",
            "outer iter:7, res:0.04735657572746277\n",
            "outer iter:8, res:0.015823498368263245\n",
            "outer iter:9, res:0.013372482731938362\n",
            "outer iter:10, res:0.028027648106217384\n",
            "outer iter:11, res:0.03749615699052811\n",
            "outer iter:12, res:0.04150688648223877\n",
            "outer iter:13, res:0.04076992720365524\n",
            "outer iter:14, res:0.03629593551158905\n",
            "outer iter:15, res:0.029362892732024193\n",
            "outer iter:16, res:0.021425435319542885\n",
            "outer iter:17, res:0.01399670634418726\n",
            "outer iter:18, res:0.008692552335560322\n",
            "outer iter:19, res:0.006999203469604254\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MuYtR0YyNZ6n",
        "colab_type": "text"
      },
      "source": [
        "# Plot\n",
        "plot the motion-resolved image and the iMoCo reconstructed image"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oKxv2nOuC_Fy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "593191cc-c7c6-4df6-9ec6-e2612c98b708"
      },
      "source": [
        "# plot motion-resolved mrL image\n",
        "mrL = cfl.read_cfl('lung_mri/MRI_Raw_mrL')\n",
        "pl.ImagePlot(mrL, x = -3, y = -1)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sigpy.plot.ImagePlot at 0x7ff54df28898>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3vGpRXZ4_dj7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "89c4d752-38ae-4080-c39e-3c976c716c16"
      },
      "source": [
        "# plot iMoCo image\n",
        "imoco = cfl.read_cfl('lung_mri/MRI_Raw_imoco')\n",
        "pl.ImagePlot(imoco, x = -3, y = -1)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sigpy.plot.ImagePlot at 0x7ff54df287f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hjq7CRFEDIf2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}