{ "cells": [ { "cell_type": "markdown", "id": "5ccbcbde", "metadata": {}, "source": [ "# Normalization module\n", "The normalization module of the SUIT toolbox calculates a deformation field that maps the cerebellum of an individual subject to the SUIT template, using the mask generated by the isolation module. The Python-version uses the ANTSPy package to perform the normalization. The deformation field can then be used to reslice functional and anatomical data into SUIT space.\n", "\n", "This notebook shows more detailed usage and options for the normalization module. We assume you have produced an isolation mask (``_cerebellum_dseg.nii.gz``) as described in the last step. " ] }, { "cell_type": "code", "execution_count": null, "id": "015ec5f6", "metadata": { "scrolled": true }, "outputs": [], "source": [ "# Import necessary packages\n", "from nilearn import plotting as npl\n", "import SUITPy as suit\n", "import nibabel as nib\n", "import matplotlib.pyplot as plt\n", "import os" ] }, { "cell_type": "markdown", "id": "4530b4fa", "metadata": {}, "source": [ "## Basic usage \n", "In the simplest case, `normalize` function takes the T1-weight image and isolation mask (``_cerebellum_dseg.nii.gz`` from `isolation` function) as two parameters: \n" ] }, { "cell_type": "code", "execution_count": 2, "id": "1bd1b660", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normalizing sub-ex_T1w to tpl-SUIT_T1w.nii.gz\n", "Saving the normalized image into sub-ex_T1w_space-SUIT.nii.gz\n", "Saving deformation field into sub-ex_T1w_to-SUIT_mode-image_xfm.nii.gz\n" ] } ], "source": [ "# This function normalizes a source image to the SUIT cerebellar template using a provided cerebellum mask.\n", "results = suit.normalize(source_file = 'sub-ex_T1w.nii.gz',\n", " mask_file = 'sub-ex_T1w_cerebellum_dseg.nii.gz',\n", " verbose = 1)" ] }, { "cell_type": "markdown", "id": "4daf6bb5", "metadata": {}, "source": [ "Results is a dictionary that contains the filenames of the saved files. \n", "\n", "By default, ``normalize`` saves two files: \n", "\n", "* The deformation field required to reslice images into SUIT space: ``_to-SUIT_mode-image_xfm.nii.gz``\n", "* The resliced and masked anatomical in SUIT space: ``_space-SUIT.nii.gz``" ] }, { "cell_type": "markdown", "id": "e69cf9e5", "metadata": {}, "source": [ "## Checking the normalization\n", "Even though normalization usually works well, it is useful to check the alignment: " ] }, { "cell_type": "code", "execution_count": 11, "id": "129aafa5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "w_anat_img = nib.load(results['normalized_image'])\n", "template_img = nib.load(os.path.join(os.path.dirname(suit.__file__), 'templates', 'tpl-SUIT_T1w.nii.gz'))\n", "plt.figure(figsize=(6,3))\n", "ax=plt.subplot(2,1,1)\n", "npl.plot_img(w_anat_img,display_mode='y',cut_coords=[-80.0,-60.0,-40.0] , colorbar=False,axes=ax)\n", "ax=plt.subplot(2,1,2)\n", "npl.plot_img(template_img,display_mode='y',cut_coords=[-80.0,-60.0,-40.0] ,colorbar=False,axes=ax)" ] }, { "cell_type": "markdown", "id": "8752fbe4", "metadata": {}, "source": [ "## Saving the Jacobian determinant\n", "Set `write_jacobian_determinant=True` to save Jacobian determinant maps.\n", "The default output is the geometric Jacobian determinant.\n", "\n", "This file is useful for volumetric or VBM analyses.\n", "(for more options, see the `quickstart_volume` documentation)." ] }, { "cell_type": "code", "execution_count": null, "id": "dd569498", "metadata": {}, "outputs": [], "source": [ "# Saving the log-Jacobian determinant maps.\n", "results = suit.normalize(source_file = 'sub-ex_T1w.nii.gz',\n", " mask_file = 'sub-ex_T1w_cerebellum_dseg.nii.gz',\n", " write_log_jacobian_determinant=True,\n", " verbose = 0)" ] }, { "cell_type": "code", "execution_count": null, "id": "8d5e81ee", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the log-Jacobian determinant maps. \n", "log_jac = nib.load(results['log_jacobian_determinant'])\n", "plt.figure(figsize=(4,2))\n", "npl.plot_img(log_jac,display_mode='y',cut_coords=[-60.0] , colorbar=False, vmin=-1,vmax=1,cmap='bwr',axes=plt.gca())\n" ] }, { "cell_type": "markdown", "id": "6e0b415c", "metadata": {}, "source": [ "## Saving the ants transform files\n", "If you are familiar with ANTS and would like to work with the transformation files saved in any format, you can use: \n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "6b265fde", "metadata": {}, "outputs": [], "source": [ "# Set write_ants_transform=True to save the ants transformation files.\n", "results = suit.normalize(source_file = 'sub-ex_T1w.nii.gz',\n", " mask_file = 'sub-ex_T1w_cerebellum_dseg.nii.gz',\n", " write_ants_transform=True,\n", " verbose = 0)" ] }, { "cell_type": "markdown", "id": "02e058d6", "metadata": {}, "source": [ "## Saving inverse deformation\n", "If you want to bring a group template of atlas into the space of an individual, you need the inverse deformation file. \n", "To save that inverse deformation set `write_inv_deformation` to `True`. " ] }, { "cell_type": "code", "execution_count": null, "id": "6a4629e9", "metadata": {}, "outputs": [], "source": [ "# Set write_inv_deformation=True to save the inverse transformation file.\n", "results = suit.normalize(source_file = 'sub-ex_T1w.nii.gz',\n", " mask_file = 'sub-ex_T1w_cerebellum_dseg.nii.gz',\n", " write_inv_deformation=True,\n", " verbose = 0)" ] }, { "cell_type": "markdown", "id": "1f090a85", "metadata": {}, "source": [ "The inverse deformation is saved as `sub-ex_T1w_from-SUIT_mode-image_xfm.nii.gz`. \n", "\n", "We can then reslice any atlas from SUIT into the individual space. \n", "\n", "(for how to use inverse deformation, see the `reslice` documentation)." ] }, { "cell_type": "markdown", "id": "2a4561bb", "metadata": {}, "source": [ "## Other Template spaces\n", "By default, the image is normalized to the SUIT cerebellar template. For compatibility, the toolbox supports three different cerebellar-only templates\n", "\n", "* `SUIT`: The original SUIT template from Diedrichsen (2005). \n", "* `MNI`: A cerebellar-only version of the `MNI152NLin6Asym` template used for example in FSL. \n", "* `MNISym`: A cerebellar-only version of the `MNI152NLin2009cSymC` template for symmetric analyses \n", "\n" ] }, { "cell_type": "code", "execution_count": 12, "id": "e7fd37e8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "template_dir = os.path.join(os.path.dirname(suit.__file__), 'templates')\n", "SUIT_template_img = nib.load(os.path.join(template_dir, 'tpl-SUIT_T1w.nii.gz'))\n", "MNI_template_img = nib.load(os.path.join(template_dir, 'tpl-MNI152NLin6AsymC_T1w.nii.gz'))\n", "MNISym_template_img = nib.load(os.path.join(template_dir, 'tpl-MNI152NLin2009cSymC_T1w.nii.gz'))\n", "\n", "plt.figure(figsize=(10,2))\n", "ax=plt.subplot(1,3,1)\n", "npl.plot_img(SUIT_template_img,display_mode='y',cut_coords=[-60.0],title='SUIT',colorbar=False,axes=ax,annotate=False)\n", "ax=plt.subplot(1,3,2)\n", "npl.plot_img(MNI_template_img,display_mode='y',cut_coords=[-60.0],title='MNI',colorbar=False,axes=ax,annotate=False)\n", "ax=plt.subplot(1,3,3)\n", "npl.plot_img(MNISym_template_img,display_mode='y',cut_coords=[-60.0],title='MNISym',colorbar=False,axes=ax,annotate=False)" ] }, { "cell_type": "markdown", "id": "fc3bdf81", "metadata": {}, "source": [ "\n", "If you want to use a different target space, simply specify the ``space`` parameter: `SUIT`, `MNI152NLin6AsymC` / 'MNI', `MNI152NLin2009cSymC` / 'MNISym'.\n", "\n", "If you wish to use a custom cerebellum-only template that is not included in these options, you can provide a file directly using the ``template_file`` parameter: Optional path to a custom template file." ] }, { "cell_type": "code", "execution_count": null, "id": "ecb885c8", "metadata": {}, "outputs": [], "source": [ "# This function normalizes a source image to the MNI152NLin2009cSymC space using a provided cerebellum mask.\n", "results = suit.normalize(source_file = 'sub-ex_T1w.nii.gz',\n", " mask_file = 'sub-ex_T1w_cerebellum_dseg.nii.gz',\n", " space = 'MNISym',\n", " verbose = 0)" ] }, { "cell_type": "markdown", "id": "f39038f6", "metadata": {}, "source": [ "## Transforms between different template spaces \n", "Even though the atlas templates are very close to each other, the normalization will differ ever so slightly. \n", "\n", "If you have data in one cerebellar space, and you would like to transform it to another, the [Cerebellar Atlas](https://github.com/diedrichsenlab/cerebellar_atlases) repository contains the deformation files between different atlas spaces. \n", "\n", "For example if you have data in SUIT space, and want to transform it to MNISym space: " ] }, { "cell_type": "code", "execution_count": 13, "id": "6012b0b5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "('tpl-MNI152NLin2009cSymC_from-SUIT_mode-image_xfm.nii',\n", " )" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# If you have not downloaded it yet, you need to fetch the xfm files from SUIT to MNISym space from cerebellar atlas repository first\n", "import urllib.request\n", "import os\n", "url = (\"https://raw.githubusercontent.com/DiedrichsenLab/cerebellar_atlases/master/tpl-MNI152NLin2009cSymC/tpl-MNI152NLin2009cSymC_from-SUIT_mode-image_xfm.nii\")\n", "xfm_file = \"tpl-MNI152NLin2009cSymC_from-SUIT_mode-image_xfm.nii\"\n", "urllib.request.urlretrieve(url, xfm_file)" ] }, { "cell_type": "code", "execution_count": 14, "id": "db442508", "metadata": {}, "outputs": [], "source": [ "# then reslice the SUIT-space image to MNISym space\n", "xfm_file = \"tpl-MNI152NLin2009cSymC_from-SUIT_mode-image_xfm.nii\"\n", "output_img = suit.reslice_image(source_image = 'sub-ex_T1w_space-SUIT.nii.gz',\n", " deformation = xfm_file)" ] } ], "metadata": { "kernelspec": { "display_name": "base", "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": "3.11.8" } }, "nbformat": 4, "nbformat_minor": 5 }