File size: 2,985 Bytes
6dbba9b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Motion correction in ANTsPy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We rely on ants.registration to do motion correction which provides the user with full access to parameters and outputs. The key steps, then, are to:\n",
"* split the N dimensional (e.g. N=4) image to a list of N-1 dimensional images\n",
"* run registration to a selected fixed image for each image in the list\n",
"* merge the results back to a N dimensional image.\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import ants\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We illustrate the steps below by building a 3D \"functional\" image and then \"motion correcting\" just as we would do with functional MRI or any other dynamic modality."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"image = ants.image_read(ants.get_ants_data('r16'))\n",
"image2 = ants.image_read(ants.get_ants_data('r64'))\n",
"ants.set_spacing( image, (2,2) )\n",
"ants.set_spacing( image2, (2,2) )\n",
"imageTar = ants.make_image( ( *image2.shape, 2 ) )\n",
"ants.set_spacing( imageTar, (2,2,2) )\n",
"fmri = ants.list_to_ndimage( imageTar, [image,image2] )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we motion correct this image just using the first slice as target."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"ants.set_direction( fmri, np.eye( 3 ) * 2 )\n",
"images_unmerged = ants.ndimage_to_list( fmri )\n",
"motion_corrected = list()\n",
"for i in range( len( images_unmerged ) ):\n",
" areg = ants.registration( images_unmerged[0], images_unmerged[i], \"SyN\" )\n",
" motion_corrected.append( areg[ 'warpedmovout' ] )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Merge the resuling list back to a 3D image."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"motCorr = ants.list_to_ndimage( fmri, motion_corrected )\n",
"# ants.image_write( motCorr, '/tmp/temp.nii.gz' )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Done!"
]
}
],
"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": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|