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_sources/extra/nifti.ipynb

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"cells": [
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"cell_type": "markdown",
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"metadata": {},
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"# The extra mile\n",
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{
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"cell_type": "markdown",
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"First, use the `load()` function to create a NiBabel image object from a NIfTI file.\n",
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"cell_type": "code",
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},
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{
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"cell_type": "markdown",
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"Loading in a NIfTI file with `NiBabel` gives us a special type of data object which encodes all the information in the file.\n",
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"### 2. Data\n",
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},
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"What type of data is this exactly? We can determine this by calling the `type()` function on `dwi_data`."
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"The data is a multidimensional **array** representing the image data.\n",
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"As expected, the data contains 4 dimensions (*i, j, k* and gradient number).\n",
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"This tells us that the image is 128, 128, 66\n",
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"### 3. [Affine](https://nipy.org/nibabel/coordinate_systems.html): tells the position of the image array data in a reference space\n",
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"To explain this concept, recall that we referred to coordinates in our data as *voxel coordinates (i,j,k)* coordinates such that:\n",

_sources/notebook/01-intro.ipynb

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"# The problem of head-motion in dMRI"
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"A recurring problem for any MRI acquisition is that image reconstruction and modeling are extremely sensitive to very small changes in the position of the imaged object.\n",
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"## Dimensions of the head-motion problem\n",

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