This lesson is in the early stages of development (Alpha version)

# Introduction to Image Manipulation using Nilearn

## Overview

Teaching: 30 min
Exercises: 15 min
Questions
• How can be perform arithmetic operations on MR images

Objectives
• Use Nilearn to perform masking and mathematical operations

• Learn how to resample across modalities for image viewing and manipulation

# Introduction

Nilearn is a functional neuroimaging analysis and visualization library that wraps up a whole bunch of high-level operations (machine learning, statistical analysis, data cleaning, etc…) in easy-to-use commands. The neat thing about Nilearn is that it implements Nibabel under the hood. What this means is that everything you do in Nilearn can be represented by performing a set of operations on Nibabel objects. This has the important consequence of allowing you, yourself to perform high-level operations (like resampling) using Nilearn then dropping into Nibabel for more custom data processing then jumping back up to Nilearn for interactive image viewing. Pretty cool!

# Setting up

The first thing we’ll do is to important some Python modules that will allow us to use Nilearn:

import os
import matplotlib.pyplot as plt
from nilearn import image as nimg
from nilearn import plotting as nplot
from bids import BIDSLayout

#for inline visualization in jupyter notebook
%matplotlib inline


Notice that we imported two things:

1. image as nimg - allows us to load NIFTI images using nibabel under the hood
2. plotting as nplot- allows us to using Nilearn’s plotting library for easy visualization

First let’s grab some data from where we downloaded our FMRIPREP outputs. Note that we’re using the argument return_type='file' so that pyBIDS gives us file paths directly rather than the standard BIDSFile objects

#Base directory for fmriprep output
fmriprep_dir = '../data/ds000030/derivatives/fmriprep/'
layout= BIDSLayout(fmriprep_dir, config=['bids','derivatives'])

T1w_files = layout.get(subject='10788', datatype='anat',
desc='preproc', extension='.nii.gz',
return_type='file')

desc='brain', extension='.nii.gz',
return_type='file')


Here we used pyBIDS (as introduced in earlier sections) to pull a single participant. Specifically, we pulled all preprocessed (MNI152NLin2009cAsym, and T1w) anatomical files as well as their respective masks. Let’s quickly view these files for review:

#Display preprocessed files inside of anatomy folder
T1w_files

['/home/jerry/projects/workshops/SDC-BIDS-fMRI/data/ds000030/derivatives/fmriprep/sub-10788/anat/sub-10788_desc-preproc_T1w.nii.gz',
'/home/jerry/projects/workshops/SDC-BIDS-fMRI/data/ds000030/derivatives/fmriprep/sub-10788/anat/sub-10788_space-MNI152NLin2009cAsym_desc-preproc_T1w.nii.gz']


Now that we have our files set up, let’s start performing some basic image operations.

## Basic Image Operations

In this section we’re going to deal with the following files:

1. sub-10171_desc-preproc_T1w.nii.gz - the T1 image in native space
2. sub-10171_desc-brain_mask.nii.gz - a mask with 1’s representing the brain and 0’s elsewhere.
t1 = T1w_files[0].path



Using the plotting module (which we’ve aliased as nplot), we can view our MR image:

nplot.plot_anat(t1_img)


This gives just a still image of the brain. We can also view the brain more interactively using the view_img function. It will require some additional settings however:

nplot.view_img(t1_img,
bg_img=False, # Disable using a standard image as the background
cmap='Greys_r', # Set color scale so white matter appears lighter than grey
symmetric_cmap=False, # We don't have negative values
threshold="auto", # Clears out the background
)


Try clicking and dragging the image in each of the views that are generated!

Try viewing the mask as well!

nplot.plot_anat(bm_img)


### Arithmetic Operations

Let’s start performing some image operations. The simplest operations we can perform is element-wise, what this means is that we want to perform some sort of mathematical operation on each voxel of the MR image. Since voxels are represented in a 3D array, this is equivalent to performing an operation on each element (i,j,k) of a 3D array. Let’s try inverting the image, that is, flip the colour scale such that all blacks appear white and vice-versa. To do this, we’ll use the method

nimg.math_img(formula, **imgs) Where:

• formula is a mathematical expression such as 'a+1'
• **imgs is a set of key-value pairs linking variable names to images. For example a=T1

In order to invert the image, we can simply flip the sign which will set the most positive elements (white) to the most negatve elements (black), and the least positives elements (black) to the least negative elements (white). This effectively flips the colour-scale:

invert_img = nimg.math_img('-a', a=T1)
nplot.plot_anat(invert_img)


Alternatively we don’t need to first load in our t1_img using img.load_img. Instead we can feed in a path to img.math_img:

invert_img = nimg.math_img('-a', a=t1)
nplot.plot_anat(invert_img)


This will yield the same result!

Let’s extend this idea of applying operations to each element of an image to multiple images. Instead of specifying just one image like the following:

nimg.math_img('a+1',a=img_a)

We can specify multiple images by tacking on additional variables:

nimg.math_img('a+b', a=img_a, b=img_b)

The key requirement here is that when dealing with multiple images, that the size of the images must be the same. The reason being is that we’re deaing with element-wise operations. That means that some voxel (i,j,k) in img_a is being paired with some voxel (i,j,k) in img_b when performing operations. So every voxel in img_a must have some pair with a voxel in img_b; sizes must be the same.

We can take advantage of this property when masking our data using multiplication. Masking works by multipling a raw image (our T1), with some mask image (our bm). Whichever voxel (i,j,k) has a value of 0 in the mask multiplies with voxel (i,j,k) in the raw image resulting in a product of 0. Conversely, any voxel (i,j,k) in the mask with a value of 1 multiplies with voxel (i,j,k) in the raw image resulting in the same value. Let’s try this out in practice and see what the result is:

masked_t1 = nimg.math_img('a*b', a=T1, b=bm)


As you can see areas where the mask image had a value of 1 were retained, everything else was set to 0

## Exercise #1

Try applying the mask such that the brain is removed, but the rest of the head is intact! Hint: Remember that a mask is composed of 0’s and 1’s, where parts of the data labelled 1 are regions to keep, and parts of the data that are 0, are to throw away. You can do this in 2 steps:

1. Switch the 0’s and 1’s using an equation (simple addition/substraction) or condition (like x == 0).

## Solution

# Invert the mask

# Apply the mask


## Slicing

Recall that our data matrix is organized in the following manner:

Slicing does exactly what it seems to imply. Given our 3D volume, we can pull out 2D subsets (called “slices”). Here’s an example of slicing moving from left to right via an animated GIF:

What you see here is a series of 2D images that start from the left, and move toward the right. Each frame of this GIF is a slice - a 2D subset of a 3D volume. Slicing can be useful for cases in which you’d want to loop through each MR slice and perform a computation; importantly in functional imaging data slicing is useful for pulling out timepoints as we’ll see later!

Slicing is done easily on an image file using the attribute .slicer of a Nilearn image object. For example we can grab the $10^{\text{th}}$ slice along the x axis as follows:

x_slice = t1_img.slicer[10:11,:,:]


The statement $10:11$ is intentional and is required by .slicer. Alternatively we can slice along the x-axis using the data matrix itself:

t1_data = t1_img.get_data()
x_slice = t1_data[10,:,:]


This will yield the same result as above. Notice that when using the t1_data array we can just specify which slice to grab instead of using :. We can use slicing in order to modify visualizations. For example, when viewing the T1 image, we may want to specify at which slice we’d like to view the image. This can be done by specifying which coordinates to cut the image at:

nplot.plot_anat(t1_img,cut_coords=(50,30,20))


The cut_coords option specifies 3 numbers:

• The first number says cut the X coordinate at slice 50 and display (sagittal view in this case!)
• The second number says cut the Y coordinate at slice 30 and display (coronal view)
• The third number says cut the Z coordinate at slice 20 and display (axial view)

Remember nplot.plot_anat yields 3 images, therefore cut_coords allows you to display where to take cross-sections of the brain from different perspectives (axial, sagittal, coronal)

This covers the basics of image manipulation using T1 images. To review in this section we covered:

• Basic image arithmetic
• Visualization
• Slicing

In the next section we will cover how to integrate additional modalities (functional data) to what we’ve done so far using Nilearn. Then we can start using what we’ve learned in order to perform analysis and visualization!

## Key Points

• MR images are essentially 3D arrays where each voxel is represented by an (i,j,k) index

• Nilearn is Nibabel under the hood, knowing how Nibabel works is key to understanding Nilearn