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nifti prac

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%% Cell type:markdown id: tags:
# NIfTI images and python
The [`nibabel`](http://nipy.org/nibabel/) module is used to read and write NIfTI
images and also some other medical imaging formats (e.g., ANALYZE, GIFTI,
MINC, MGH). `nibabel` is included within the FSL python environment.
Building upon `nibabel`, the
[`fsl.data`](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/fsl.data.html#module-fsl.data)
package contains a number of FSL-specific classes and functions which you may
find useful. This is covered in a different practical
(`advanced_topics/08_fslpy.ipynb`).
[`fslpy`](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/) library
contains a number of FSL-specific classes and functions which you may find
useful. But let's start with `nibabel` - `fslpy` is introduced in a different
practical (`advanced_topics/08_fslpy.ipynb`).
## Contents
* [Reading images](#reading-images)
* [Header info](#header-info)
* [Voxel sizes](#voxel-sizes)
* [Coordinate orientations and mappings](#orientation-info)
* [Writing images](#writing-images)
* [Exercise](#exercise)
---
<a class="anchor" id="reading-images"></a>
## Reading images
It is easy to read an image:
%% Cell type:code id: tags:
```
import numpy as np
import nibabel as nib
import os.path as op
filename = op.expandvars('${FSLDIR}/data/standard/MNI152_T1_1mm.nii.gz')
imobj = nib.load(filename, mmap=False)
# display header object
imhdr = imobj.header
# extract data (as an numpy array)
imdat = imobj.get_data().astype(float)
# extract data (as a numpy array)
imdat = imobj.get_fdata()
print(imdat.shape)
```
%% Cell type:markdown id: tags:
> Make sure you use the full filename, including the .nii.gz extension.
> Make sure you use the full filename, including the `.nii.gz` extension.
> `fslpy` provides FSL-like automatic file suffix detection though.
> We use the expandvars() function above to insert the FSLDIR
> We use the `expandvars()` function above to insert the FSLDIR
> environmental variable into our string. This function is
> discussed more fully in the file management practical.
Reading the data off the disk is not done until `get_data()` is called.
Reading the data off the disk is not done until `get_fdata()` is called.
> Pitfall:
>
> The option `mmap=False`is necessary as turns off memory mapping,
> which otherwise would be invoked for uncompressed NIfTI files but not for
> compressed files. Since some functionality behaves differently on memory
> mapped objects, it is advisable to turn this off.
> The option `mmap=False` disables memory mapping, which would otherwise be
> invoked for uncompressed NIfTI files but not for compressed files. Since
> some functionality behaves differently on memory mapped objects, it is
> advisable to turn this off unless you specifically want it.
Once the data is read into a numpy array then it is easily manipulated.
> We recommend converting it to float at the start to avoid problems with
> integer arithmetic and overflow, though this is not compulsory.
> The `get_fdata` method will return floating point data, regardless of the
> underlying image data type. If you want the image data in the type that it
> is stored (e.g. integer ROI labels), then use
> `imdat = np.asanyarray(imobj.dataobj)` instead.
---
<a class="anchor" id="header-info"></a>
## Header info
There are many methods available on the header object - for example, look at
`dir(imhdr)` or `help(imhdr)` or the [nibabel webpage about NIfTI
images](http://nipy.org/nibabel/nifti_images.html)
<a class="anchor" id="voxel-sizes"></a>
### Voxel sizes
Dimensions of the voxels, in mm, can be found from:
%% Cell type:code id: tags:
```
voxsize = imhdr.get_zooms()
print(voxsize)
```
%% Cell type:markdown id: tags:
<a class="anchor" id="orientation-info"></a>
### Coordinate orientations and mappings
Information about the NIfTI qform and sform matrices can be extracted like this:
%% Cell type:code id: tags:
```
sform = imhdr.get_sform()
sformcode = imhdr['sform_code']
qform = imhdr.get_qform()
qformcode = imhdr['qform_code']
print(qformcode)
print(qform)
```
%% Cell type:markdown id: tags:
You can also get both code and matrix together like this:
%% Cell type:code id: tags:
```
affine, code = imhdr.get_qform(coded=True)
print(affine, code)
```
%% Cell type:markdown id: tags:
---
<a class="anchor" id="writing-images"></a>
## Writing images
If you have created a modified image by making or modifying a numpy array then
you need to put this into a NIfTI image object in order to save it to a file.
The easiest way to do this is to copy all the header info from an existing
image like this:
%% Cell type:code id: tags:
```
newdata = imdat * imdat
newhdr = imhdr.copy()
newobj = nib.nifti1.Nifti1Image(newdata, None, header=newhdr)
nib.save(newobj, "mynewname.nii.gz")
```
%% Cell type:markdown id: tags:
where `newdata` is the numpy array (the above is a random example only) and
`imhdr` is the existing image header (as above).
> It is possible to also just pass in an affine matrix rather than a
> copied header, but we *strongly* recommend against this if you are
> processing an existing image as otherwise you run the risk of
> swapping the left-right orientation. Those that have used
> `save_avw` in matlab may well have been bitten in this way in the
> past. Therefore, copy a header from one of the input images
> whenever possible, and just use the affine matrix option if you are
> creating an entirely separate image, like a simulation.
If the voxel size of the image is different, then extra modifications will be
required. Take a look at the `fslpy` practical for more advanced image
manipulation options (`advanced_topics/08_fslpy.ipynb`).
required. Take a look at the `fslpy` practical for some extra image
manipulation options, including cropping and resampling
(`advanced_topics/08_fslpy.ipynb`).
---
<a class="anchor" id="exercises"></a>
## Exercise
Write some code to read in a 4D fMRI image (you can find one
[here](http://www.fmrib.ox.ac.uk/~mark/files/av.nii.gz) if you don't have one
handy), calculate the tSNR and then save the 3D result.
> The tSNR of a time series signal is simply its mean divided by its standard
> deviation.
%% Cell type:code id: tags:
```
# Calculate tSNR
```
......
getting_started/05_nifti.md
+ 25
− 18
View file @ 7ee8c4a3
......@@ -6,10 +6,10 @@ MINC, MGH). `nibabel` is included within the FSL python environment.
Building upon `nibabel`, the
[`fsl.data`](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/fsl.data.html#module-fsl.data)
package contains a number of FSL-specific classes and functions which you may
find useful. This is covered in a different practical
(`advanced_topics/08_fslpy.ipynb`).
[`fslpy`](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/) library
contains a number of FSL-specific classes and functions which you may find
useful. But let's start with `nibabel` - `fslpy` is introduced in a different
practical (`advanced_topics/08_fslpy.ipynb`).
## Contents
......@@ -38,31 +38,33 @@ imobj = nib.load(filename, mmap=False)
# display header object
imhdr = imobj.header
# extract data (as an numpy array)
imdat = imobj.get_data().astype(float)
# extract data (as a numpy array)
imdat = imobj.get_fdata()
print(imdat.shape)
```
> Make sure you use the full filename, including the .nii.gz extension.
> Make sure you use the full filename, including the `.nii.gz` extension.
> `fslpy` provides FSL-like automatic file suffix detection though.
> We use the expandvars() function above to insert the FSLDIR
> We use the `expandvars()` function above to insert the FSLDIR
> environmental variable into our string. This function is
> discussed more fully in the file management practical.
Reading the data off the disk is not done until `get_data()` is called.
Reading the data off the disk is not done until `get_fdata()` is called.
> Pitfall:
>
> The option `mmap=False`is necessary as turns off memory mapping,
> which otherwise would be invoked for uncompressed NIfTI files but not for
> compressed files. Since some functionality behaves differently on memory
> mapped objects, it is advisable to turn this off.
> The option `mmap=False` disables memory mapping, which would otherwise be
> invoked for uncompressed NIfTI files but not for compressed files. Since
> some functionality behaves differently on memory mapped objects, it is
> advisable to turn this off unless you specifically want it.
Once the data is read into a numpy array then it is easily manipulated.
> We recommend converting it to float at the start to avoid problems with
> integer arithmetic and overflow, though this is not compulsory.
> The `get_fdata` method will return floating point data, regardless of the
> underlying image data type. If you want the image data in the type that it
> is stored (e.g. integer ROI labels), then use
> `imdat = np.asanyarray(imobj.dataobj)` instead.
---
......@@ -109,6 +111,7 @@ print(affine, code)
<a class="anchor" id="writing-images"></a>
## Writing images
If you have created a modified image by making or modifying a numpy array then
you need to put this into a NIfTI image object in order to save it to a file.
The easiest way to do this is to copy all the header info from an existing
......@@ -134,8 +137,9 @@ where `newdata` is the numpy array (the above is a random example only) and
> creating an entirely separate image, like a simulation.
If the voxel size of the image is different, then extra modifications will be
required. Take a look at the `fslpy` practical for more advanced image
manipulation options (`advanced_topics/08_fslpy.ipynb`).
required. Take a look at the `fslpy` practical for some extra image
manipulation options, including cropping and resampling
(`advanced_topics/08_fslpy.ipynb`).
---
......@@ -148,6 +152,9 @@ Write some code to read in a 4D fMRI image (you can find one
[here](http://www.fmrib.ox.ac.uk/~mark/files/av.nii.gz) if you don't have one
handy), calculate the tSNR and then save the 3D result.
> The tSNR of a time series signal is simply its mean divided by its standard
> deviation.
```
# Calculate tSNR
```
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