Multi-slice 2D registration

This examples shows how mdreg can be used to perform 2D motion correction slice-by-slice on a 4D array. Typical application examples are motion correction of multi-slice MRI series such as Look-Locker T1 mapping as in this example.

Setup

Import packages

import numpy as np
import mdreg

Fetch the multi-slice MOLLI dataset and plot

data = mdreg.fetch('MOLLI')

# Get the relevant variables
array = data['array']
TI = np.array(data['TI'])/1000

# Visualise the motion
mdreg.animation(array, vmin=0, vmax=1e4, show=True)

Once Loop Reflect

<matplotlib.animation.ArtistAnimation object at 0x0000019CA896D060>

Perform slice-by-slice motion correction

The signal model for the MOLLI is the following:

molli = {
    'func': mdreg.abs_exp_recovery_2p,
    'TI': TI,
}

Slice-by-slice analysis works the same way as single-slice analysis. We just have to remember to set the keyword argument force_2d to True so fit knows that we want 2D motion correction. This overrules the default behaviour of fitting 3D data with 3D motion correction:

coreg, defo, fit, pars = mdreg.fit(
    array, fit_image=molli, force_2d=True, maxit=2)

Visualise the results

anim = mdreg.animation(
    coreg, title='Motion corrected',
    interval=500, vmin=0, vmax=1e4, show=True)

Once Loop Reflect

Different options per slice

It is not uncommon that each slice in a multislice sequence has different imaging parameters. For instance in a MOLLI sequence such as used in this example, it is often the case that each slice has its own set of TI values.

The situation can be accomodated in fit by assigning a list of dictionaries to the fit_image argument - one for each slice. We illustrate that here assuming that the TI’s for each slice are offset by 100 ms relative to the previous slice:

molli = [
    {
        'func': mdreg.abs_exp_recovery_2p,
        'TI': TI + 0.1 * z,
    }
for z in range(array.shape[2])]

The call to mdreg is the same as before

coreg, defo, fit, pars = mdreg.fit(
    array, fit_image=molli, force_2d=True, maxit=2)

Visualise the results

anim = mdreg.animation(
    coreg, title='Motion corrected (variable TI)',
    interval=500, vmin=0, vmax=1e4, show=True)

Once Loop Reflect

Slice-by-slice with custom models

If the signal model fit is defined with a custom model via the fit_pixel argument, the slice-by-slice operation works the same: set the force_2d keyword to True, and - if each slice has different parameter settings - supply the fit_pixel argument as a list of dictionaries.