Model-driven image registration
Python implementation of model-based image coregistration for quantitative medical imaging applications.
The distribution comes with a number of common signal models and uses ITK-Elastix for deformable image registration.
CAUTION: mdreg is developed in public but is work in progress and backwards compatibility is not guaranteed. It is likely there WILL be breaking changes in future versions
pip install mdreg.
Example data in DICOM format are provided for testing the setup.
How to run
Input data must be image arrays in numpy format, with dimensions
To perform MDR on an image array
im with default settings do:
from mdreg import MDReg mdr = MDReg() mdr.set_array(im) mdr.fit()
These default settings will apply a linear signal model and coregistration
as defined in the elastix parameter file
When fitting is complete the following data are available:
mdr.coreg: motion-corrected data are in the same dimensions as the original
mdr.deformation: the calculated deformation fields in format
(x,y,z,d,t). The dimension
ycomponents of the deformation field. The third
zcomponent exists if the input array is 3D.
mdr.fit: the model fit to the coregistered images in the same dimensions
(x,y,t)as the original
mdr.par: the fitted parameters are in dimensions
(x,y,z,p)(for 3D data), where
penumerates the model parameters.
MDR can be configured to apply different signal models and elastix coregistration settings. A number of example models and alternative elastix parameter files are included in the distribution as templates.
The following example fits a mono-exponential decay, uses a mask
im_mask for co-registration and applies an elastix parameter file
par_file optimized for a previous DTI-MRI study:
from mdreg import MDReg from mdreg.models import exponential_decay mdr = MDReg() mdr.set_array(im) mdr.set_mask(im_mask) mdr.signal_model = exponential_decay mdr.read_elastix(par_file) mdr.fit()
im_mask must be a binary (0's and 1's) or boolean (True's and False's) image array in numpy format with the same dimensions as
The signal model often depends on fixed constants and signal parameters such as sequence parameters in MRI, or patient-specific constants. These should all be grouped in a list and set before running the signal model.
Equally elastix parameters can be fine tuned, either by importing a dedicated elastix file, or by modifying the settings.
You may also choose if you wish to run the process in multiple cores (
parallel) and to print the co-registration progress to the terminal plus a text file (
log) or not.
Then, a number of parameters are available to optimize MDR such as the precision (stopping criterion) and maximum number of iterations.
from mdreg import MDReg from mdreg.models import exponential_decay t = [0.0, 1.25, 2.50, 3.75] # time points for exponential in sec mdr = MDReg() mdr.set_array(im) mdr.signal_parameters = t mdr.signal_model = exponential_decay mdr.set_elastix(MaximumNumberOfIterations = 256) # change defaults mdr.precision = 0.5 # default = 1 mdr.max_iterations = 3 # default = 5 mdr.parallel = False # default = True mdr.log = True # default = False mdr.fit()
mdreg comes with a number of options to
export results and diagnostics:
mdr.export_unregistered = True # export parameters and fit without registration mdr.export_path = filepath # default is a results folder in the current working directory mdr.export() # export results after calling fit.
This export creates movies of original images, motion corrected images, modelfits, and maps of the fitted parameters.
Model fitting without motion correction
MDReg also can be used to perform model fitting
without correcting the motion. The following script
fits a linearised exponential model to each pixel and exports data
of model and fit:
from mdreg import MDReg from mdreg.models import exponential_decay mdr = MDReg() mdr.set_array(im) mdr.signal_model = linear_exponential_decay mdr.fit_signal() mdr.export_data() mdr.export_fit()
Defining new MDR models
A model must be defined as a separate module or class with two required functions
pars() must return a list of strings specifying the names of the model parameters.
main(im, const) performs the pixel based model fitting and has two required arguments.
im is a numpy ndarray with dimensions
const is a list
of any constant model parameters.
The function must return the fit to the model as an numpy ndarray with the same dimensions
im, and an ndarray
pars with dimensions
the model parameters.
mdreg was first developed for use in quantitative renal MRI in the iBEAt study,
and validated against group-wise model-free registration
(Tagkalakis F, et al. Model-based motion correction outperforms a model-free method in quantitative renal MRI. Abstract-1383, ISMRM 2021).
The iBEAt study is part of the BEAt-DKD project. The BEAt-DKD project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 115974. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA with JDRF. For a full list of BEAt-DKD partners, see www.beat-dkd.eu.
Kanishka Sharma, Joao Almeida e Sousa, Steven Sourbron
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