Reconstruction module

PyMIALSRTK reconstruction functions.

MialsrtkImageReconstruction

Link to code

Bases: nipype.interfaces.base.core.BaseInterface

Creates a high resolution image from a set of low resolution images [1]_.

References

[1]	Tourbier et al.; NeuroImage, 2015. (link to paper)

Example

>>> from pymialsrtk.interfaces.reconstruction import MialsrtkImageReconstruction
>>> srtkImageReconstruction = MialsrtkTVSuperResolution()
>>> srtkImageReconstruction.inputs.bids_dir = '/my_directory'
>>> srtkImageReconstruction.input_images = ['sub-01_ses-01_run-1_T2w.nii.gz', 'sub-01_ses-01_run-2_T2w.nii.gz',     'sub-01_ses-01_run-3_T2w.nii.gz', 'sub-01_ses-01_run-4_T2w.nii.gz']
>>> srtkImageReconstruction.input_masks = ['sub-01_ses-01_run-1_mask.nii.gz', 'sub-01_ses-01_run-2_mask.nii.gz',     'sub-01_ses-01_run-3_mask.nii.gz', 'sub-01_ses-01_run-4_mask.nii.gz']
>>> srtkImageReconstruction.inputs.stacks_order = [3,1,2,4]
>>> srtkImageReconstruction.inputs.sub_ses = 'sub-01_ses-01'
>>> srtkImageReconstruction.inputs.in_roi = 'mask'
>>> srtkImageReconstruction.inputs.in_deltat = 0.01
>>> srtkImageReconstruction.inputs.in_lambda = 0.75
>>> srtkImageReconstruction.run()  # doctest: +SKIP

bids_dir : a directory name

BIDS root directory.

in_roi : ‘mask’ or ‘all’ or ‘box’ or ‘mask’

Define region of interest (required):

• box: Use intersections for roi calculation
• mask: Use masks for roi calculation
• all: Use the whole image FOV.

(Nipype default value: mask)

stacks_order : a list of items which are any value

List of stack run-id that specify the order of the stacks.

input_images : a list of items which are a pathlike object or string representing a file

Input images.

input_masks : a list of items which are a pathlike object or string representing a file

Masks of the input images.

input_rad_dilatation : a float

Radius dilatation used in prior step to construct output filename. (Nipype default value: 1.0)

no_reg : a boolean

Skip slice-to-volume registration.

out_sdi_prefix : a unicode string

Suffix added to construct output scattered data interpolation filename. (Nipype default value: SDI_)

out_transf_postfix : a unicode string

Suffix added to construct output transformation filenames. (Nipype default value: _transform)

sub_ses : a unicode string

Subject and session BIDS identifier to construct output filename. (Nipype default value: x)

output_sdi : a pathlike object or string representing a file

Output scattered data interpolation image file.

output_transforms : a list of items which are a pathlike object or string representing a file

Output transformation files.

MialsrtkTVSuperResolution

Link to code

Bases: nipype.interfaces.base.core.BaseInterface

Apply super-resolution algorithm using one or multiple input images [1]_.

References

[1]	Tourbier et al.; NeuroImage, 2015. (link to paper)

Example

>>> from pymialsrtk.interfaces.reconstruction import MialsrtkTVSuperResolution
>>> srtkTVSuperResolution = MialsrtkTVSuperResolution()
>>> srtkTVSuperResolution.inputs.bids_dir = '/my_directory'
>>> srtkTVSuperResolution.input_images = ['sub-01_ses-01_run-1_T2w.nii.gz', 'sub-01_ses-01_run-2_T2w.nii.gz',     'sub-01_ses-01_run-3_T2w.nii.gz', 'sub-01_ses-01_run-4_T2w.nii.gz']
>>> srtkTVSuperResolution.input_masks = ['sub-01_ses-01_run-1_mask.nii.gz', 'sub-01_ses-01_run-2_mask.nii.gz',     'sub-01_ses-01_run-3_mask.nii.gz', 'sub-01_ses-01_run-4_mask.nii.gz']
>>> srtkTVSuperResolution.input_transforms = ['sub-01_ses-01_run-1_transform.txt', 'sub-01_ses-01_run-2_transform.txt',     'sub-01_ses-01_run-3_transform.txt', 'sub-01_ses-01_run-4_transform.txt']
>>> srtkTVSuperResolution.input_sdi = 'sdi.nii.gz'
>>> srtkTVSuperResolution.inputs.stacks_order = [3,1,2,4]
>>> srtkTVSuperResolution.inputs.sub_ses = 'sub-01_ses-01'
>>> srtkTVSuperResolution.inputs.in_loop = 10
>>> srtkTVSuperResolution.inputs.in_deltat = 0.01
>>> srtkTVSuperResolution.inputs.in_lambda = 0.75
>>> srtkTVSuperResolution.run()  # doctest: +SKIP

bids_dir : a directory name

BIDS root directory.

in_deltat : a float

Parameter deltat of TV optimizer.

in_lambda : a float

TV regularization factor which weights the data fidelity term in TV optimizer.

in_loop : an integer (int or long)

Number of loops (SR/denoising).

input_sdi : a pathlike object or string representing a file

Reconstructed image for initialization. Typically the output of MialsrtkImageReconstruction is used.

deblurring : a boolean

Flag to set deblurring PSF during SR (double the neighborhood). (Nipype default value: False)

in_bregman_loop : an integer (int or long)

Number of Bregman loops. (Nipype default value: 1)

in_gamma : an integer (int or long)

Parameter gamma. (Nipype default value: 10)

in_inner_thresh : a float

Inner loop convergence threshold. (Nipype default value: 1e-05)

in_iter : an integer (int or long)

Number of inner iterations. (Nipype default value: 50)

in_outer_thresh : a float

Outer loop convergence threshold. (Nipype default value: 1e-06)

in_step_scale : an integer (int or long)

Parameter step scale. (Nipype default value: 10)

input_images : a list of items which are a pathlike object or string representing a file

Input image filenames for super-resolution.

input_masks : a list of items which are a pathlike object or string representing a file

Masks of input images for super-resolution.

input_rad_dilatation : a float

Radius dilatation used in prior step to construct output filename. (Nipype default value: 1.0)

input_transforms : a list of items which are a pathlike object or string representing a file

Estimated slice-by-slice ITK transforms of input images.

out_prefix : a unicode string

Prefix added to construct output super-resolution filename. (Nipype default value: SRTV_)

stacks_order : a list of items which are any value

List of stack run-id that specify the order of the stacks.

sub_ses : a unicode string

Subject and session BIDS identifier to construct output filename. (Nipype default value: x)

use_manual_masks : a boolean

Use masks of input files. (Nipype default value: False)

output_json_path : a pathlike object or string representing a file

Output path where output_dict should be saved .

output_sr : a pathlike object or string representing a file

Output super-resolution image file.

MialsrtkTVSuperResolution.m_out_files = ''

MialsrtkTVSuperResolution.m_output_dict = {}