# -*- coding: utf-8 -*-
##########################################################################
# pySAP - Copyright (C) CEA, 2017 - 2018
# Distributed under the terms of the CeCILL-B license, as published by
# the CEA-CNRS-INRIA. Refer to the LICENSE file or to
# http://www.cecill.info/licences/Licence_CeCILL-B_V1-en.html
# for details.
##########################################################################
"""
This implements the single image reconstruction using a Radon forward model.
"""
from .base import ReconstructorBase
from ..operators import GradSynthesis, GradAnalysis, WaveletPywt
[docs]class TomoReconstructor(ReconstructorBase):
""" This class implements the Single channel MR image Reconstruction.
Notes
-----
For the Analysis case, finds the solution for x of:
..math:: (1/2) * sum(||R x - y||^2_2, 1) + mu * H (W x)
For the Synthesis case, finds the solution of:
..math:: (1/2) * sum(||R Wt alpha - y||^2_2, 1) + mu * H (alpha)
Parameters
----------
data_op: object of class Radon2D, Radon3D located in etomo.operators
Defines the Radon operator R in the above equation.
linear_op: object, (optional, default None)
Defines the linear sparsifying operator W. This must operate on x and
have 2 functions, op(x) and adj_op(coeff) which implements the
operator and adjoint operator. For high order TV, this can be object of
class HOTV or HOTV_3D from etomo.operators .
If None, sym8 wavelet with nb_scale=3 is chosen.
regularizer_op: operator, (optional default None)
Defines the regularization operator for the regularization function H.
If None, the regularization chosen is Identity and the optimization
turns to gradient descent.
gradient_formulation: str between 'analysis' or 'synthesis',
default 'synthesis'
defines the formulation of the image model which defines the gradient.
verbose: int, optional default 0
Verbosity levels
1 => Print basic debug information
5 => Print all initialization information
20 => Calculate cost at the end of each iteration.
30 => Print the debug information of operators if defined by class
NOTE - High verbosity (>20) levels are computationally intensive.
**kwargs : Extra keyword arguments
for gradient initialization:
Please refer to mri.operators.gradient.base for information
regularizer_op: operator, (optional default None)
Defines the regularization operator for the regularization
function H. If None, the regularization chosen is Identity and
the optimization turns to gradient descent.
"""
def __init__(self, data_op, linear_op=None,
gradient_formulation="synthesis", verbose=0, **kwargs):
# Ensure that we are not in multichannel config
if linear_op is None:
# TODO change nb_scales to max_nb_scale - 1
linear_op = WaveletPywt(
wavelet_name="sym8",
nb_scale=3,
)
if gradient_formulation == 'analysis':
grad_class = GradAnalysis
elif gradient_formulation == 'synthesis':
grad_class = GradSynthesis
super().__init__(
data_op=data_op,
linear_op=linear_op,
gradient_formulation=gradient_formulation,
grad_class=grad_class,
verbose=verbose,
**kwargs,
)
