etomo.reconstructors.forwardtomo#

This implements the single image reconstruction using a Radon forward model.

class TomoReconstructor(data_op, linear_op=None, gradient_formulation='synthesis', verbose=0, **kwargs)[source]#

Bases: etomo.reconstructors.base.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.