Python Sparse data Analysis Package external MRI plugin.

FISTA or POGM MRI reconstruction.

mri.optimizers.forward_backward.fista(gradient_op, linear_op, prox_op, cost_op, lambda_init=1.0, max_nb_of_iter=300, x_init=None, metric_call_period=5, metrics={}, verbose=0, **lambda_update_params)[source]¶

The FISTA sparse reconstruction

Parameters

gradient_op: instance of class GradBase

the gradient operator.

linear_op: instance of LinearBase

the linear operator: seek the sparsity, ie. a wavelet transform.

prox_op: instance of ProximityParent

the proximal operator.

cost_op: instance of costObj

the cost function used to check for convergence during the optimization.

lambda_init: float, (default 1.0)

initial value for the FISTA step.

max_nb_of_iter: int (optional, default 300)

the maximum number of iterations in the Condat-Vu proximal-dual splitting algorithm.

x_init: np.ndarray (optional, default None)

Inital guess for the image

metric_call_period: int (default 5)

the period on which the metrics are compute.

metrics: dict (optional, default None)

the list of desired convergence metrics: {‘metric_name’: [@metric, metric_parameter]}. See modopt for the metrics API.

verbose: int (optional, default 0)

the verbosity level.

lambda_update_params: dict,

Parameters for the lambda update in FISTA mode

Returns

x_final: ndarray

the estimated FISTA solution.

costs: list of float

the cost function values.

metrics: dict

the requested metrics values during the optimization.

mri.optimizers.forward_backward.pogm(gradient_op, linear_op, prox_op, cost_op=None, max_nb_of_iter=300, x_init=None, metric_call_period=5, sigma_bar=0.96, metrics={}, verbose=0)[source]¶

Perform sparse reconstruction using the POGM algorithm.