Python Sparse data Analysis Package external MRI plugin.

Gather the best reconstruction result.

This function launches off reconstruction for a grid specified through use of kwarg dictionaries.

Parameters

kspace_data: np.ndarray

the kspace data for reconstruction

reconstructor_class: class

reconstructor class

reconstructor_kwargs: dict

extra kwargs for reconstructor

fourier_op: object of class FFT

this defines the fourier operator. for NonCartesianFFT, please make fourier_op as None and pass fourier_params to allow parallel execution

linear_params: dict, default None

dictionary for linear operator parameters if None, a sym8 wavelet is chosen

regularizer_params: dict, default None

dictionary for regularizer operator parameters if None, mu=0, ie no regularization is done

optimizer_params: dict, default None

dictionary for optimizer key word arguments if None, a FISTA optimization is done for 100 iterations

compare_metric_details: dict default None

dictionary that holds the metric to be compared and metric direction please refer to gather_result documentation. if None, all raw_results are returned and best_idx is None

n_jobs: int, default 1

number of parallel jobs for execution

verbose: int default 0

Verbosity level 0 => No debug prints 1 => View best results if present