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Python Sparse data Analysis Package external MRI plugin.

Note

This page is a reference documentation. It only explains the function signature, and not how to use it. Please refer to the gallery for the big picture.

mri.scripts.gridsearch.launch_grid(kspace_data, reconstructor_class, reconstructor_kwargs, fourier_op=None, linear_params=None, regularizer_params=None, optimizer_params=None, compare_metric_details=None, n_jobs=1, verbose=0)[source]

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

Examples

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© 2019, Antoine Grigis Samuel Farrens Jean-Luc Starck Philippe Ciuciu