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.
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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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