Python Sparse data Analysis Package external MRI plugin.

Internal Class to save a test case in a format and call reconstruct

Parameters

kspace_data : np.ndarray

the kspace data for reconstruction

linear_op_class : class

linear operator initialization class

linear_op_kwargs : dict

kwargs for initializing linear operator

regularizer_op_class : class

regularizer operator initialization class

regularizer_op_kwargs : dict

kwargs for initializing regularizer operator

optimizer_kwargs : dict

kwargs for optimizer

reconstruct_case(fourier_op, reconstructor_class, reconstructor_kwargs, fourier_params=None)[source]¶

Internal Function to carry out reconstruction for a special case. This function pulls in appropriate keyword arguments from input and declares appropriate Linear, Fourier and Regularizer Operators. These operators later are used to create the image model by defining the reconstructor. Then the reconstruction is carried and results are returned

Parameters

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

reconstructor_class : class

reconstructor class

reconstructor_kwargs : dict

extra kwargs for reconstructor

fourier_params : dict, default None

holds dictionary with init_class pointing to fourier class to be used and args having keyword arguments for initialization This is passed only if fourier_op is None so that fourier_op can be made on spot during reconstruction. NOTE: We declare fourier operator inside this function to allow parallel execution as NonCartesianFFT cannot be pickled.