sigpy.mri.app.L1WaveletRecon

class sigpy.mri.app.L1WaveletRecon(y, mps, lamda, weights=None, coord=None, wave_name='db4', device=<CPU Device>, coil_batch_size=None, comm=None, show_pbar=True, transp_nufft=False, **kwargs)[source]

L1 Wavelet regularized reconstruction.

Considers the problem

\[\min_x \frac{1}{2} \| P F S x - y \|_2^2 + \lambda \| W x \|_1\]

where P is the sampling operator, F is the Fourier transform operator, S is the SENSE operator, W is the wavelet operator, x is the image, and y is the k-space measurements.

Parameters:
  • y (array) – k-space measurements.
  • mps (array) – sensitivity maps.
  • lamda (float) – regularization parameter.
  • weights (float or array) – weights for data consistency.
  • coord (None or array) – coordinates.
  • wave_name (str) – wavelet name.
  • device (Device) – device to perform reconstruction.
  • coil_batch_size (int) – batch size to process coils.
  • affects memory usage. (Only) –
  • comm (Communicator) – communicator for distributed computing.
  • **kwargs – Other optional arguments.

References

Lustig, M., Donoho, D., & Pauly, J. M. (2007). Sparse MRI: The application of compressed sensing for rapid MR imaging. Magnetic Resonance in Medicine, 58(6), 1082-1195.

__init__(y, mps, lamda, weights=None, coord=None, wave_name='db4', device=<CPU Device>, coil_batch_size=None, comm=None, show_pbar=True, transp_nufft=False, **kwargs)[source]

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(y, mps, lamda[, weights, coord, …]) Initialize self.
objective()
run() Run the App.