sigpy.mri.app.TotalVariationRecon

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

Total variation regularized reconstruction.

Considers the problem:

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

where P is the sampling operator, F is the Fourier transform operator, S is the SENSE operator, G is the gradient 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.
  • 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

Block, K. T., Uecker, M., & Frahm, J. (2007). Undersampled radial MRI with multiple coils. Iterative image reconstruction using a total variation constraint. Magnetic Resonance in Medicine, 57(6), 1086-1098.

__init__(y, mps, lamda, weights=None, coord=None, 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.