sigpy.mri.app.SenseRecon

class sigpy.mri.app.SenseRecon(y, mps, lamda=0, weights=None, tseg=None, coord=None, device=<CPU Device>, coil_batch_size=None, comm=None, show_pbar=True, transp_nufft=False, **kwargs)[source]

SENSE Reconstruction.

Considers the problem

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

where P is the sampling operator, F is the Fourier transform operator, S is the SENSE 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.
  • tseg (None or Dictionary) – parameters for time-segmented off-resonance correction. Parameters are ‘b0’ (array), ‘dt’ (float), ‘lseg’ (int), and ‘n_bins’ (int). Lseg is the number of time segments used, and n_bins is the number of histogram bins.
  • coord (None or array) – coordinates.
  • device (Device) – device to perform reconstruction.
  • coil_batch_size (int) – batch size to process coils. Only affects memory usage.
  • comm (Communicator) – communicator for distributed computing.
  • **kwargs – Other optional arguments.

References

Pruessmann, K. P., Weiger, M., Scheidegger, M. B., & Boesiger, P. (1999). SENSE: sensitivity encoding for fast MRI. Magnetic resonance in medicine, 42(5), 952-962.

Pruessmann, K. P., Weiger, M., Bornert, P., & Boesiger, P. (2001). Advances in sensitivity encoding with arbitrary k-space trajectories. Magnetic resonance in medicine, 46(4), 638-651.

__init__(y, mps, lamda=0, weights=None, tseg=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, tseg, …]) Initialize self.
objective()
run() Run the App.