sigpy.mri.app.JsenseRecon

class sigpy.mri.app.JsenseRecon(y, mps_ker_width=16, ksp_calib_width=24, lamda=0, device=<CPU Device>, comm=None, weights=None, coord=None, img_shape=None, grd_shape=None, max_iter=10, max_inner_iter=10, normalize=True, show_pbar=True)[source]

JSENSE reconstruction.

Considers the problem

\[\min_{l, r} \frac{1}{2} \| l \ast r - y \|_2^2 + \frac{\lambda}{2} (\| l \|_2^2 + \| r \|_2^2)\]

where \(\ast\) is the convolution operator.

Parameters:
  • y (array) – k-space measurements.
  • mps_ker_width (int) – sensitivity maps kernel width.
  • ksp_calib_width (int) – k-space calibration width.
  • lamda (float) – regularization parameter.
  • device (Device) – device to perform reconstruction.
  • weights (float or array) – weights for data consistency.
  • coord (None or array) – coordinates.
  • img_shape (None or list) – Image shape.
  • grd_shape (None or list) – Shape of grid.
  • max_iter (int) – Maximum number of iterations.
  • max_inner_iter (int) – Maximum number of inner iterations.

References

Ying, L., & Sheng, J. (2007). Joint image reconstruction and sensitivity estimation in SENSE (JSENSE). Magnetic Resonance in Medicine, 57(6), 1196-1202.

Uecker, M., Hohage, T., Block, K. T., & Frahm, J. (2008). Image reconstruction by regularized nonlinear inversion- joint estimation of coil sensitivities and image content. Magnetic Resonance in Medicine, 60(#), 674-682.

__init__(y, mps_ker_width=16, ksp_calib_width=24, lamda=0, device=<CPU Device>, comm=None, weights=None, coord=None, img_shape=None, grd_shape=None, max_iter=10, max_inner_iter=10, normalize=True, show_pbar=True)[source]

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

Methods

__init__(y[, mps_ker_width, …]) Initialize self.
run() Run the App.