loss_l2_AHreg#

mrinufft.extras.optim.loss_l2_AHreg(image: ndarray[tuple[int, ...], dtype[_ScalarType_co]], operator: FourierOperatorBase, kspace_data: ndarray[tuple[int, ...], dtype[_ScalarType_co]], *args, **kwargs)[source]#

Compute the norm of the residual in the image domain.

Parameters:

  • image (NDArray) – Current image estimate. Shape and dtype must be compatible with the operator.

  • operator (FourierOperatorBase) – The NUFFT (non-uniform FFT) operator used for forward modeling.

  • kspace_data (NDArray) – Measured k-space data. Shape must match the output of the operator.op(image).

Returns:

norm_res – The computed L2 regularized least squares loss value(s). If batched, shape = (n_batchs,).

Return type:

float or NDArray

Notes

  • Batch dimension is preserved if present.

  • This function can be used as a callback in cg or lsqr method to keep track of the convergence.

Note

This function uses numpy for all CPU arrays, and cupy for all on-gpu array. It will convert all its array argument to the respective array library. The outputs will be converted back to the original array module and device.

Example using loss_l2_AHreg:#

Least Squares Image Reconstruction

Least Squares Image Reconstruction