iterative Sense + Tikonov regularisation

supersedes #6

Design suggestion for IterativeSENSE reconstruction algorithm

class iterativeSENSE(Reconstruction):
     acquisition_model: LinearOperator
     initial_value: None | IData
     n_max_iterations: int
     dcf: DcfOperator | None

Design suggestion for IterativeSENSEWithTikonov reconstruction algorithm

class iterativeSENSEWithTikonov(Reconstruction):
     acquisition_model: LinearOperator
     initial_value: None | IData
     n_max_iterations: int
     dcf: DcfOperator | None
     regularisation_image: None | IData
     regularisation_weight: float

If regularisation_weight == 0 or regularisation_image is None this defaults to iterativeSENSE

Open questions:

Should we have an additional l2_regularised_least_squares optimizer which solves the more general problem ||Ax - y||_2^2 + lambda||Tx - xreg||_2^2 or are we happy to use cg directly
In order to be able to write A^H A + lambda as an operator do we need an additional unity operator or can this already be achieved with our current operator classes
Would it make sense to have an (Linear)AcquisitionModel Operator which includes the functionality of "from_kdata", "recalculate_csm", "recalculate_fourier_op"... which is currently in DirectReconstruction. DirectReconstruction could then also get acquisition_model as input.

PTB-MR / mrpro

iterative Sense + Tikonov regularisation #321