Quantitative
Susceptibility Mapping reconstructions may benefit from L1-regularization
and magnitude weighing, however these iterative reconstruction methods are
time-consuming. Recently, progression has been made in reducing the reconstruction
times with Split Bregman iterations, allowing subject-specific regularization
weights. Here a further reduction of the reconstruction time is reported,
mostly based on accelerating the automatic selection of the optimal regularization
parameter. The overall
procedure reduces computational load more than threefold, without accuracy
loss. Reduction of reconstruction times, may contribute to realize QSM
algorithms which are either clinically feasible, or that may pave the way to
include more sophisticated regularization mechanisms.
