  Parallel imaging is widely used in magnetic resonance imaging as an
acceleration technology. Traditional linear reconstruction methods in parallel
imaging often suffer from noise amplification. Recently, a non-linear robust
artificial-neural-network for k-space interpolation (RAKI) exhibits superior
noise resilience over other linear methods. However, RAKI performs poorly at
high acceleration rates, and needs a large amount of autocalibration signals as
the training samples. In order to tackle these issues, we propose a
multi-weight method that implements multiple weighting matrices on the
undersampled data, named as MW-RAKI. Enforcing multiple weighted matrices on
the measurements can effectively reduce the influence of noise and increase the
data constraints. Furthermore, we incorporate the strategy of multiple
weighting matrixes into a residual version of RAKI, and form
MW-rRAKI.Experimental compari-sons with the alternative methods demonstrated
noticeably better reconstruction performances, particularly at high
acceleration rates.
