Multi-Weight Respecification of Scan-specific Learning for Parallel
Imaging
- URL: http://arxiv.org/abs/2204.01979v1
- Date: Tue, 5 Apr 2022 04:28:06 GMT
- Title: Multi-Weight Respecification of Scan-specific Learning for Parallel
Imaging
- Authors: Hui Tao, Haifeng Wang, Shanshan Wang, Dong Liang, Xiaoling Xu, Qiegen
Liu
- Abstract summary: A non-linear artificial-neural-network for k-space (RAKI) exhibits superior noise resilience over other linear methods.
RAKI performs poorly at high acceleration rates, and needs a large amount of autocalibration signals as the training samples.
We propose a multi-weight method that implements multiple weighting matrices on the undersampled data, named as MW-RAKI.
- Score: 25.47472506331645
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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