Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-Weight Respecification of Scan-specific Learning for Parallel
  Imaging   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.

17
The categories of this paper are: q-fin
25
The categories of this paper are: patt-sol
10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
2
The categories of this paper are: math
35
The categories of this paper are: mtrl-th
33
The categories of this paper are: plasm-ph
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
32
The categories of this paper are: atom-ph
22
The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
21
The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin