An improved non-Cartesian partially parallel imaging by exploiting artificial sparsity.
Magn Reson Med
; 78(1): 271-279, 2017 07.
Article
em En
| MEDLINE
| ID: mdl-27501442
ABSTRACT
PURPOSE:
To improve the performance of non-Cartesian partially parallel imaging (PPI) by exploiting artificial sparsity, the generalized autocalibrating partially parallel acquisitions (GRAPPA) operator for wider band lines (GROWL) is taken as a specific example for explanation. THEORY This work is based on the GRAPPA-like PPI having an improved performance when the to-be-reconstructed image is sparse in the image domain.METHODS:
A systematic scheme is proposed to artificially generate the sparse image for non-Cartesian trajectory. Using GROWL as a specific non-Cartesian PPI method, artificial sparsity-enhanced GROWL (ARTS-GROWL) is used to demonstrate the efficiency of the proposed scheme. The ARTS-GROWL consists of threesteps:
1) generating synthetic k-space data corresponding to an image with smaller support, that is, artificial sparsity; 2) applying GROWL to the synthetic k-space data from previous step; and 3) recovering the final image from the reconstruction with the processed data.RESULTS:
For simulation and in vivo data, the experiments demonstrate that the proposed ARTS-GROWL significantly reduces the reconstruction errors compared with the conventional GROWL technique for the tested acceleration factors.CONCLUSION:
Taking ARTS-GROWL, for instance, experimental results indicate that artificial sparsity improved the signal-to-noise ratio and normalized root-mean-square error of non-Cartesian PPI. Magn Reson Med 78271-279, 2017. © 2016 International Society for Magnetic Resonance in Medicine.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Processamento de Sinais Assistido por Computador
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Imageamento por Ressonância Magnética
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Interpretação de Imagem Assistida por Computador
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Aumento da Imagem
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Artefatos
Tipo de estudo:
Diagnostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2017
Tipo de documento:
Article