A sparse Bayesian representation for super-resolution of cardiac MR images.
Magn Reson Imaging
; 36: 77-85, 2017 Feb.
Article
em En
| MEDLINE
| ID: mdl-27742436
High-quality cardiac magnetic resonance (CMR) images can be hardly obtained when intrinsic noise sources are present, namely heart and breathing movements. Yet heart images may be acquired in real time, the image quality is really limited and most sequences use ECG gating to capture images at each stage of the cardiac cycle during several heart beats. This paper presents a novel super-resolution algorithm that improves the cardiac image quality using a sparse Bayesian approach. The high-resolution version of the cardiac image is constructed by combining the information of the low-resolution series -observations from different non-orthogonal series composed of anisotropic voxels - with a prior distribution of the high-resolution local coefficients that enforces sparsity. In addition, a global prior, extracted from the observed data, regularizes the solution. Quantitative and qualitative validations were performed in synthetic and real images w.r.t to a baseline, showing an average increment between 2.8 and 3.2 dB in the Peak Signal-to-Noise Ratio (PSNR), between 1.8% and 2.6% in the Structural Similarity Index (SSIM) and 2.% to 4% in quality assessment (IL-NIQE). The obtained results demonstrated that the proposed method is able to accurately reconstruct a cardiac image, recovering the original shape with less artifacts and low noise.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Processamento de Imagem Assistida por Computador
/
Imageamento por Ressonância Magnética
/
Coração
Tipo de estudo:
Prognostic_studies
/
Qualitative_research
Limite:
Humans
Idioma:
En
Revista:
Magn Reson Imaging
Ano de publicação:
2017
Tipo de documento:
Article
País de afiliação:
Colômbia
País de publicação:
Holanda