title: A sparse Bayesian representation for super-resolution of cardiac MR images.
abstract: 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.
authors.affiliation: Computer Imaging and Medical Applications Laboratory - CIM@LAB, Universidad Nacional de Colombia, Bogotá, Colombia; Universidad Militar Nueva Granada, Bogotá, Colombia.
authors.forename: Nelson F
authors.initials: NF
authors.lastname: Velasco
authors.affiliation: Computer Imaging and Medical Applications Laboratory - CIM@LAB, Universidad Nacional de Colombia, Bogotá, Colombia; Departamento de Ingeniería de Sistemas, Pontificia Universidad Javeriana, Bogotá, Colombia.
authors.forename: Andrea
authors.initials: A
authors.lastname: Rueda
authors.affiliation: Departamento de Física Matemática y de Fluidos, Universidad Nacional de Educación a Distancia, Madrid, Spain.
authors.forename: Cristina
authors.initials: C
authors.lastname: Santa Marta
authors.affiliation: Computer Imaging and Medical Applications Laboratory - CIM@LAB, Universidad Nacional de Colombia, Bogotá, Colombia. Electronic address: edromero@unal.edu.co.
authors.forename: Eduardo
authors.initials: E
authors.lastname: Romero
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001499:Bayes Theorem; D006321:Heart / Q000000981:diagnostic imaging*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D059629:Signal-To-Noise Ratio
keywords: Magnetic resonance; Sparse representation; Super-resolution