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A sparse Bayesian representation for super-resolution of cardiac MR images

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dc.contributor.authorVelasco, Nelson F.
dc.contributor.authorRueda Olarte, Andrea del Pilar
dc.contributor.authorRomero, Eduardo C.
dc.contributor.authorSanta Marta Pastrana, Cristina María
dc.date.accessioned2024-05-20T11:28:40Z
dc.date.available2024-05-20T11:28:40Z
dc.date.issued2017-02
dc.description.abstractHigh-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.en
dc.description.versionversión final
dc.identifier.doihttp://doi.org/10.1016/j.mri.2016.10.009
dc.identifier.issn1873-5894
dc.identifier.urihttps://hdl.handle.net/20.500.14468/12070
dc.journal.titleMagnetic Resonance Imaging
dc.journal.volume36
dc.language.isoes
dc.publisherElsevier
dc.relation.centerFacultad de Ciencias
dc.relation.departmentFísica Matemática y de Fluídos
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject.keywordsMagnetic resonance
dc.subject.keywordsSuper-resolution
dc.subject.keywordsSparse representation
dc.titleA sparse Bayesian representation for super-resolution of cardiac MR imageses
dc.typejournal articleen
dc.typeartículoes
dspace.entity.typePublication
relation.isAuthorOfPublication3d74eb47-9db3-41ef-83e7-f7bdb91dde8c
relation.isAuthorOfPublication.latestForDiscovery3d74eb47-9db3-41ef-83e7-f7bdb91dde8c
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