A 3D MRI denoising algorithm based on Bayesian theory.
Background: Within this manuscript a noise filtering technique for magnetic resonance image stack is presented. Magnetic resonance images are usually affected by artifacts and noise due to several reasons. Several denoising approaches have been proposed in literature, with different trade-off betwee...
| Publicado en: | BioMedical Engineering OnLine Vol. 16; pp. 1 - 20 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
BioMed Central
2/7/2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=121190748&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 121190748 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1475925X 1CGX jtl: BioMedical Engineering OnLine issn: 1475925X maglogo: N pubinfo: dt: 2/7/2017 vid: 16 pid: 24147 pub: BioMed Central artinfo: ui: 121190748 121190748 NLM28173816 10.1186/s12938-017-0319-x NLM28173816 121190748 ppf: 1 ppct: 19 formats: tig: atl: A 3D MRI denoising algorithm based on Bayesian theory. aug: au: Baselice, Fabio Ferraioli, Giampaolo Pascazio, Vito affil: Dipartimento di Ingegneria, University of Naples Parthenope, Centro Direzionale di Napoli, Is. C4, 80143 Naples, Italy sug: subj: Image Enhancement Methods Imaging, Three-Dimensional Methods Artifacts Magnetic Resonance Imaging Methods Algorithms Brain Anatomy and Histology Information Science Methods Sensitivity and Specificity Data Analysis, Statistical Probability Magnetic Resonance Imaging Equipment and Supplies Phantoms, Imaging Reproducibility of Results Impact of Events Scale ab: Background: Within this manuscript a noise filtering technique for magnetic resonance image stack is presented. Magnetic resonance images are usually affected by artifacts and noise due to several reasons. Several denoising approaches have been proposed in literature, with different trade-off between computational complexity, regularization and noise reduction. Most of them is supervised, i.e. requires the set up of several parameters. A completely unsupervised approach could have a positive impact on the community.Results: The method exploits Markov random fields in order to implement a 3D maximum a posteriori estimator of the image. Due to the local nature of the considered model, the algorithm is able do adapt the smoothing intensity to the local characteristics of the images by analyzing the 3D neighborhood of each voxel. The effect is a combination of details preservation and noise reduction. The algorithm has been compared to other widely adopted denoising methodologies in MRI. Both simulated and real datasets have been considered for validation. Real datasets have been acquired at 1.5 and 3 T. The methodology is able to provide interesting results both in terms of noise reduction and edge preservation without any supervision.Conclusions: A novel method for regularizing 3D MR image stacks is presented. The approach exploits Markov random fields for locally adapt filter intensity. Compared to other widely adopted noise filters, the method has provided interesting results without requiring the tuning of any parameter by the user. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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