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 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.
Dipartimento di Ingegneria, University of Naples Parthenope, Centro Direzionale di Napoli, Is. C4, 80143, Naples, Italy. fabio.baselice@uniparthenope.it.
Fabio
http://orcid.org/0000-0002-5964-8667
F
Baselice
Dipartimento di Scienze e Tecnologie, University of Naples Parthenope, Centro Direzionale di Napoli, Is. C4, 80143, Naples, Italy.
Giampaolo
G
Ferraioli
Dipartimento di Ingegneria, University of Naples Parthenope, Centro Direzionale di Napoli, Is. C4, 80143, Naples, Italy.
Vito
V
Pascazio
D000465:Algorithms; D016477:Artifacts; D001499:Bayes Theorem; D001921:Brain / Q000033:anatomy & histology*; D003627:Data Interpretation, Statistical; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000295:instrumentation / Q000379:methods*; D008390:Markov Chains; D010363:Pattern Recognition, Automated / Q000379:methods; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D000069558:Unsupervised Machine Learning
3D MRI denoising; Markov random fields; Maximum a posteriori; Statistical signal processing