论著摘要 |【AI-MR】基于贝叶斯理论的三维MRI消噪算法(双语版)

2018-02-08 18:57:44 admin 0
标签:  人工智能 贝叶斯理论 去噪 影像去噪 马尔科夫随机场 滤波 统计信号处理 最大后验概率

A 3D MRI denoising algorithm based on Bayesian theory.

发表日期: 2017.02.07   来源:Biomed Eng Online. 2017 Feb 7;16(1):25.

作者:

Baselice F1, Ferraioli G2, Pascazio V3.

作者介绍:

1. Dipartimento di Ingegneria, University of Naples Parthenope, Centro Direzionale di Napoli, Is. C4, 80143, Naples, Italy.

2. Dipartimento di Scienze e Tecnologie, University of Naples Parthenope, Centro Direzionale di Napoli, Is. C4, 80143, Naples, Italy.

3. Dipartimento di Ingegneria, University of Naples Parthenope, Centro Direzionale di Napoli, Is. C4, 80143, Naples, Italy.

摘要

Abstact

背景

在这篇论文中,介绍了磁共振图像叠层的噪声过滤技术。由于几个原因,磁共振图像通常受到伪影和噪声的影响。在文献中已经提出了几种去噪方法,在计算复杂度,正则化和降噪之间有不同的折衷。他们中的大多数是受监督的,即需要建立几个参数。完全没有监督的方法可能会对社区产生积极的影响。

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.

结果

该方法利用马尔可夫随机场来实现图像的3D最大后验估计。由于所考虑的模型的局部性质,通过分析每个体素的三维邻域,该算法能够使平滑强度适应图像的局部特征。效果是细节保存和降噪的结合。该算法已经与其他广泛采用的MRI去噪方法进行了比较。模拟和真实数据集都被考虑用于验证。实际数据集已在1.5和3吨获得。该方法能够在没有任何监督的情况下在降噪和边缘保存方面提供有趣的结果。

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.

结论

提出了一种新的三维磁共振图像叠加正则化方法。该方法利用马尔可夫随机场进行局部适应滤波器的强度。与其他广泛采用的噪声滤波器相比,该方法提供了有趣的结果,而不需要用户调整任何参数。

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.

关键词:

3维MRI消噪;马尔科夫随机场;最大后验概率;统计信号处理

Keywords:

3D MRI denoising; Markov random fields; Maximum a posteriori; Statistical signal processing

阅读原文:PMID: 28173816  PMCID: PMC5297150  DOI: 10.1186/s12938-017-0319-x


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