A generalized order mixture model for tracing connectivity of white matter fascicles complexity in brain from diffusion MRI.

IF 0.8 4区 数学 Q4 BIOLOGY
Ashishi Puri, Sanjeev Kumar
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引用次数: 1

Abstract

This paper focuses on tracing the connectivity of white matter fascicles in the brain. In particular, a generalized order algorithm based on mixture of non-central Wishart distribution model is proposed for this purpose. The proposed algorithm utilizes the generalization of integer order based approach with the mixture of non-central Wishart distribution model. Pseudo super anomalous behavior of water diffusion inside human brain is the prime motivation of the the present study. We have shown results on multiple synthetic simulations with fibers orientations in two and three directions in each voxel as well as experiments on real data. Synthetic simulations were performed with varying noise levels and diffusion weighting gradient i.e. $b-$values. The proposed model performed outstanding especially for distinguishing closely oriented fibers.

从弥散核磁共振成像追踪脑白质束复杂性连接的广义阶混合物模型。
本文的重点是追踪大脑白质分册的连通性。为此,本文特别提出了一种基于非中心 Wishart 分布模型混合物的广义阶算法。所提出的算法利用了基于整数阶次方法的广义阶次与非中心 Wishart 分布模型的混合物。人脑内部水扩散的伪超反常行为是本研究的主要动机。我们展示了在每个体素的两个和三个方向上纤维方向的多个合成模拟结果,以及真实数据的实验结果。合成模拟是在不同的噪声水平和扩散加权梯度(即 $b-$值)下进行的。所提出的模型表现出色,尤其是在区分紧密定向的纤维方面。
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来源期刊
CiteScore
2.20
自引率
0.00%
发文量
15
审稿时长
>12 weeks
期刊介绍: Formerly the IMA Journal of Mathematics Applied in Medicine and Biology. Mathematical Medicine and Biology publishes original articles with a significant mathematical content addressing topics in medicine and biology. Papers exploiting modern developments in applied mathematics are particularly welcome. The biomedical relevance of mathematical models should be demonstrated clearly and validation by comparison against experiment is strongly encouraged. The journal welcomes contributions relevant to any area of the life sciences including: -biomechanics- biophysics- cell biology- developmental biology- ecology and the environment- epidemiology- immunology- infectious diseases- neuroscience- pharmacology- physiology- population biology
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