Vectorial total variation regularisation of orientation distribution functions in diffusion weighted MRI.

Q4 Health Professions

International Journal of Bioinformatics Research and Applications Pub Date : 2014-01-01 DOI:10.1504/IJBRA.2014.058781

Yuyuan Ouyang, Yunmei Chen, Ying Wu

引用次数: 6

Abstract

We propose a model for simultaneous Orientation Distribution Function (ODF) reconstruction and regularisation. The ODFs are represented by real spherical harmonic functions, and we propose to solve the spherical harmonic coefficients of the ODFs, with spatial regularisation by minimising the Vectorial Total Variation (VTV) of the coefficients. The proposed model also incorporates angular regularisation of the ODFs using Laplace-Beltrami operator on the unit sphere. A modified primal-dual hybrid gradient algorithm is applied to solve the model efficiently. The experimental results indicate better directional structures of reconstructed ODFs.

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扩散加权MRI中方向分布函数的矢量总变分正则化。

提出了一种同时重建和正则化方向分布函数(ODF)的模型。odf用实数球面调和函数表示，我们提出了通过最小化系数的矢量总变差(VTV)来求解odf的球面调和系数的空间正则化。该模型还在单位球上使用Laplace-Beltrami算子对odf进行了角正则化。采用一种改进的原始-对偶混合梯度算法对模型进行有效求解。实验结果表明，重构后的odf具有更好的定向结构。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Bioinformatics Research and Applications Health Professions-Health Information Management

CiteScore

0.60

自引率

0.00%

发文量

期刊介绍： Bioinformatics is an interdisciplinary research field that combines biology, computer science, mathematics and statistics into a broad-based field that will have profound impacts on all fields of biology. The emphasis of IJBRA is on basic bioinformatics research methods, tool development, performance evaluation and their applications in biology. IJBRA addresses the most innovative developments, research issues and solutions in bioinformatics and computational biology and their applications. Topics covered include Databases, bio-grid, system biology Biomedical image processing, modelling and simulation Bio-ontology and data mining, DNA assembly, clustering, mapping Computational genomics/proteomics Silico technology: computational intelligence, high performance computing E-health, telemedicine Gene expression, microarrays, identification, annotation Genetic algorithms, fuzzy logic, neural networks, data visualisation Hidden Markov models, machine learning, support vector machines Molecular evolution, phylogeny, modelling, simulation, sequence analysis Parallel algorithms/architectures, computational structural biology Phylogeny reconstruction algorithms, physiome, protein structure prediction Sequence assembly, search, alignment Signalling/computational biomedical data engineering Simulated annealing, statistical analysis, stochastic grammars.