{"title":"Estimation of fiber orientation distributions in superficial white matter using an asymmetric constrained spherical deconvolution method","authors":"Jingxin Meng, Jianglin He, Yuanjun Wang","doi":"10.1016/j.jneumeth.2024.110353","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>Superficial white matter is an important component of white matter. Estimation of fiber orientation distributions based on diffusion magnetic resonance imaging is a critical step in white matter tractography imaging. However, due to the complex structure of superficial white matter, existing models for estimating fiber orientation distributions are ineffective in reconstructing superficial white matter and even reconstruct incorrect orientation distributions.</div></div><div><h3>New method</h3><div>In this paper, we improve the traditional constrained spherical deconvolution method and propose a novel asymmetric constrained spherical deconvolution method. The method takes into account that the displacement profile of the water molecules in brain tissue are non-Gaussian diffusion and the core parameter kurtosis might characterize tissue structure better than diffusivity coefficients. So diffusion kurtosis imaging model is used to estimate the white matter response function. The proposed method applies the diffusion kurtosis imaging model response function to the asymmetric fiber orientation distributions, and this is the first attempt to obtain more accurate fiber orientation distributions. Furthermore, the Gaussian-Distribution distance weight and Watson-Distribution angle weight are used for asymmetric regularization.</div></div><div><h3>Results</h3><div>We evaluate the method using FiberCup phantom, ISMRM 2015 data and in vivo data provided CHCP dataset. The results show that our proposed method can more accurately reconstruct the complex fiber structure of superficial white matter with more accurate fiber orientation, fewer pseudo-peaks, and mitigate gyral bias.</div></div><div><h3>Comparison with existing methods</h3><div>Our proposed method has higher accuracy in estimating the fiber orientation distributions and can reconstruct highly curved fiber voxels.</div></div><div><h3>Conclusion</h3><div>This proposed method provides new insights into the estimation of the orientation distribution of superficial white matter fibers.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"415 ","pages":"Article 110353"},"PeriodicalIF":2.7000,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Neuroscience Methods","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S016502702400298X","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
Background
Superficial white matter is an important component of white matter. Estimation of fiber orientation distributions based on diffusion magnetic resonance imaging is a critical step in white matter tractography imaging. However, due to the complex structure of superficial white matter, existing models for estimating fiber orientation distributions are ineffective in reconstructing superficial white matter and even reconstruct incorrect orientation distributions.
New method
In this paper, we improve the traditional constrained spherical deconvolution method and propose a novel asymmetric constrained spherical deconvolution method. The method takes into account that the displacement profile of the water molecules in brain tissue are non-Gaussian diffusion and the core parameter kurtosis might characterize tissue structure better than diffusivity coefficients. So diffusion kurtosis imaging model is used to estimate the white matter response function. The proposed method applies the diffusion kurtosis imaging model response function to the asymmetric fiber orientation distributions, and this is the first attempt to obtain more accurate fiber orientation distributions. Furthermore, the Gaussian-Distribution distance weight and Watson-Distribution angle weight are used for asymmetric regularization.
Results
We evaluate the method using FiberCup phantom, ISMRM 2015 data and in vivo data provided CHCP dataset. The results show that our proposed method can more accurately reconstruct the complex fiber structure of superficial white matter with more accurate fiber orientation, fewer pseudo-peaks, and mitigate gyral bias.
Comparison with existing methods
Our proposed method has higher accuracy in estimating the fiber orientation distributions and can reconstruct highly curved fiber voxels.
Conclusion
This proposed method provides new insights into the estimation of the orientation distribution of superficial white matter fibers.
期刊介绍:
The Journal of Neuroscience Methods publishes papers that describe new methods that are specifically for neuroscience research conducted in invertebrates, vertebrates or in man. Major methodological improvements or important refinements of established neuroscience methods are also considered for publication. The Journal''s Scope includes all aspects of contemporary neuroscience research, including anatomical, behavioural, biochemical, cellular, computational, molecular, invasive and non-invasive imaging, optogenetic, and physiological research investigations.