基于多模型响应函数约束球面反卷积的纤维方向估计

Fiber direction estimation using constrained spherical deconvolution based on multi-model response function

约束球面反卷积可以从大脑扩散磁共振成像数据中量化白质纤维取向分布信息,该方法仅适用于单壳的扩散磁共振成像数据;在包含各向同性扩散信号的白质组织中,该方法会提供错误的纤维方向信息。针对这一不足,本文在约束球面反卷积的基础上,结合多壳数据和多种扩散模型下估计的响应函数,提出一种基于多模型响应函数的约束球面反卷积方法。多壳数据可以提高纤维方向估计的稳定性,多模型响应函数可以衰减脑白质中各向同性扩散信号,提供更加准确的纤维方向信息。为了验证算法的有效性,利用模拟数据和来自公开数据库的真实人脑数据进行对比实验。结果表明,本文算法可以衰减白质组织中的各向同性扩散信号,克服部分容积效应的影响,能更准确地进行纤维方向估计;重建的纤维方向分布稳定,伪峰少,而且对交叉纤维的识别能力也更强,为纤维束追踪技术的进一步研究奠定了基础。

Constrained spherical deconvolution can quantify white matter fiber orientation distribution information from diffusion magnetic resonance imaging data.But this method is only applicable to single shell diffusion magnetic resonance imaging data and will provide wrong fiber orientation information in white matter tissue which contains isotropic diffusion signals.To solve these problems,this paper proposes a constrained spherical deconvolution method based on multi-model response function.Multi-shell data can improve the stability of fiber orientation,and multimodel response function can attenuate isotropic diffusion signals in white matter,providing more accurate fiber orientation information.Synthetic data and real brain data from public database were used to verify the effectiveness of this algorithm.The results demonstrate that the proposed algorithm can attenuate isotropic diffusion signals in white matter and overcome the influence of partial volume effect on fiber direction estimation,thus estimate fiber direction more accurately.The reconstructed fiber direction distribution is stable,the false peaks are less,and the recognition ability of cross fiber is stronger,which lays a foundation for the further research of fiber bundle tracking technology.