高角分辨率扩散成像的上域尺度空间和正则化

L. Florack
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引用次数: 21

摘要

正则化是高角分辨率扩散成像(HARDI)的一个重要方面,因为与经典扩散张量成像(DTI)不同,原始数据在上域中没有先验的规律性,即被视为固定空间位置的多光谱信号。因此,HARDI预处理是任何后续分析之前的关键步骤,并且必须对正则化范式及其相互关系有所了解。本文提出了一种上域尺度空间正则化范式,该范式迄今尚未应用于HARDI。不同于以前的(一阶和二阶)方案,它基于无限阶正则化,但可以完全可操作。进一步利用拉普拉斯变换建立了一阶Tikhonov正则化的闭形式关系。
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Codomain scale space and regularization for high angular resolution diffusion imaging
Regularization is an important aspect in high angular resolution diffusion imaging (HARDI), since, unlike with classical diffusion tensor imaging (DTI), there is no a priori regularity of raw data in the co-domain, i.e. considered as a multispectral signal for fixed spatial position. HARDI preprocessing is therefore a crucial step prior to any subsequent analysis, and some insight in regularization paradigms and their interrelations is compulsory. In this paper we posit a codomain scale space regularization paradigm that has hitherto not been applied in the context of HARDI. Unlike previous (first and second order) schemes it is based on infinite order regularization, yet can be fully operationalized. We furthermore establish a closed-form relation with first order Tikhonov regularization via the Laplace transform.
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