Titlebook: Diffusion Tensor Imaging; A Practical Handbook Wim Van Hecke,Louise Emsell,Stefan Sunaert Book 2016 Springer Science+Business Media New Yor

慌張

Introduction to Diffusion Tensor ImagingI technique, and example applications. Special attention is given to the “DTI pipeline,” from defining the purpose of DTI data collection, optimizing data acquisition, processing and analysis, through to how to interpret DTI-based findings. The chapter concludes with a discussion on the potential role of DTI in future clinical practice.

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From Diffusion to the Diffusion Tensorof DTI: obtaining these so-called diffusion tensors. What are they actually modeling? And, in this context, what . a tensor anyway? There’s a short and clear answer to this: the diffusion tensor describes the apparent diffusion coefficient (ADC), in function of direction. Hmm… “ADC” you say…?

清醒

高興一回

DTI in Neurosurgical Planningry information. This chapter reviews potential neurosurgical applications of DTI, important limitations of the technique in this context, and the practical aspects of using DTI in neurosurgical planning.

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內(nèi)部

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Concepts of Diffusion in MRIof obstacles on diffusion in biological tissue and the basic principles of configuring a magnetic resonance imaging machine to be sensitive to diffusion phenomena. We cover the important microstructural properties of central nervous system tissue and their impact on the diffusion characteristics of

帽子

From Diffusion to the Diffusion Tensora formal point of view, however, DTI is the practice of fitting a tensor model to the DWI data. It is one of the simplest ways to model the DWI data that accounts, up to some extent, for the anisotropy in this kind of data. Exploiting this anisotropy is key to obtaining the characteristic directiona

LUDE

Decongestant