EXULT
发表于 2025-3-25 04:06:29
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可耕种
发表于 2025-3-25 09:49:30
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esculent
发表于 2025-3-25 11:56:47
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裁决
发表于 2025-3-25 18:30:19
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无聊的人
发表于 2025-3-25 20:16:09
Visualization of Diffusion Propagator and Multiple Parameter Diffusion Signal multiple b-values, multiple orientations and multiple diffusion times. These new and demanding acquisitions go beyond classical diffusion tensor imaging (DTI) and single b-value high angular resolution diffusion imaging (HARDI) acquisitions. Recent studies show that such multiple parameter diffusio
anachronistic
发表于 2025-3-26 03:31:04
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HARD
发表于 2025-3-26 04:49:45
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BIAS
发表于 2025-3-26 09:03:14
Visualizing Symmetric Indefinite 2D Tensor Fields Using the Heat Kernel Signaturee, and multiscale nature, it has been successfully applied in many geometric applications. From a more general point of view, the HKS can be considered as a descriptor of the metric of a Riemannian manifold. Given a symmetric positive definite tensor field we may interpret it as the metric of some R
thalamus
发表于 2025-3-26 13:54:46
A Framework for the Analysis of Diffusion Compartment Imaging (DCI)e gained in vivo by means of diffusion-weighted imaging that is sensitive to the local patterns of diffusion of water molecules throughout the brain. Diffusion compartment imaging (DCI) provides a separate parameterization for the diffusion signal arising from each compartment of water molecules at
CUMB
发表于 2025-3-26 20:28:59
Statistical and Machine Learning Methods for Neuroimaging: Examples, Challenges, and Extensions to Dn. In this chapter, we survey two broad families of approaches to quantitative analysis of neuroimaging data: statistical testing and machine learning. We discuss how methods developed for traditional scalar structural neuroimaging data have been extended to diffusion magnetic resonance imaging data