BOGUS
发表于 2025-3-25 05:53:51
Ermittlungsmöglichkeiten der Steuerfahndungical applicability, we present a novel, efficient algorithm for region-to-region geodesic tractography which extends existing point-to-point algorithms and incorporates anatomical knowledge by assuming a topographic organization of fibers. The proposed method connects only seed and target voxels tha
Notorious
发表于 2025-3-25 07:33:40
Auslöser für Ermittlungen der Steuerfahndungg., the FSL XTRACT toolbox, provides an alternative method of ROI analysis by estimating tract regions in an individual native diffusion space, but the exact advantages and disadvantages compared to using a standard space have not been well documented. In the present study, we perform ROI analysis o
Spina-Bifida
发表于 2025-3-25 13:54:09
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行业
发表于 2025-3-25 17:13:35
Grundzüge des Ermittlungsverfahrenst to account for brain lesions and deformations, four preprocessing strategies are applied to dMRI, including the novel application of a lesion normalization technique to dMRI. The pipeline involving the lesion normalization technique provides the best prediction performance, with a mean accuracy of
轻而薄
发表于 2025-3-25 21:52:07
https://doi.org/10.1007/978-3-322-90596-3crostructure. We report the ground-truth tissue volume fractions (“intra-axonal”, “extra-axonal”, “myelin”), the fibre density, the bundle density and the fibre orientation distributions (FODs). We believe that this characterization will be beneficial for validating quantitative structural connectiv
欢呼
发表于 2025-3-26 01:20:49
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avarice
发表于 2025-3-26 05:52:00
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BARK
发表于 2025-3-26 10:01:12
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Freeze
发表于 2025-3-26 16:23:12
Generalised Hierarchical Bayesian Microstructure Modelling for Diffusion MRIrostructural models, and fit the models with a Markov chain Monte Carlo (MCMC) algorithm. We implement our method by utilising Dmipy, a microstructure modelling software package for diffusion MRI data. Our code is available at github.com/PaddySlator/dmipy-bayesian.
optional
发表于 2025-3-26 18:50:16
Brain Tissue Microstructure Characterization Using dMRI Based Autoencoder Neural-Networks data fidelity and the number of microstructural features. Our results show how this number is impacted by the number of shells and the .-values used to sample the dMRI signal. We also show how our technique paves the way to a richer characterization of the brain tissue microstructure in-vivo.