Titlebook: Computational Diffusion MRI; MICCAI Workshop, Qué Enrico Kaden,Francesco Grussu,Jelle Veraart Conference proceedings 2018 Springer Internat

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书目名称Computational Diffusion MRI影响因子(影响力)




书目名称Computational Diffusion MRI影响因子(影响力)学科排名




书目名称Computational Diffusion MRI网络公开度




书目名称Computational Diffusion MRI网络公开度学科排名




书目名称Computational Diffusion MRI被引频次




书目名称Computational Diffusion MRI被引频次学科排名




书目名称Computational Diffusion MRI年度引用




书目名称Computational Diffusion MRI年度引用学科排名




书目名称Computational Diffusion MRI读者反馈




书目名称Computational Diffusion MRI读者反馈学科排名

安定

(,, ,)-Compressed Sensing for dMRI with Joint Spatial-Angular Sparsity PriorHARDI), remain underutilized compared to diffusion tensor imaging because the scan times needed to produce accurate estimations of fiber orientation are significantly longer. To accelerate DSI and HARDI, recent methods from compressed sensing (CS) exploit a sparse underlying representation of the da

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Spatio-Temporal dMRI Acquisition Design: Reducing the Number of , Samples Through a Relaxed Probabilcheme that maximizes signal quality and satisfies given time constraints is NP-hard. We alleviate that by introducing a relaxed probabilistic model of the problem, for which sub-optimal solutions can be found effectively. Our model is defined in the . space, so that it captures both spacial and temp

Observe

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LVAD360

Diffeomorphic Registration of Diffusion Mean Apparent Propagator Fields Using Dynamic Programming onescribing the dMRI data and the associated optimization method used to warp non-scalar images obtained from this model. In this paper, we take into account the full information available from the diffusion-weighted signal by using the local SHORE Mean Apparent Propagator (MAP) model. A discrete repr

LVAD360

Diffusion Orientation Histograms (DOH) for Diffusion Weighted Image Analysisor quantizes local diffusion gradients into histograms over spatial location and orientation, in a manner analogous to the quantization of image gradients in the widely used Histogram of Oriented Gradients(HOG) technique. Diffusion gradient symmetry allows representing half of the orientation space

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Learning a Single Step of Streamline Tractography Based on Neural Networks based on neural networks. We train 18 different classifiers in order to assess the effect of including neighbourhood information in the learning step or as a post processing step. Moreover, the performance using four different post processing approaches as well as the variation of the number of cla

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Probabilistic Tractography for Complex Fiber Orientations with Automatic Model Selection orientations in regions with multiple fiber populations, and the uncertainty in the fiber orientations as a result of noise. In this work, we use a range of multi-tensor models to cope with crossing fibers. The uncertainty in fiber orientation is captured using the Cramér-Rao lower bound. Furthermo

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