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Titlebook: Computational Diffusion MRI; MICCAI Workshop, Ath Andrea Fuster,Aurobrata Ghosh,Marco Reisert Conference proceedings 2017 Springer Internat

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書目名稱Computational Diffusion MRI
副標(biāo)題MICCAI Workshop, Ath
編輯Andrea Fuster,Aurobrata Ghosh,Marco Reisert
視頻videohttp://file.papertrans.cn/233/232241/232241.mp4
概述Careful mathematical derivations.Large number of rich full-color visualizations.Biologically or clinically relevant results
叢書名稱Mathematics and Visualization
圖書封面Titlebook: Computational Diffusion MRI; MICCAI Workshop, Ath Andrea Fuster,Aurobrata Ghosh,Marco Reisert Conference proceedings 2017 Springer Internat
描述.This volume offers a valuable starting point for anyone interested in learning computational diffusion MRI and mathematical methods for brain connectivity, while also sharing new perspectives and insights on the latest research challenges for those currently working in the field...Over the last decade, interest in diffusion MRI has virtually exploded. The technique provides unique insights into the microstructure of living tissue and enables in-vivo connectivity mapping of the brain. Computational techniques are key to the continued success and development of diffusion MRI and to its widespread transfer into the clinic, while new processing methods are essential to addressing issues at each stage of the diffusion MRI pipeline: acquisition, reconstruction, modeling and model fitting, image processing, fiber tracking, connectivity mapping, visualization, group studies and inference...These papers from the 2016 MICCAI Workshop “Computational Diffusion MRI” – which was intended to provide a snapshot of the latest developments within the highly active and growing field of diffusion MR – cover a wide range of topics, from fundamental theoretical work on mathematical modeling, to the dev
出版日期Conference proceedings 2017
關(guān)鍵詞fiber tractography; medical image analysis; neuroimaging; connectomics; inverse problems; brain network a
版次1
doihttps://doi.org/10.1007/978-3-319-54130-3
isbn_softcover978-3-319-85326-0
isbn_ebook978-3-319-54130-3Series ISSN 1612-3786 Series E-ISSN 2197-666X
issn_series 1612-3786
copyrightSpringer International Publishing AG 2017
The information of publication is updating

書目名稱Computational Diffusion MRI影響因子(影響力)




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書目名稱Computational Diffusion MRI網(wǎng)絡(luò)公開度




書目名稱Computational Diffusion MRI網(wǎng)絡(luò)公開度學(xué)科排名




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Noise Floor Removal via Phase Correction of Complex Diffusion-Weighted Images: Influence on DTI andffusion signal. As a consequence, also the estimated diffusion metrics can be biased. We study the effect of phase correction, a procedure that re-establishes the Gaussianity of the noise distribution in DWIs by taking into account the corresponding phase images. We quantify the debiasing effects of
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Regularized Dictionary Learning with Robust Sparsity Fitting for Compressed Sensing Multishell HARD with a proposed . term to handle low signal-to-noise ratios at high . values. We combine the dictionary model for diffusion signals together with a multiscale (wavelet-based) spatial model on images for compressed sensing. To control overfitting of the dictionary to tracts with unknown orientations
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Denoising Diffusion-Weighted Images Using Grouped Iterative Hard Thresholding of Multi-Channel Framve analysis, it is often desirable to remove noise and at the same time preserve relevant image features. In this paper, we propose a tight wavelet frame based approach for edge-preserving denoising of DW images. Our approach (1) employs the unitary extension principle (UEP) to generate frames that
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Sensitivity of OGSE ActiveAx to Microstructural Dimensions on a Clinical Scanner,of techniques to measure axon diameterusing diffusion MR I have been proposed, majority of which uses single diffusion encoding (SDE) spin-echo sequence. However, recent theoretical research suggests that low-frequency oscillating gradient spin echo(OGSE ) offers benefits over SDE for imaging diamet
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