By Lauren O'Donnell, Gemma Nedjati-Gilani, Yogesh Rathi, Marco Reisert, Torben Schneider
This publication includes papers offered on the 2014 MICCAI Workshop on Computational Diffusion MRI, CDMRI’14. Detailing new computational equipment utilized to diffusion magnetic resonance imaging information, it deals readers a photograph of the present cutting-edge and covers quite a lot of subject matters from basic theoretical paintings on mathematical modeling to the advance and overview of sturdy algorithms and purposes in neuroscientific reviews and scientific practice.
Inside, readers will locate details on mind community research, mathematical modeling for medical functions, tissue microstructure imaging, super-resolution tools, sign reconstruction, visualization, and extra. Contributions contain either cautious mathematical derivations and a great number of wealthy full-color visualizations.
Computational concepts are key to the ongoing luck and improvement of diffusion MRI and to its common move into the health center. This quantity will provide a useful start line for somebody attracted to studying computational diffusion MRI. It additionally deals new views and insights on present learn demanding situations for these at the moment within the box. The booklet should be of curiosity to researchers and practitioners in laptop technological know-how, MR physics, and utilized mathematics.
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Extra resources for Computational Diffusion MRI: MICCAI Workshop, Boston, MA, USA, September 2014
The correlation of metrics in complex networks with applications in functional brain networks. J. Stat. Mech. 2011(11), P11,018 (2011) 9. : Diffusion tensor tractography reveals abnormal topological organization in structural cortical networks in Alzheimer’s disease. J. Neurosci. 30(50), 16,876–85 (2010) 10. : Functional connectivity and brain networks in schizophrenia. J. Neurosci. 30(28), 9477–9487 (2010) 11. : Identifying population differences in whole-brain structural networks: a machine learning approach.
A k-d tree with a leaf size of 30 was used to perform the nearest neighbour search, with an Euclidean distance metric. To determine the robustness of the technique, various settings for the number of neighbours k and the neighbour weighting function w were used, as explained below in Sect. 4. J. Kuijf et al. Fig. 1 Example image data of a subject with a high white matter hyperintensity (WMH) load (WMH volume: 34 ml). The top row shows the intensity features, where WMH appear dark on T1 and IR, and bright on FLAIR.
R2 2 Œ0; 1 represents the amount of variation in the data explained by a given model and is equivalent to 1/0 if all/none of the variation in the data is captured by the model. We furthermore compared the results for R2 for the logarithmic model given by Eq. 1 to a linear model. The results are given in Table 2. Our results show that the logarithmic model explains on average 95 % of the variation within the data and outperforms a linear model for all network measures. 2 Determining an Independent Subset of Measures In a further test we analysed the non-linear correlations between the model parameters across the network measures.
Computational Diffusion MRI: MICCAI Workshop, Boston, MA, USA, September 2014 by Lauren O'Donnell, Gemma Nedjati-Gilani, Yogesh Rathi, Marco Reisert, Torben Schneider