NODDI-SH: a computational efficient NODDI extension for fODF estimation in diffusion MRI

Diffusion Magnetic Resonance Imaging (DMRI) is the only non-invasive imaging technique which is able to detect the principal directions of water diffusion as well as neurites density in the human brain. Exploiting the ability of Spherical Harmonics (SH) to model spherical functions, we propose a new reconstruction model for DMRI data which is able to estimate both the fiber Orientation Distribution Function (fODF) and the relative volume fractions of the neurites in each voxel, which is robust to multiple fiber crossings. We consider a Neurite Orientation Dispersion and Density Imaging (NODDI) inspired single fiber diffusion signal to be derived from three compartments: intracellular, extracellular, and cerebrospinal fluid. The model, called NODDI-SH, is derived by convolving the single fiber response with the fODF in each voxel. NODDI-SH embeds the calculation of the fODF and the neurite density in a unified mathematical model providing efficient, robust and accurate results. Results were validated on simulated data and tested on \textit{in-vivo} data of human brain, and compared to and Constrained Spherical Deconvolution (CSD) for benchmarking. Results revealed competitive performance in all respects and inherent adaptivity to local microstructure, while sensibly reducing the computational cost. We also investigated NODDI-SH performance when only a limited number of samples are available for the fitting, demonstrating that 60 samples are enough to obtain reliable results. The fast computational time and the low number of signal samples required, make NODDI-SH feasible for clinical application

Better Generalization of White Matter Tract Segmentation to Arbitrary Datasets with Scaled Residual Bootstrap

White matter (WM) tract segmentation is a crucial step for brain connectivity studies. It is performed on diffusion magnetic resonance imaging (dMRI), and deep neural networks (DNNs) have achieved promising segmentation accuracy. Existing DNN-based methods use an annotated dataset for model training. However, the performance of the trained model on a different test dataset may not be optimal due to distribution shift, and it is desirable to design WM tract segmentation approaches that allow better generalization of the segmentation model to arbitrary test datasets. In this work, we propose a WM tract segmentation approach that improves the generalization with scaled residual bootstrap. The difference between dMRI scans in training and test datasets is most noticeably caused by the different numbers of diffusion gradients and noise levels. Since both of them lead to different signal-to-noise ratios (SNRs) between the training and test data, we propose to augment the training scans by adjusting the noise magnitude and develop an adapted residual bootstrap strategy for the augmentation. To validate the proposed approach, two dMRI datasets were used, and the experimental results show that our method consistently improved the generalization of WM tract segmentation under various settings

Media do not exist : performativity and mediating conjunctures

Collection : Theory on demand ; 31Media Do Not Exist: Performativity and Mediating Conjunctures by Jean-Marc Larrue and Marcello Vitali-Rosati offers a radically new approach to the phenomenon of mediation, proposing a new understanding that challenges the very notion of medium. It begins with a historical overview of recent developments in Western thought on mediation, especially since the mid 80s and the emergence of the disciplines of media archaeology and intermediality. While these developments are inseparable from the advent of digital technology, they have a long history. The authors trace the roots of this thought back to the dawn of philosophy. Humans interact with their environment – which includes other humans – not through media, but rather through a series of continually evolving mediations, which Larrue and Vitali-Rosati call ‘mediating conjunctures’. This observation leads them to the paradoxical argument that ‘media do not exist’. Existing theories of mediation processes remain largely influenced by a traditional understanding of media as relatively stable entities. Media Do Not Exist demonstrates the limits of this conception. The dynamics relating to mediation are the product not of a single medium, but rather of a series of mediating conjunctures. They are created by ceaselessly shifting events and interactions, blending the human and the non-human, energy, and matter

Special Libraries, March 1944

Volume 35, Issue 3https://scholarworks.sjsu.edu/sla_sl_1944/1002/thumbnail.jp