Results 71 to 80 of about 19,436 (200)

Simulation‐Informed Evaluation of Microvascular Parameter Mapping for Diffusion MR Imaging of Solid Tumours

Magnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose We aim to inform the design of new diffusion MRI (dMRI) approaches for microvasculature quantification that enhance the biological specificity of imaging towards cancer. Methods We adopted simulation‐informed modelling of the vascular dMRI signal. We synthesised signals from 1500 synthetic vascular networks, for a variety of protocols (
Anna Kira Voronova, Olivia Prior, Athanasios Grigoriou, Francesc Salvà, Elena Elez, Luz M. Atlagich, Roser Sala‐Llonch, Marco Palombo, Els Fieremans, Dmitry S. Novikov, Raquel Perez‐Lopez, Francesco Grussu   +11 more
wiley   +1 more source

Application of Diffusion Tensor Imaging Parameters to Detect Change in Longitudinal Studies in Cerebral Small Vessel Disease. [PDF]

PLoS ONE, 2016
Cerebral small vessel disease (SVD) is the major cause of vascular cognitive impairment, resulting in significant disability and reduced quality of life.
Eva Anna Zeestraten, Philip Benjamin, Christian Lambert, Andrew John Lawrence, Owen Alan Williams, Robin Guy Morris, Thomas Richard Barrick, Hugh Stephen Markus   +7 more
doaj   +1 more source

The Contribution of White Matter Diffusion and Cortical Perfusion Pathology to Vascular Cognitive Impairment: A Multimode Imaging-Based Machine Learning Study

Frontiers in Aging Neuroscience, 2021
Widespread impairments in white matter and cerebrovascular integrity have been consistently implicated in the pathophysiology of patients with small vessel disease (SVD).
Yao Wang, Peiwen Lu, Yafeng Zhan, Xiaowei Wu, Yage Qiu, Zheng Wang, Qun Xu, Yan Zhou   +7 more
doaj   +1 more source

Enhancing generalized spectral clustering with embedding Laplacian graph regularization

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract An enhanced generalised spectral clustering framework that addresses the limitations of existing methods by incorporating the Laplacian graph and group effect into a regularisation term is presented. By doing so, the framework significantly enhances discrimination power and proves highly effective in handling noisy data.
Hengmin Zhang, Jian Yang, Bob Zhang, Yang Tang, Wenli Du, Bihan Wen   +5 more
wiley   +1 more source

Tensor Decomposition Through Neural Architectures

Applied Sciences
Machine learning (ML) technologies are currently widely used in many domains of science and technology, to discover models that transform input data into output data.
Chady Ghnatios, Francisco Chinesta
doaj   +1 more source

The effect of the total small vessel disease burden on the structural brain network

Scientific Reports, 2018
Different cerebral small vessel disease (SVD) lesion types have been shown to disrupt structural brain network individually. Considering that they often coexist, we investigated the relation between their collective effect using the recently proposed ...
Xiaopei Xu, Kui Kai Lau, Yuen Kwun Wong, Henry K. F. Mak, Edward S. Hui   +4 more
doaj   +1 more source

Tensor Changepoint Detection and Eigenbootstrap

Journal of Time Series Analysis, EarlyView.
ABSTRACT Tensor data consisting of multivariate outcomes over the items and across the subjects with longitudinal and cross‐sectional dependence are considered. A completely distribution‐free and tweaking‐parameter‐free detection procedure for changepoints at different locations is designed, which does not require training data.
Michal Pešta, Barbora Peštová, Martin Romaňák   +2 more
wiley   +1 more source

AG-LRTR: An Adaptive and Generic Low-Rank Tensor-Based Recovery for IIoT Network Traffic Factors Denoising

IEEE Access, 2022
The ubiquitous 5G-enable industrial Internet of Things interconnects a great number of intelligent sensors and actuators. Network management becomes challenging due to massive traffic data generated by industrial equipment.
Gang Yue, Zhuo Sun, Jinpo Fan
doaj   +1 more source

Tensor-SVD Based Graph Learning for Multi-View Subspace Clustering

Proceedings of the AAAI Conference on Artificial Intelligence, 2020
Low-rank representation based on tensor-Singular Value Decomposition (t-SVD) has achieved impressive results for multi-view subspace clustering, but it does not well deal with noise and illumination changes embedded in multi-view data. The major reason is that all the singular values have the same contribution in tensor-nuclear norm based on t-SVD ...
Quanxue Gao, Wei Xia, Zhizhen Wan, Deyan Xie, Pu Zhang   +4 more
openaire   +2 more sources

Building a Digital Twin for Material Testing: Model Reduction and Data Assimilation

Proceedings in Applied Mathematics and Mechanics, Volume 26, Issue 2, June 2026.
ABSTRACT The rapid advancement of industrial technologies, data collection, and handling methods has paved the way for the widespread adoption of digital twins (DTs) in engineering, enabling seamless integration between physical systems and their virtual counterparts.
Rubén Aylwin, Nina Beranek, Gajendra Babu Budihala, Matthias Mettenleiter, Goksu Oruc, Karsten Urban   +5 more
wiley   +1 more source