Zobrazeno 1 - 10
of 2 824
pro vyhledávání: '"Novikov, A. S."'
Water diffusion gives rise to micrometer-scale sensitivity of diffusion MRI (dMRI) to cellular-level tissue structure. The advent of precision medicine and quantitative imaging hinges on revealing the information content of dMRI, and providing its pa
Externí odkaz:
http://arxiv.org/abs/2409.03010
Autor:
Ades-Aron, Benjamin, Coelho, Santiago, Lemberskiy, Gregory, Veraart, Jelle, Baete, Steven, Shepherd, Timothy M., Novikov, Dmitry S., Fieremans, Els
The clinical translation of diffusion MRI (dMRI)-derived quantitative contrasts hinges on robust reproducibility, minimizing both same-scanner and cross-scanner variability. This study evaluates the reproducibility of higher-order diffusion metrics (
Externí odkaz:
http://arxiv.org/abs/2407.18253
Atomistic modeling is a widely employed theoretical method of computational materials science. It has found particular utility in the study of magnetic materials. Initially, magnetic empirical interatomic potentials or spin-polarized density function
Externí odkaz:
http://arxiv.org/abs/2405.12544
Autor:
Kotykhov, Alexey S., Gubaev, Konstantin, Sotskov, Vadim, Tantardini, Christian, Hodapp, Max, Shapeev, Alexander V., Novikov, Ivan S.
We developed a method for fitting machine-learning interatomic potentials with magnetic degrees of freedom, namely, magnetic Moment Tensor Potentials (mMTP). The main feature of our method consists in fitting mMTP to magnetic forces (negative derivat
Externí odkaz:
http://arxiv.org/abs/2405.07069
Transition-metal compounds represent a fascinating playground for exploring the intricate relationship between structural distortions, electronic properties, and magnetic behaviour, holding significant promise for technological advancements. Among th
Externí odkaz:
http://arxiv.org/abs/2404.19343
Autor:
Coelho, Santiago, Liao, Ying, Szczepankiewicz, Filip, Veraart, Jelle, Chung, Sohae, Lui, Yvonne W., Novikov, Dmitry S., Fieremans, Els
Joint modeling of diffusion and relaxation has seen growing interest due to its potential to provide complementary information about tissue microstructure. For brain white matter, we designed an optimal diffusion-relaxometry MRI protocol that samples
Externí odkaz:
http://arxiv.org/abs/2402.17175
Autor:
Coronado-Leija, Ricardo, Abdollahzadeh, Ali, Lee, Hong-Hsi, Coelho, Santiago, Ades-Aron, Benjamin, Liao, Ying, Salo, Raimo A., Tohka, Jussi, Sierra, Alejandra, Novikov, Dmitry S., Fieremans, Els
Biophysical modeling of diffusion MRI (dMRI) offers the exciting potential of bridging the gap between the macroscopic MRI resolution and microscopic cellular features, effectively turning the MRI scanner into a noninvasive in vivo microscope. In bra
Externí odkaz:
http://arxiv.org/abs/2310.04608
Autor:
Liao, Ying, Coelho, Santiago, Chen, Jenny, Ades-Aron, Benjamin, Pang, Michelle, Osorio, Ricardo, Shepherd, Timothy, Lui, Yvonne W., Novikov, Dmitry S., Fieremans, Els
Publikováno v:
Imaging Neuroscience, 2, 1-17, 2024
Diffusion magnetic resonance imaging offers unique in vivo sensitivity to tissue microstructure in brain white matter, which undergoes significant changes during development and is compromised in virtually every neurological disorder. Yet, the challe
Externí odkaz:
http://arxiv.org/abs/2307.16386
Autor:
Chen, Jenny, Ades-Aron, Benjamin, Lee, Hong-Hsi, Mehrin, Subah, Pang, Michelle, Novikov, Dmitry S., Veraart, Jelle, Fieremans, Els
Various diffusion MRI (dMRI) preprocessing pipelines are currently available to yield more accurate diffusion parameters. Here, we evaluated accuracy and robustness of the optimized Diffusion parameter EStImation with Gibbs and NoisE Removal (DESIGNE
Externí odkaz:
http://arxiv.org/abs/2305.14445
Autor:
Aliev, Timur A., Belyaev, Vadim E., Pomytkina, Anastasiya V., Nesterov, Pavel V., Shityakov, Sergei V., Sadovnychiy, Roman V., Novikov, Alexander S., Orlova, Olga Yu., Masalovich, Maria S., Skorb, Ekaterina V.
Present study is dedicated to the problem of electrochemical analysis of multicomponent mixtures such as milk. A combination of cyclic voltammetry facilities and machine learning technique made it possible to create a pattern recognition system for a
Externí odkaz:
http://arxiv.org/abs/2212.04422