Results 181 to 190 of about 148,752 (225)

Altered Cerebrospinal Fluid Tryptophan–Kynurenine Pathway Metabolism in Multiple System Atrophy

Movement Disorders, EarlyView.
Abstract Background Alterations in tryptophan–kynurenine (TRP‐KYN) metabolism, which is associated with neuroinflammation, remain unclear in multiple system atrophy (MSA). Objective The aim was to investigate cerebrospinal fluid (CSF) TRP metabolites in MSA and their associations with other biomarkers.
Ryunosuke Nagao, Kazuya Kawabata, Yasuaki Mizutani, Sayuri Shima, Akihiro Ueda, Mizuki Ito, Yasuhiro Maeda, Akihiro Mouri, Hirohisa Watanabe   +8 more
wiley   +1 more source

Informed Dictionary‐Guided Monte Carlo Inversion for Robust and Reproducible Multidimensional MRI

Magnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose To develop a robust and efficient multidimensional MRI (MD‐MRI) data processing framework for accurately estimating joint frequency‐dependent diffusion‐relaxation distributions, while overcoming computational limitations and noise instability inherent to Monte Carlo (MC) inversion.
Joon Sik Park, Eppu Manninen, Yihong Yang, Dan Benjamini   +3 more
wiley   +1 more source

Field Strength‐Dependent White Matter R1 and R2 Anisotropy of Phase‐Cycled Balanced Steady‐State Free Precession Relaxometry

Magnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose To investigate how the relaxation rates (R1, R2) and asymmetry indices (AI), derived from phase‐cycled balanced steady‐state free precession (pc‐bSSFP) data, depend on the orientation of white matter (WM) fiber tracts at different field strengths.
Florian Birk, Hamzeh Tesh, Ali Aghaeifar, Svenja Klinkowski, Praveen Iyyappan Valsala, Sebastian Mueller, Svenja Brodt, Klaus Scheffler, Rahel Heule   +8 more
wiley   +1 more source

Spectral Representation of Neurochemicals With Phase, Frequency Offset, and Lineshape Invariance: Application to JPRESS for In Vivo Concentration and T2 Mapping by Deep Learning

Magnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose Using artificial intelligence neural networks to generate a representation that maps the input directly to neurochemical concentrations and metabolite‐level average transverse relaxation times (T2). Methods The proposed model used time‐domain JPRESS data as input and was trained to be invariant to phase shifts, frequency offsets, and ...
Yan Zhang, Jun Shen
wiley   +1 more source

Development and Validation of a Nomogram for Predicting Urinary Tract Infection After Urodynamic Study

Neurourology and Urodynamics, EarlyView.
ABSTRACT Introduction The use of antibiotic prophylaxis before urodynamic studies has been debated for a long time, with no clear consensus among international guidelines. Based on identified predictors of urinary tract infection after urodynamic studies, this study aims to develop and internally validate a nomogram to predict post‐urodynamic study ...
Omri Schwarztuch Gildor, Elad Yosef, Netanel Levin, Anna Itshak, Rony Vainrib, Amos Neheman, Michael Vainrib   +6 more
wiley   +1 more source

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Resampled Regenerative Estimators

ACM Transactions on Modeling and Computer Simulation, 2015
We discuss some estimators for simulations of processes having multiple regenerative sequences. The estimators are obtained by resampling trajectories without and with replacement, which correspond to a type of U -statistic and a type of V -statistic, respectively. The U
Calvin, James M., Nakayama, Marvin K.
openaire   +2 more sources

Biclustering by Resampling

2011
The search for similarities in large data sets has a very important role in many scientific fields. It permits to classify several types of data without an explicit information about it. In many cases researchers use analysis methodologies such as clustering to classify data with respect to the patterns and conditions together.
E. NOSOVA, R. TAGLIAFERRI, MASULLI, FRANCESCO, ROVETTA, STEFANO   +3 more
openaire   +4 more sources

Image resampling

1992
Image resampling is the process of geometrically transforming digital images. This report considers several aspects of the process. We begin by decomposing the resampling process into three simpler sub-processes: reconstruction of a continuous intensity surface from a discrete image, transformation of that continuous surface, and sampling of the ...
openaire   +1 more source

Resampling

2023
Rituparna Sen, Sourish Das
openaire   +1 more source

Resampling

2022
openaire   +1 more source