Αποτελέσματα Αναζήτησης
1 Δεκ 2020 · Multimodal fusion in neuroimaging combines data from multiple imaging modalities to overcome the fundamental limitations of individual modalities. Neuroimaging fusion can achieve higher temporal and spatial resolution, enhance contrast, correct imaging distortions, and bridge physiological and cognitive information.
- Reconfigurable MRI Technology for low-SAR Imaging of Deep Brain Stimulation at 3T
This excludes sequences that are essential to rule out...
- Changes After Treatment With Xanomeline
These data are reported separately, by active Drug vs....
- A Magnetic Resonance Spectroscopy Study at 7 Tesla
1. Introduction. Alzheimer's disease (AD) is the most common...
- 3 Through 60 Months of Age
In this work, we investigate regional and whole brain growth...
- Processing of Structural Neuroimaging Data in Young Children
The structure of the brain is subject to very rapid...
- Hippocampal Metabolic Abnormalities in Mild Cognitive Impairment and Alzheimer's Disease
Mild cognitive impairment (MCI) is thought to represent a...
- MCI
The data were analyzed by using SPSS ® 13.0 and MedCalc ®...
- Central Nervous System Abnormalities Assessed With Prenatal Magnetic Resonance Imaging
Central nervous system (CNS) abnormalities affect...
- Reconfigurable MRI Technology for low-SAR Imaging of Deep Brain Stimulation at 3T
2 Ιουν 2018 · Since the development of CT and MRI, the field of neuroimaging has exploded. Advanced imaging techniques are now firmly integrated into clinical practice, allowing to visualize the brain not only as an organ but also at the level of axons and fiber tracts.
30 Σεπ 2021 · Here we describe a minimally invasive intravital imaging methodology based on three-photon excitation, indirect adaptive optics (AO) and active electrocardiogram gating to advance deep-tissue...
4 Σεπ 2021 · To do that, scientists and engineers have developed an array of methods capable of looking at whole-brain activity from a zoomed-out perspective. In this review, we aim to provide the reader...
8 Νοε 2023 · By tracking whole-brain signals across a diverse range of cognitive and behavioural states or mapping differences associated with specific traits or clinical conditions, fMRI has advanced our...
Likewise, Li et al. applied GANs to augment brain data sets by generating paired data which were used to train and test different DL-based segmentation techniques. Kim et al. [ 37 ] used a model called Boundary Equilibrium Generative Adversarial Network (BEGAN) to extract features of Alzheimer’s disease and normal cognitive brain 18F FDG PET/CT.
Brain image segmentation is one of the most time-consuming and challenging procedures in a clinical environment. Recently, a drastic increase in the number of brain disorders has been noted.
