AIMC Topic: Brain

Clear Filters Showing 2201 to 2210 of 4413 articles

Simultaneous brain structure segmentation in magnetic resonance images using deep convolutional neural networks.

Radiological physics and technology
In brain magnetic resonance imaging (MRI) examinations, rapidly acquired two-dimensional (2D) T1-weighted sagittal slices are typically used to confirm brainstem atrophy and the presence of signals in the posterior pituitary gland. Image segmentation...

A deep learning approach for magnetic resonance fingerprinting: Scaling capabilities and good training practices investigated by simulations.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
MR fingerprinting (MRF) is an innovative approach to quantitative MRI. A typical disadvantage of dictionary-based MRF is the explosive growth of the dictionary as a function of the number of reconstructed parameters, an instance of the curse of dimen...

A database and deep learning toolbox for noise-optimized, generalized spike inference from calcium imaging.

Nature neuroscience
Inference of action potentials ('spikes') from neuronal calcium signals is complicated by the scarcity of simultaneous measurements of action potentials and calcium signals ('ground truth'). In this study, we compiled a large, diverse ground truth da...

A 3D deep learning model to predict the diagnosis of dementia with Lewy bodies, Alzheimer's disease, and mild cognitive impairment using brain 18F-FDG PET.

European journal of nuclear medicine and molecular imaging
PURPOSE: The purpose of this study is to develop and validate a 3D deep learning model that predicts the final clinical diagnosis of Alzheimer's disease (AD), dementia with Lewy bodies (DLB), mild cognitive impairment due to Alzheimer's disease (MCI-...

Neural and computational mechanisms of momentary fatigue and persistence in effort-based choice.

Nature communications
From a gym workout, to deciding whether to persevere at work, many activities require us to persist in deciding that rewards are 'worth the effort' even as we become fatigued. However, studies examining effort-based decisions typically assume that th...

Generation of synthetic PET images of synaptic density and amyloid from F-FDG images using deep learning.

Medical physics
PURPOSE: Positron emission tomography (PET) imaging with various tracers is increasingly used in Alzheimer's disease (AD) studies. However, access to PET scans using new or less-available tracers with sophisticated synthesis and short half-life isoto...

Graph-theory based degree centrality combined with machine learning algorithms can predict response to treatment with antiepileptic medications in children with epilepsy.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
BACKGROUND AND PURPOSE: The purpose of the current study is to detect changes of graph-theory-based degree centrality (DC) and their relationship with the clinical treatment effects of anti-epileptic drugs (AEDs) for patients with childhood absence e...

Bayesian Fully Convolutional Networks for Brain Image Registration.

Journal of healthcare engineering
The purpose of medical image registration is to find geometric transformations that align two medical images so that the corresponding voxels on two images are spatially consistent. Nonrigid medical image registration is a key step in medical image p...

Deep grey matter quantitative susceptibility mapping from small spatial coverages using deep learning.

Zeitschrift fur medizinische Physik
INTRODUCTION: Quantitative Susceptibility Mapping (QSM) is generally acquired with full brain coverage, even though many QSM brain-iron studies focus on the deep grey matter (DGM) region only. Reducing the spatial coverage to the DGM vicinity can sub...

Deep Learning with Neuroimaging and Genomics in Alzheimer's Disease.

International journal of molecular sciences
A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer's disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous...