AIMC Topic: Brain

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Brain CT registration using hybrid supervised convolutional neural network.

Biomedical engineering online
BACKGROUND: Image registration is an essential step in the automated interpretation of the brain computed tomography (CT) images of patients with acute cerebrovascular disease (ACVD). However, performing brain CT registration accurately and rapidly r...

Rigid motion-resolved prediction using deep learning for real-time parallel-transmission pulse design.

Magnetic resonance in medicine
PURPOSE: Tailored parallel-transmit (pTx) pulses produce uniform excitation profiles at 7 T, but are sensitive to head motion. A potential solution is real-time pulse redesign. A deep learning framework is proposed to estimate pTx distributions foll...

Evaluation on the generalization of a learned convolutional neural network for MRI reconstruction.

Magnetic resonance imaging
Recently, deep learning approaches with various network architectures have drawn significant attention from the magnetic resonance imaging (MRI) community because of their great potential for image reconstruction from undersampled k-space data in fas...

Structural and functional brain networks of individual differences in trait anger and anger control: An unsupervised machine learning study.

The European journal of neuroscience
The ability to experience, use and eventually control anger is crucial to maintain well-being and build healthy relationships. Despite its relevance, the neural mechanisms behind individual differences in experiencing and controlling anger are poorly...

A cross-scanner and cross-tracer deep learning method for the recovery of standard-dose imaging quality from low-dose PET.

European journal of nuclear medicine and molecular imaging
PURPOSE: A critical bottleneck for the credibility of artificial intelligence (AI) is replicating the results in the diversity of clinical practice. We aimed to develop an AI that can be independently applied to recover high-quality imaging from low-...

Subtyping of mild cognitive impairment using a deep learning model based on brain atrophy patterns.

Cell reports. Medicine
Trajectories of cognitive decline vary considerably among individuals with mild cognitive impairment (MCI). To address this heterogeneity, subtyping approaches have been developed, with the objective of identifying more homogeneous subgroups. To date...

Brain tissue segmentation via non-local fuzzy c-means clustering combined with Markov random field.

Mathematical biosciences and engineering : MBE
The segmentation and extraction of brain tissue in magnetic resonance imaging (MRI) is a meaningful task because it provides a diagnosis and treatment basis for observing brain tissue development, delineating lesions, and planning surgery. However, M...

A stroke detection and discrimination framework using broadband microwave scattering on stochastic models with deep learning.

Scientific reports
Stroke poses an immense public health burden and remains among the primary causes of death and disability worldwide. Emergent therapy is often precluded by late or indeterminate times of onset before initial clinical presentation. Rapid, mobile, safe...

A CNN-transformer hybrid approach for decoding visual neural activity into text.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Most studies used neural activities evoked by linguistic stimuli such as phrases or sentences to decode the language structure. However, compared to linguistic stimuli, it is more common for the human brain to perceive the o...

Decoding Color Visual Working Memory from EEG Signals Using Graph Convolutional Neural Networks.

International journal of neural systems
Color has an important role in object recognition and visual working memory (VWM). Decoding color VWM in the human brain is helpful to understand the mechanism of visual cognitive process and evaluate memory ability. Recently, several studies showed ...