AIMC Topic: Brain

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Denoising low-field MR images with a deep learning algorithm based on simulated data from easily accessible open-source software.

Journal of magnetic resonance (San Diego, Calif. : 1997)
In this study, we introduce a denoising method aimed at improving the contrast ratio in low-field MRI (LFMRI) using an advanced 3D deep convolutional residual network model. Our approach employs synthetic brain imaging datasets that closely mimic the...

Estimating global phase synchronization by quantifying multivariate mutual information and detecting network structure.

Neural networks : the official journal of the International Neural Network Society
In neuroscience, phase synchronization (PS) is a crucial mechanism that facilitates information processing and transmission between different brain regions. Specifically, global phase synchronization (GPS) characterizes the degree of PS among multiva...

3D full-dose brain-PET volume recovery from low-dose data through deep learning: quantitative assessment and clinical evaluation.

European radiology
OBJECTIVES: Low-dose (LD) PET imaging would lead to reduced image quality and diagnostic efficacy. We propose a deep learning (DL) method to reduce radiotracer dosage for PET studies while maintaining diagnostic quality.

STANet: A Novel Spatio-Temporal Aggregation Network for Depression Classification with Small and Unbalanced FMRI Data.

Tomography (Ann Arbor, Mich.)
: Early diagnosis of depression is crucial for effective treatment and suicide prevention. Traditional methods rely on self-report questionnaires and clinical assessments, lacking objective biomarkers. Combining functional magnetic resonance imaging ...

MACNet: A Multidimensional Attention-Based Convolutional Neural Network for Lower-Limb Motor Imagery Classification.

Sensors (Basel, Switzerland)
Decoding lower-limb motor imagery (MI) is highly important in brain-computer interfaces (BCIs) and rehabilitation engineering. However, it is challenging to classify lower-limb MI from electroencephalogram (EEG) signals, because lower-limb motions (L...

Enhancement and evaluation for deep learning-based classification of volumetric neuroimaging with 3D-to-2D knowledge distillation.

Scientific reports
The application of deep learning techniques for the analysis of neuroimaging has been increasing recently. The 3D Convolutional Neural Network (CNN) technology, which is commonly adopted to encode volumetric information, requires a large number of da...

Taming Prolonged Ionic Drift-Diffusion Dynamics for Brain-Inspired Computation.

Advanced materials (Deerfield Beach, Fla.)
Recent advances in neural network-based computing have enabled human-like information processing in areas such as image classification and voice recognition. However, many neural networks run on conventional computers that operate at GHz clock freque...

The efficacy of topological properties of functional brain networks in identifying major depressive disorder.

Scientific reports
Major Depressive Disorder (MDD) is a common mental disorder characterized by cognitive impairment, and its pathophysiology remains to be explored. In this study, we aimed to explore the efficacy of brain network topological properties (TPs) in identi...

Active Inference and Deep Generative Modeling for Cognitive Ultrasound.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Ultrasound (US) has the unique potential to offer access to medical imaging to anyone, everywhere. Devices have become ultraportable and cost-effective, akin to the stethoscope. Nevertheless, and despite many advances, US image quality and diagnostic...

AI-Assisted Post Contrast Brain MRI: Eighty Percent Reduction in Contrast Dose.

Academic radiology
OBJECTIVES: In the context of growing safety concerns regarding the use of gadolinium-based contrast agents in contrast-enhanced MRI, there is a need for dose reduction without compromising diagnostic accuracy. A deep learning (DL) method is proposed...