AIMC Topic: Brain

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Adaptive Knowledge Distillation for High-Quality Unsupervised MRI Reconstruction With Model-Driven Priors.

IEEE journal of biomedical and health informatics Jun 6, 2024
Magnetic Resonance Imaging (MRI) reconstruction has made significant progress with the introduction of Deep Learning (DL) technology combined with Compressed Sensing (CS). However, most existing methods require large fully sampled training datasets t...
Humans Algorithms Image Processing, Computer-Assisted Brain Magnetic Resonance Imaging Deep Learning Unsupervised Machine Learning
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Radial Undersampled MRI Reconstruction Using Deep Learning With Mutual Constraints Between Real and Imaginary Components of K-Space.

IEEE journal of biomedical and health informatics Jun 6, 2024
The deep learning method is an efficient solution for improving the quality of undersampled magnetic resonance (MR) image reconstruction while reducing lengthy data acquisition. Most deep learning methods neglect the mutual constraints between the re...
Humans Magnetic Resonance Imaging Algorithms Knee Deep Learning Image Processing, Computer-Assisted Brain
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Examining the Most Important Risk Factors for Predicting Youth Persistent and Distressing Psychotic-Like Experiences.

Biological psychiatry. Cognitive neuroscience and neuroimaging Jun 5, 2024
BACKGROUND: Persistence and distress distinguish more clinically significant psychotic-like experiences (PLEs) from those that are less likely to be associated with impairment and/or need for care. Identifying risk factors that identify clinically re...
Male Brain Magnetic Resonance Imaging Adolescent Child Humans Psychotic Disorders Female Machine Learning Risk Factors
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Scan-Specific Self-Supervised Bayesian Deep Non-Linear Inversion for Undersampled MRI Reconstruction.

IEEE transactions on medical imaging Jun 5, 2024
Magnetic resonance imaging is subject to slow acquisition times due to the inherent limitations in data sampling. Recently, supervised deep learning has emerged as a promising technique for reconstructing sub-sampled MRI. However, supervised deep lea...
Supervised Machine Learning Magnetic Resonance Imaging Bayes Theorem Humans Algorithms Deep Learning Image Processing, Computer-Assisted Brain
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Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model.

IEEE transactions on neural networks and learning systems Jun 4, 2024
Recently, brain networks have been widely adopted to study brain dynamics, brain development, and brain diseases. Graph representation learning techniques on brain functional networks can facilitate the discovery of novel biomarkers for clinical phen...
Humans Neural Networks, Computer Machine Learning Models, Neurological Connectome Nerve Net Brain Magnetic Resonance Imaging Algorithms
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Adoption of deep learning-based magnetic resonance image information diagnosis in brain function network analysis of Parkinson's disease patients with end-of-dose wearing-off.

Journal of neuroscience methods Jun 3, 2024
OBJECTIVE: this study was to analyze the brain functional network of end-of-dose wearing-off (EODWO) in patients with Parkinson's disease (PD) using a convolutional neural network (CNN)-based functional magnetic resonance imaging (fMRI) data classifi...
Female Aged Male Humans Brain Magnetic Resonance Imaging Parkinson Disease Antiparkinson Agents Middle Aged Deep Learning
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Synergistic information supports modality integration and flexible learning in neural networks solving multiple tasks.

PLoS computational biology Jun 3, 2024
Striking progress has been made in understanding cognition by analyzing how the brain is engaged in different modes of information processing. For instance, so-called synergistic information (information encoded by a set of neurons but not by any sub...
Cognition Models, Neurological Neurons Brain Learning Computational Biology Humans Neural Networks, Computer
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Deep Learning With Physics-Embedded Neural Network for Full Waveform Ultrasonic Brain Imaging.

IEEE transactions on medical imaging Jun 3, 2024
The convenience, safety, and affordability of ultrasound imaging make it a vital non-invasive diagnostic technique for examining soft tissues. However, significant differences in acoustic impedance between the skull and soft tissues hinder the succes...
Image Processing, Computer-Assisted Phantoms, Imaging Algorithms Brain Deep Learning Ultrasonography Animals Humans Neural Networks, Computer
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Hybrid CNN-Transformer Network With Circular Feature Interaction for Acute Ischemic Stroke Lesion Segmentation on Non-Contrast CT Scans.

IEEE transactions on medical imaging Jun 3, 2024
Lesion segmentation is a fundamental step for the diagnosis of acute ischemic stroke (AIS). Non-contrast CT (NCCT) is still a mainstream imaging modality for AIS lesion measurement. However, AIS lesion segmentation on NCCT is challenging due to low c...
Humans Neural Networks, Computer Algorithms Ischemic Stroke Brain Tomography, X-Ray Computed
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Compositionally Equivariant Representation Learning.

IEEE transactions on medical imaging Jun 3, 2024
Deep learning models often need sufficient supervision (i.e., labelled data) in order to be trained effectively. By contrast, humans can swiftly learn to identify important anatomy in medical images like MRI and CT scans, with minimal guidance. This ...
Humans Algorithms Deep Learning Image Processing, Computer-Assisted Magnetic Resonance Imaging Brain Tomography, X-Ray Computed
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