AIMC Topic:
Magnetic Resonance Imaging
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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...
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...
Evaluation of deep-learning TSE images in clinical musculoskeletal imaging.
Journal of medical imaging and radiation oncology
Jun 5, 2024
In this study, we compared the fat-saturated (FS) and non-FS turbo spin echo (TSE) magnetic resonance imaging knee sequences reconstructed conventionally (conventional-TSE) against a deep learning-based reconstruction of accelerated TSE (DL-TSE) scan...
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...
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...
Deep Learning-Based Approach for Identifying and Measuring Focal Liver Lesions on Contrast-Enhanced MRI.
Journal of magnetic resonance imaging : JMRI
Jun 3, 2024
BACKGROUND: The number of focal liver lesions (FLLs) detected by imaging has increased worldwide, highlighting the need to develop a robust, objective system for automatically detecting FLLs.
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...
MADR-Net: multi-level attention dilated residual neural network for segmentation of medical images.
Scientific reports
Jun 3, 2024
Medical image segmentation has made a significant contribution towards delivering affordable healthcare by facilitating the automatic identification of anatomical structures and other regions of interest. Although convolution neural networks have bec...
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 ...
Multitask Learning for Joint Diagnosis of Multiple Mental Disorders in Resting-State fMRI.
IEEE transactions on neural networks and learning systems
Jun 3, 2024
Facing the increasing worldwide prevalence of mental disorders, the symptom-based diagnostic criteria struggle to address the urgent public health concern due to the global shortfall in well-qualified professionals. Thanks to the recent advances in n...
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