AIMC Topic: Brain
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Dynamic glucose enhanced imaging using direct water saturation.
Magnetic resonance in medicine
Mar 17, 2025
PURPOSE: Dynamic glucose enhanced (DGE) MRI studies employ CEST or spin lock (CESL) to study glucose uptake. Currently, these methods are hampered by low effect size and sensitivity to motion. To overcome this, we propose to utilize exchange-based li...
Masked Deformation Modeling for Volumetric Brain MRI Self-Supervised Pre-Training.
IEEE transactions on medical imaging
Mar 17, 2025
Self-supervised learning (SSL) has been proposed to alleviate neural networks' reliance on annotated data and to improve downstream tasks' performance, which has obtained substantial success in several volumetric medical image segmentation tasks. How...
Heterogeneous Graph Representation Learning Framework for Resting-State Functional Connectivity Analysis.
IEEE transactions on medical imaging
Mar 17, 2025
Brain functional connectivity analysis is important for understanding brain development and brain disorders. Recent studies have suggested that the variations of functional connectivity among multiple subnetworks are closely related to the developmen...
CGNet: A Correlation-Guided Registration Network for Unsupervised Deformable Image Registration.
IEEE transactions on medical imaging
Mar 17, 2025
Deformable medical image registration plays a significant role in medical image analysis. With the advancement of deep neural networks, learning-based deformable registration methods have made great strides due to their ability to perform fast end-to...
A Learnable Prior Improves Inverse Tumor Growth Modeling.
IEEE transactions on medical imaging
Mar 17, 2025
Biophysical modeling, particularly involving partial differential equations (PDEs), offers significant potential for tailoring disease treatment protocols to individual patients. However, the inverse problem-solving aspect of these models presents a ...
Toward Integrating Federated Learning With Split Learning via Spatio-Temporal Graph Framework for Brain Disease Prediction.
IEEE transactions on medical imaging
Mar 17, 2025
Functional Magnetic Resonance Imaging (fMRI) is used for extracting blood oxygen signals from brain regions to map brain functional connectivity for brain disease prediction. Despite its effectiveness, fMRI has not been widely used: on the one hand, ...
Exploiting adaptive neuro-fuzzy inference systems for cognitive patterns in multimodal brain signal analysis.
Scientific reports
Mar 16, 2025
The analysis of cognitive patterns through brain signals offers critical insights into human cognition, including perception, attention, memory, and decision-making. However, accurately classifying these signals remains a challenge due to their inher...
MGAug: Multimodal Geometric Augmentation in Latent Spaces of Image Deformations.
Medical image analysis
Mar 15, 2025
Geometric transformations have been widely used to augment the size of training images. Existing methods often assume a unimodal distribution of the underlying transformations between images, which limits their power when data with multimodal distrib...
A deep learning approach to multi-fiber parameter estimation and uncertainty quantification in diffusion MRI.
Medical image analysis
Mar 14, 2025
Diffusion MRI (dMRI) is the primary imaging modality used to study brain microstructure in vivo. Reliable and computationally efficient parameter inference for common dMRI biophysical models is a challenging inverse problem, due to factors such as va...
Predicting infant brain connectivity with federated multi-trajectory GNNs using scarce data.
Medical image analysis
Mar 13, 2025
The understanding of the convoluted evolution of infant brain networks during the first postnatal year is pivotal for identifying the dynamics of early brain connectivity development. Thanks to the valuable insights into the brain's anatomy, existing...
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