AIMC Topic: Magnetic Resonance Imaging
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Quasi-supervised MR-CT image conversion based on unpaired data.
Physics in medicine and biology
Jun 17, 2025
. In radiotherapy planning, acquiring both magnetic resonance (MR) and computed tomography (CT) images is crucial for comprehensive evaluation and treatment. However, simultaneous acquisition of MR and CT images is time-consuming, economically expens...
High-definition motion-resolved MRI using 3D radial kooshball acquisition and deep learning spatial-temporal 4D reconstruction.
Physics in medicine and biology
Jun 17, 2025
To develop motion-resolved volumetric MRI with 1.1 mm isotropic resolution and scan times <5 min using a combination of 3D radial kooshball acquisition and spatial-temporal deep learning 4D reconstruction for free-breathing high-definition (HD) lung ...
Classification of glioma grade and Ki-67 level prediction in MRI data: A SHAP-driven interpretation.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jun 16, 2025
This study focuses on artificial intelligence-driven classification of glioma and Ki-67 leveling using T2w-FLAIR MRI, exploring the association of Ki-67 biomarkers with deep learning (DL) features through explainable artificial intelligence (XAI) and...
Rate of brain aging associates with future executive function in Asian children and older adults.
eLife
Jun 16, 2025
Brain age has emerged as a powerful tool to understand neuroanatomical aging and its link to health outcomes like cognition. However, there remains a lack of studies investigating the rate of brain aging and its relationship to cognition. Furthermore...
MRI super-resolution reconstruction using efficient diffusion probabilistic model with residual shifting.
Physics in medicine and biology
Jun 16, 2025
Magnetic resonance imaging (MRI) is essential in clinical and research contexts, providing exceptional soft-tissue contrast. However, prolonged acquisition times often lead to patient discomfort and motion artifacts. Diffusion-based deep learning sup...
FFLUNet: Feature Fused Lightweight UNet for brain tumor segmentation.
Computers in biology and medicine
Jun 14, 2025
Brain tumors, particularly glioblastoma multiforme, are considered one of the most threatening types of tumors in neuro-oncology. Segmenting brain tumors is a crucial part of medical imaging. It plays a key role in diagnosing conditions, planning tre...
Redefining parameter-efficiency in ADHD diagnosis: A lightweight attention-driven kolmogorov-arnold network with reduced parameter complexity and a novel activation function.
Psychiatry research. Neuroimaging
Jun 13, 2025
As deep learning continues to advance in medical analysis, the increasing complexity of models, particularly Convolutional Neural Networks (CNNs), presents significant challenges related to interpretability, computational costs, and real-world applic...
3D-MRI brain glioma intelligent segmentation based on improved 3D U-net network.
PloS one
Jun 13, 2025
PURPOSE: To enhance glioma segmentation, a 3D-MRI intelligent glioma segmentation method based on deep learning is introduced. This method offers significant guidance for medical diagnosis, grading, and treatment strategy selection.
Same-model and cross-model variability in knee cartilage thickness measurements using 3D MRI systems.
PloS one
Jun 13, 2025
PURPOSE: Magnetic Resonance Imaging (MRI) based three-dimensional analysis of knee cartilage has evolved to become fully automatic. However, when implementing these measurements across multiple clinical centers, scanner variability becomes a critical...
NeuroEmo: A neuroimaging-based fMRI dataset to extract temporal affective brain dynamics for Indian movie video clips stimuli using dynamic functional connectivity approach with graph convolution neural network (DFC-GCNN).
Computers in biology and medicine
Jun 12, 2025
FMRI, a non-invasive neuroimaging technique, can detect emotional brain activation patterns. It allows researchers to observe functional changes in the brain, making it a valuable tool for emotion recognition. For improved emotion recognition systems...
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