AIMC Topic: Magnetic Resonance Imaging
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Self-supervised learning for MRI reconstruction through mapping resampled k-space data to resampled k-space data.
Magnetic resonance imaging
May 3, 2025
In recent years, significant advancements have been achieved in applying deep learning (DL) to magnetic resonance imaging (MRI) reconstruction, which traditionally relies on fully sampled data. However, real-world clinical scenarios often demonstrate...
Radiomic study of common sellar region lesions differentiation in magnetic resonance imaging based on multi-classification machine learning model.
BMC medical imaging
May 3, 2025
OBJECTIVE: Pituitary adenomas (PAs), craniopharyngiomas (CRs), Rathke's cleft cysts (RCCs), and tuberculum sellar meningiomas (TSMs) are common sellar region lesions with similar imaging characteristics, making differential diagnosis challenging. Thi...
Meningeal Neoplasms
Sella Turcica
Craniopharyngioma
Adenoma
Central Nervous System Cysts
Female
Aged
Diagnosis, Differential
Radiomics
Humans
Meningioma
Young Adult
Male
Machine Learning
Adult
Middle Aged
Retrospective Studies
Adolescent
Support Vector Machine
Magnetic Resonance Imaging
Pituitary Neoplasms
Optimizing stroke lesion segmentation: A dual-approach using Gaussian mixture models and nnU-Net.
Computers in biology and medicine
May 2, 2025
Machine learning-based stroke lesion segmentation models are widely used in biomedical imaging, but their ability to detect treatment effects remains largely unexplored. Gaussian Mixture Models (GMM) and nnU-Net are among the most prominent and well-...
Visualizing functional network connectivity differences using an explainable machine-learning method.
Physiological measurement
May 2, 2025
. Functional network connectivity (FNC) estimated from resting-state functional magnetic resonance imaging showed great information about the neural mechanism in different brain disorders. But previous research has mainly focused on standard statisti...
Deep Learning-Based Algorithm for Automatic Quantification of Nigrosome-1 and Parkinsonism Classification Using Susceptibility Map-Weighted MRI.
AJNR. American journal of neuroradiology
May 2, 2025
BACKGROUND AND PURPOSE: The diagnostic performance of deep learning model that simultaneously detecting and quantifying nigrosome-1 abnormality by using susceptibility map-weighted imaging (SMwI) remains unexplored. This study aimed to develop and va...
Development and Evaluation of Automated Artificial Intelligence-Based Brain Tumor Response Assessment in Patients with Glioblastoma.
AJNR. American journal of neuroradiology
May 2, 2025
This project aimed to develop and evaluate an automated, AI-based, volumetric brain tumor MRI response assessment algorithm on a large cohort of patients treated at a high-volume brain tumor center. We retrospectively analyzed data from 634 patients ...
Empowering Data Sharing in Neuroscience: A Deep Learning Deidentification Method for Pediatric Brain MRIs.
AJNR. American journal of neuroradiology
May 2, 2025
BACKGROUND AND PURPOSE: Privacy concerns, such as identifiable facial features within brain scans, have hindered the availability of pediatric neuroimaging data sets for research. Consequently, pediatric neuroscience research lags adult counterparts,...
Automated Quantification of Cerebral Microbleeds in SWI: Association with Vascular Risk Factors, White Matter Hyperintensity Burden, and Cognitive Function.
AJNR. American journal of neuroradiology
May 2, 2025
BACKGROUND AND PURPOSE: The amount and distribution of cerebral microbleeds (CMB) are important risk factors for cognitive impairment. Our objective was to train and validate a deep learning (DL)-based segmentation model for cerebral microbleeds (CMB...
Spatiotemporal Implicit Neural Representation for Unsupervised Dynamic MRI Reconstruction.
IEEE transactions on medical imaging
May 2, 2025
Supervised Deep-Learning (DL)-based reconstruction algorithms have shown state-of-the-art results for highly-undersampled dynamic Magnetic Resonance Imaging (MRI) reconstruction. However, the requirement of excessive high-quality ground-truth data hi...
UniAda: Domain Unifying and Adapting Network for Generalizable Medical Image Segmentation.
IEEE transactions on medical imaging
May 2, 2025
Learning a generalizable medical image segmentation model is an important but challenging task since the unseen (testing) domains may have significant discrepancies from seen (training) domains due to different vendors and scanning protocols. Existin...
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