AIMC Topic: Magnetic Resonance Imaging
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A Lightweight Deep Convolutional Neural Network Extracting Local and Global Contextual Features for the Classification of Alzheimer's Disease Using Structural MRI.
IEEE journal of biomedical and health informatics
Mar 6, 2025
Recent advancements in the classification of Alzheimer's disease have leveraged the automatic feature generation capability of convolutional neural networks (CNNs) using neuroimaging biomarkers. However, most of the existing CNN-based methods often d...
LUCF-Net: Lightweight U-Shaped Cascade Fusion Network for Medical Image Segmentation.
IEEE journal of biomedical and health informatics
Mar 6, 2025
The performance of modern U-shaped neural networks for medical image segmentation has been significantly enhanced by incorporating Transformer layers. Although Transformer architectures are powerful at extracting global information, its ability to ca...
Evaluating fusion models for predicting occult lymph node metastasis in tongue squamous cell carcinoma.
European radiology
Mar 5, 2025
OBJECTIVES: This study evaluated and compared the effectiveness of various predictive models for forecasting occult lymph node metastasis (LNM) in tongue squamous cell carcinoma (TSCC) patients.
Dual-type deep learning-based image reconstruction for advanced denoising and super-resolution processing in head and neck T2-weighted imaging.
Japanese journal of radiology
Mar 5, 2025
PURPOSE: To assess the utility of dual-type deep learning (DL)-based image reconstruction with DL-based image denoising and super-resolution processing by comparing images reconstructed with the conventional method in head and neck fat-suppressed (Fs...
Development of Hybrid radiomic Machine learning models for preoperative prediction of meningioma grade on multiparametric MRI.
Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
Mar 5, 2025
PURPOSE: To develop and compare machine learning models for distinguishing low and high grade meningiomas on multiparametric MRI.
AI-Generated Synthetic STIR of the Lumbar Spine from T1 and T2 MRI Sequences Trained with Open-Source Algorithms.
AJNR. American journal of neuroradiology
Mar 4, 2025
BACKGROUND AND PURPOSE: Lumbar spine MRIs can be time consuming, stressful for patients, and costly to acquire. In this work, we train and evaluate open-source generative adversarial network (GAN) to create synthetic lumbar spine MRI STIR volumes fro...
Deep learning based image enhancement for dynamic non-Cartesian MRI: Application to "silent" fMRI.
Computers in biology and medicine
Mar 3, 2025
Radial based non-Cartesian sequences may be used for silent functional MRI examinations particularly in settings where scanner noise could pose issues. However, to achieve reasonable temporal resolution, under-sampled 3D radial k-space commonly resul...
MRI radiomics based on machine learning in high-grade gliomas as a promising tool for prediction of CD44 expression and overall survival.
Scientific reports
Mar 3, 2025
We aimed to predict CD44 expression and assess its prognostic significance in patients with high-grade gliomas (HGG) using non-invasive radiomics models based on machine learning. Enhanced magnetic resonance imaging, along with the corresponding gene...
Development of a deep learning radiomics model combining lumbar CT, multi-sequence MRI, and clinical data to predict high-risk cage subsidence after lumbar fusion: a retrospective multicenter study.
Biomedical engineering online
Mar 2, 2025
BACKGROUND: To develop and validate a model that integrates clinical data, deep learning radiomics, and radiomic features to predict high-risk patients for cage subsidence (CS) after lumbar fusion.
Use of deep learning-based high-resolution magnetic resonance to identify intracranial and extracranial symptom-related plaques.
Neuroscience
Mar 1, 2025
This study aims to develop a deep learning model using high-resolution vessel wall imaging (HR-VWI) to differentiate symptom-related intracranial and extracranial plaques, which is crucial for stroke treatment and prevention. We retrospectively analy...
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