AIMC Topic: Magnetic Resonance Imaging
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Prediction of hepatocellular carcinoma response to radiation segmentectomy using an MRI-based machine learning approach.
Abdominal radiology (New York)
Oct 26, 2024
PURPOSE: To evaluate the value of pre-treatment MRI-based radiomics in patients with hepatocellular carcinoma (HCC) for the prediction of response to Yttrium 90 radiation segmentectomy.
Impact of non-contrast-enhanced imaging input sequences on the generation of virtual contrast-enhanced breast MRI scans using neural network.
European radiology
Oct 25, 2024
OBJECTIVE: To investigate how different combinations of T1-weighted (T1w), T2-weighted (T2w), and diffusion-weighted imaging (DWI) impact the performance of virtual contrast-enhanced (vCE) breast MRI.
An Efficient Muscle Segmentation Method via Bayesian Fusion of Probabilistic Shape Modeling and Deep Edge Detection.
IEEE transactions on bio-medical engineering
Oct 25, 2024
OBJECTIVE: Paraspinal muscle segmentation and reconstruction from MR images are critical to implement quantitative assessment of chronic and recurrent low back pains. Due to unclear muscle boundaries and shape variations, current segmentation methods...
An optimized siamese neural network with deep linear graph attention model for gynaecological abdominal pelvic masses classification.
Abdominal radiology (New York)
Oct 24, 2024
An adnexal mass, also known as a pelvic mass, is a growth that develops in or near the uterus, ovaries, fallopian tubes, and supporting tissues. For women suspected of having ovarian cancer, timely and accurate detection of a malignant pelvic mass is...
Deep learning corrects artifacts in RASER MRI profiles.
Magnetic resonance imaging
Oct 24, 2024
A newly developed magnetic resonance imaging (MRI) approach is based on "Radiowave amplification by the stimulated emission of radiation" (RASER). RASER MRI potentially allows for higher resolution, is inherently background-free, and does not require...
Robust brain MRI image classification with SIBOW-SVM.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Oct 24, 2024
Primary Central Nervous System tumors in the brain are among the most aggressive diseases affecting humans. Early detection and classification of brain tumor types, whether benign or malignant, glial or non-glial, is critical for cancer prevention an...
Accuracy of distinguishing benign, high-risk lesions and malignancies with inductive machine learning models in BIRADS 4 and BIRADS 5 lesions on breast MR examinations.
European journal of radiology
Oct 24, 2024
RATIONALE AND OBJECTIVES: The aim of this study is to explore the utility of Inductive Decision Tree models (IDTs) in distinguishing between benign, malignant, and high-risk (B3) breast lesions.
Comparison of Manual vs Artificial Intelligence-Based Muscle MRI Segmentation for Evaluating Disease Progression in Patients With CMT1A.
Neurology
Oct 24, 2024
BACKGROUND AND OBJECTIVES: Intramuscular fat fraction (FF), assessed with quantitative MRI (qMRI), has emerged as one of the few responsive outcome measures in CMT1A patients. The main limitation for its use in future therapeutic trials is the time r...
A Quantitative Comparison Between Human and Artificial Intelligence in the Detection of Focal Cortical Dysplasia.
Investigative radiology
Oct 23, 2024
OBJECTIVES: Artificial intelligence (AI) is thought to improve lesion detection. However, a lack of knowledge about human performance prevents a comparative evaluation of AI and an accurate assessment of its impact on clinical decision-making. The ob...
Exploring potential ADHD biomarkers through advanced machine learning: An examination of audiovisual integration networks.
Computers in biology and medicine
Oct 23, 2024
Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental condition marked by inattention and impulsivity, linked to disruptions in functional brain connectivity and structural alterations in large-scale brain networks. Although sensory...
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