AIMC Topic: Magnetic Resonance Imaging
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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...
Artificial intelligence-driven 3D MRI of lumbosacral nerve root anomalies: accuracy, incidence, and clinical utility.
Neuroradiology
Mar 1, 2025
PURPOSE: Lumbosacral nerve root anomalies are relatively rare but can be a risk factor for intraoperative nerve injury. However, it is often difficult to evaluate them with preoperative imaging. We developed a software that automatically generates th...
Comprehensive evaluation of pipelines for classification of psychiatric disorders using multi-site resting-state fMRI datasets.
Neural networks : the official journal of the International Neural Network Society
Feb 28, 2025
Objective classification biomarkers that are developed using resting-state functional magnetic resonance imaging (rs-fMRI) data are expected to contribute to more effective treatment for psychiatric disorders. Unfortunately, no widely accepted biomar...
Denoising Diffusion Probabilistic Model to Simulate Contrast-enhanced spinal MRI of Spinal Tumors: A Multi-Center Study.
Academic radiology
Feb 28, 2025
RATIONALE AND OBJECTIVES: To generate virtual T1 contrast-enhanced (T1CE) sequences from plain spinal MRI sequences using the denoising diffusion probabilistic model (DDPM) and to compare its performance against one baseline model pix2pix and three a...
Sensitivity and Specificity
Male
Middle Aged
Contrast Media
Female
Reproducibility of Results
Aged
Adult
Spinal Neoplasms
Humans
Image Enhancement
Algorithms
Signal-To-Noise Ratio
Computer Simulation
Diffusion Magnetic Resonance Imaging
Magnetic Resonance Imaging
Models, Statistical
Image Interpretation, Computer-Assisted
Artifacts
Graph-based prototype inverse-projection for identifying cortical sulcal pattern abnormalities in congenital heart disease.
Medical image analysis
Feb 28, 2025
Examining the altered arrangement and patterning of sulcal folds offers insights into the mechanisms of neurodevelopmental differences in psychiatric and neurological disorders. Previous sulcal pattern analysis used spectral graph matching of sulcal ...
A Generative Shape Compositional Framework to Synthesize Populations of Virtual Chimeras.
IEEE transactions on neural networks and learning systems
Feb 28, 2025
Generating virtual organ populations that capture sufficient variability while remaining plausible is essential to conduct in silico trials (ISTs) of medical devices. However, not all anatomical shapes of interest are always available for each indivi...
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