AIMC Topic: Magnetic Resonance Imaging

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Artificial intelligence development in pediatric body magnetic resonance imaging: best ideas to adapt from adults.

Pediatric radiology
Emerging manifestations of artificial intelligence (AI) have featured prominently in virtually all industries and facets of our lives. Within the radiology literature, AI has shown great promise in improving and augmenting radiologist workflow. In pe...

A graph neural network framework for causal inference in brain networks.

Scientific reports
A central question in neuroscience is how self-organizing dynamic interactions in the brain emerge on their relatively static structural backbone. Due to the complexity of spatial and temporal dependencies between different brain areas, fully compreh...

Current Status and Quality of Machine Learning-Based Radiomics Studies for Glioma Grading: A Systematic Review.

Oncology
INTRODUCTION: Radiomics now has significant momentum in the era of precision medicine. Glioma is one of the pathologies that has been extensively evaluated by radiomics. However, this technique has not been incorporated into clinical practice. In thi...

Current and emerging artificial intelligence applications for pediatric abdominal imaging.

Pediatric radiology
Artificial intelligence (AI) uses computers to mimic cognitive functions of the human brain, allowing inferences to be made from generally large datasets. Traditional machine learning (e.g., decision tree analysis, support vector machines) and deep l...

Accelerated multicontrast reconstruction for synthetic MRI using joint parallel imaging and variable splitting networks.

Medical physics
PURPOSE: Synthetic magnetic resonance imaging (MRI) requires the acquisition of multicontrast images to estimate quantitative parameter maps, such as T , T , and proton density (PD). The study aims to develop a multicontrast reconstruction method bas...

Autosegmentation of Prostate Zones and Cancer Regions from Biparametric Magnetic Resonance Images by Using Deep-Learning-Based Neural Networks.

Sensors (Basel, Switzerland)
The accuracy in diagnosing prostate cancer (PCa) has increased with the development of multiparametric magnetic resonance imaging (mpMRI). Biparametric magnetic resonance imaging (bpMRI) was found to have a diagnostic accuracy comparable to mpMRI in ...

Intranasal vasopressin modulates resting state brain activity across multiple neural systems: Evidence from a brain imaging machine learning study.

Neuropharmacology
Arginine vasopressin (AVP), a neuropeptide with widespread receptors in brain regions important for socioemotional processing, is critical in regulating various mammalian social behavior and emotion. Although a growing body of task-based brain imagin...

Using deep learning to predict microvascular invasion in hepatocellular carcinoma based on dynamic contrast-enhanced MRI combined with clinical parameters.

Journal of cancer research and clinical oncology
PURPOSE: Microvascular invasion (MVI) is a critical determinant of the early recurrence and poor prognosis of patients with hepatocellular carcinoma (HCC). Prediction of MVI status is clinically significant for the decision of treatment strategies an...

Large-scale nonlinear Granger causality for inferring directed dependence from short multivariate time-series data.

Scientific reports
A key challenge to gaining insight into complex systems is inferring nonlinear causal directional relations from observational time-series data. Specifically, estimating causal relationships between interacting components in large systems with only s...