AIMC Topic: Magnetic Resonance Imaging

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Radiomics and artificial intelligence for predicting pituitary neuroendocrine tumor consistency: a systematic review and meta-analysis.

Neurosurgical review
Pituitary neuroendocrine tumors (PitNETs) represent approximately 16% of primary brain tumors. Tumor consistency, whether soft or hard, directly affects surgical strategy, extent of resection, and risk of complications. This study aimed to perform a ...

Dynamic brain mechanisms supporting salient memories under cortisol.

Science advances
Cortisol is known to promote memory for emotionally arousing experiences, yet the neural networks involved in enhancing these memories are unknown. Here, we combine pharmacological fMRI with an analysis approach to determine the dynamic brain network...

Unsupervised discovery of ischemic stroke phenotypes from multimodal MRI radiomics.

Biomedical physics & engineering express
This study presents a fully unsupervised and label-independent radiomic pipeline designed to group different types of ischemic stroke lesions using multimodal Magnetic Resonance Imaging (MRI) . The aim is to address lesion heterogeneity and the absen...

Artificial Intelligence-Enabled Imaging for Predicting Preoperative Extraprostatic Extension in Prostate Cancer: Systematic Review and Meta-Analysis.

Journal of medical Internet research
BACKGROUND: Artificial intelligence (AI) techniques, particularly those using machine learning and deep learning to analyze multimodal imaging data, have shown considerable promise in enhancing preoperative prediction of extraprostatic extension (EPE...

Model-based spatiotemporal synthetic data generation framework and deep-learning reconstruction for real-time MRI oxygen extraction fraction mapping.

Physics in medicine and biology
Synthetic data has emerged as a highly efficient solution to address the scarcity of training data in deep learning-based quantitative magnetic resonance imaging (qMRI) reconstruction. However, current applications of synthetic data predominantly foc...

MR-based synthetic CT generation using dual-attention enhanced 3D Conditional GAN for head and neck radiotherapy.

Biomedical physics & engineering express
. This study aims to synthesize CT from MR images for radiotherapy planning of head and neck tumor using an improved three-dimensional conditional generative adversarial network (3D cGAN) based on dual-attention modules.. A total of 212 paired CT and...

More insights into disruption and decoupling of individual metabolic connectomes in Parkinson's disease.

Progress in neuro-psychopharmacology & biological psychiatry
PURPOSE: Metabolic disturbances are hallmark pathological features of Parkinson's disease (PD) and can be noninvasively captured by arterial spin labeling (ASL). However, the metabolic pattern of disconnections beyond regional alterations remains sca...

An automated classification of brain white matter inherited disorders (Leukodystrophy) using MRI image features.

Biomedical physics & engineering express
Leukodystrophies are a group of inherited disorders that predominantly and selectively affect the white matter of the central nervous system. Their overlapping clinical and imaging manifestations make a timely and accurate diagnosis challenging. In t...

Both Infarcted and Noninfarcted Brain Regions Contribute to Deep Learning-Based MRI Prediction of Acute Stroke Outcome.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Predicting long-term clinical outcomes based on early acute ischemic stroke (AIS) information would be useful for many reasons, including patient counseling and clinical trial execution. This study investigates how different r...