AIMC Topic: Magnetic Resonance Imaging
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Modality-Aware Discriminative Fusion Network for Integrated Analysis of Brain Imaging Genomics.
IEEE transactions on neural networks and learning systems
May 2, 2025
Mild cognitive impairment (MCI) represents an early stage of Alzheimer's disease (AD), characterized by subtle clinical symptoms that pose challenges for accurate diagnosis. The quest for the identification of MCI individuals has highlighted the impo...
Artifact estimation network for MR images: effectiveness of batch normalization and dropout layers.
BMC medical imaging
May 1, 2025
BACKGROUND: Magnetic resonance imaging (MRI) is an essential tool for medical diagnosis. However, artifacts may degrade images obtained through MRI, especially owing to patient movement. Existing methods that mitigate the artifact problem are subject...
Artificial Intelligence in Prostate Cancer Diagnosis on Magnetic Resonance Imaging: Time for a New PARADIGM.
European urology
Apr 30, 2025
Artificial intelligence (AI) may provide a solution for improving access to expert, timely, and accurate magnetic resonance imaging (MRI) interpretation. The PARADIGM trial will provide level 1 evidence on the role of AI in the diagnosis of prostate ...
Self-supervised learning for label-free segmentation in cardiac ultrasound.
Nature communications
Apr 30, 2025
Segmentation and measurement of cardiac chambers from ultrasound is critical, but laborious and poorly reproducible. Neural networks can assist, but supervised approaches require the same problematic manual annotations. We build a pipeline for self-s...
Predicting the onset of mental health problems in adolescents.
Psychological medicine
Apr 30, 2025
OBJECTIVE: Mental health problems are the major cause of disability among adolescents. Personalized prevention may help to mitigate the development of mental health problems, but no tools are available to identify individuals at risk before they requ...
Deep learning based automated left atrial segmentation and flow quantification of real time phase contrast MRI in patients with atrial fibrillation.
The international journal of cardiovascular imaging
Apr 29, 2025
Real time 2D phase contrast (RTPC) MRI is useful for flow quantification in atrial fibrillation (AF) patients, but data analysis requires time-consuming anatomical contouring for many cardiac time frames. Our goal was to develop a convolutional neura...
Image Interpretation, Computer-Assisted
Observer Variation
Automation
Blood Flow Velocity
Atrial Fibrillation
Heart Atria
Heart Rate
Atrial Function, Left
Magnetic Resonance Imaging, Cine
Humans
Female
Reproducibility of Results
Magnetic Resonance Imaging
Aged
Predictive Value of Tests
Male
Deep Learning
Middle Aged
Time Factors
Brain tumor detection empowered with ensemble deep learning approaches from MRI scan images.
Scientific reports
Apr 29, 2025
Brain tumor detection is essential for early diagnosis and successful treatment, both of which can significantly enhance patient outcomes. To evaluate brain MRI scans and categorize them into four types-pituitary, meningioma, glioma, and normal-this ...
A swin transformer and CNN fusion framework for accurate Parkinson disease classification in MRI.
Scientific reports
Apr 29, 2025
Parkinson's disease ranks as the second most prevalent neurological disorder after Alzheimer's disease. Convolutional neural networks (CNNs) have been extensively employed in Parkinson's disease (PD) detection using MR images. However, CNN models gen...
Deep learning for quality assessment of axial T2-weighted prostate MRI: a tool to reduce unnecessary rescanning.
European radiology experimental
Apr 29, 2025
BACKGROUND: T2-weighted images are a critical component of prostate magnetic resonance imaging (MRI), and it would be useful to automatically assess image quality (IQ) on a patient-specific basis without radiologist oversight.
From pixels to prognosis: leveraging radiomics and machine learning to predict IDH1 genotype in gliomas.
Neurosurgical review
Apr 29, 2025
Gliomas are the most common primary tumors of the central nervous system, and advances in genetics and molecular medicine have significantly transformed their classification and treatment. This study aims to predict the IDH1 genotype in gliomas using...
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