AIMC Topic: Magnetic Resonance Imaging
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Spatiotemporal Implicit Neural Representation for Unsupervised Dynamic MRI Reconstruction.
IEEE transactions on medical imaging
May 2, 2025
Supervised Deep-Learning (DL)-based reconstruction algorithms have shown state-of-the-art results for highly-undersampled dynamic Magnetic Resonance Imaging (MRI) reconstruction. However, the requirement of excessive high-quality ground-truth data hi...
UniAda: Domain Unifying and Adapting Network for Generalizable Medical Image Segmentation.
IEEE transactions on medical imaging
May 2, 2025
Learning a generalizable medical image segmentation model is an important but challenging task since the unseen (testing) domains may have significant discrepancies from seen (training) domains due to different vendors and scanning protocols. Existin...
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...
Quantitative susceptibility mapping in magnetically inhomogeneous tissues.
Magnetic resonance in medicine
May 1, 2025
PURPOSE: Conventional quantitative susceptibility mapping (QSM) methods rely on simplified physical models that assume isotropic and homogeneous tissue properties, leading to artifacts and inaccuracies in biological tissues. This study aims to develo...
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...
Humans
Female
Middle Aged
Deep Learning
Time Factors
Magnetic Resonance Imaging
Reproducibility of Results
Aged
Male
Automation
Blood Flow Velocity
Atrial Function, Left
Heart Rate
Magnetic Resonance Imaging, Cine
Image Interpretation, Computer-Assisted
Observer Variation
Predictive Value of Tests
Atrial Fibrillation
Heart Atria
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 ...
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