Radiology

Preoperative risk assessment of invasive endometrial cancer using MRI-based radiomics: a systematic review and meta-analysis.

OBJECTIVE: Image-derived machine learning (ML) is a robust and growing field in diagnostic imaging systems for both clinicians and radiologists. Accurate preoperative radiological evaluation of the invasive ability of endometrial cancer (EC) can increase the degree of clinical benefit. The present study aimed to investigate the diagnostic performance of magnetic resonance imaging (MRI)-derived art...

Deep learning reconstruction combined with contrast-enhancement boost in dual-low dose CT pulmonary angiography: a two-center prospective trial.

PURPOSE: To investigate whether the deep learning reconstruction (DLR) combined with contrast-enhancement-boost (CE-boost) technique can improve the diagnostic quality of CT pulmonary angiography (CTPA) at low radiation and contrast doses, compared with routine CTPA using hybrid iterative reconstruction (HIR).

Quantitative image quality metrics enable resource-efficient quality control of clinically applied AI-based reconstructions in MRI.

OBJECTIVE: AI-based MRI reconstruction techniques improve efficiency by reducing acquisition times whilst maintaining or improving image quality. Rece...

The predictive value of F-FDG PET/CT radiomics for pleural invasion in non-small cell lung cancer.

OBJECTIVE: This study aims to develop and validate a PET/CT radiomics fusion model for preoperative predicting pleural invasion (PI) in non-small cell...

Learning contrast and content representations for synthesizing magnetic resonance image of arbitrary contrast.

Magnetic Resonance Imaging (MRI) produces images with different contrasts, providing complementary information for clinical diagnoses and research. Ho...

Deep learning and iterative image reconstruction for head CT: Impact on image quality and radiation dose reduction-Comparative study.

This study focuses on an objective evaluation of a novel reconstruction algorithm-Deep Learning Image Reconstruction (DLIR)-ability to improve image ...

Optimizing the power of AI for fracture detection: from blind spots to breakthroughs.

Artificial Intelligence (AI) is increasingly being integrated into the field of musculoskeletal (MSK) radiology, from research methods to routine clin...

Improving automatic cerebral 3D-2D CTA-DSA registration.

PURPOSE: Stroke remains a leading cause of morbidity and mortality worldwide, despite advances in treatment modalities. Endovascular thrombectomy (EVT...

Non-invasive arterial input function estimation using an MRA atlas and machine learning.

BACKGROUND: Quantifying biological parameters of interest through dynamic positron emission tomography (PET) requires an arterial input function (AIF)...

Self-supervised feature learning for cardiac Cine MR image reconstruction.

We propose a self-supervised feature learning assisted reconstruction (SSFL-Recon) framework for MRI reconstruction to address the limitation of exist...

Ovarian Cancer Screening: Recommendations and Future Prospects.

Ovarian cancer remains a significant cause of mortality among women, largely due to challenges in early detection. Current screening strategies, inclu...

Development and validation of a multi-omics hemorrhagic transformation model based on hyperattenuated imaging markers following mechanical thrombectomy.

This study aimed to develop a predictive model integrating clinical, radiomics, and deep learning (DL) features of hyperattenuated imaging markers (HI...

Renal Transplant Survival Prediction From Unsupervised Deep Learning-Based Radiomics on Early Dynamic Contrast-Enhanced MRI.

RATIONALE AND OBJECTIVES: End-stage renal disease is characterized by an irreversible decline in kidney function. Despite a risk of chronic dysfunctio...

Highlights of the Society for Cardiovascular Magnetic Resonance (SCMR) 2025 Conference: leading the way to accessible, efficient and sustainable CMR.

The 28th Annual Scientific Sessions of the Society for Cardiovascular Magnetic Resonance (SCMR) took place from January 29 to February 1, 2025, in Was...

In-silico evaluation of aging-related interventions using omics data and predictive modeling.

A major challenge in aging research is identifying interventions that can improve lifespan and health and minimize toxicity. Clinical studies cannot u...

AI in Action: A Roadmap from the Radiology AI Council for Effective Model Evaluation and Deployment.

As the integration of artificial intelligence (AI) into radiology workflows continues to evolve, establishing standardized processes for the evaluatio...

Integrating Knowledge: The Power of Ontologies in Psychiatric Research and Clinical Informatics.

Ontologies are structured frameworks for representing knowledge by systematically defining concepts, categories, and their relationships. While widely...

Artificial Intelligence in Rotator Cuff Tear Detection: A Systematic Review of MRI-Based Models.

: This descriptive systematic review aimed to assess in the available literature on the current application and overall performance of Artificial Inte...

A Convolutional Mixer-Based Deep Learning Network for Alzheimer's Disease Classification from Structural Magnetic Resonance Imaging.

Alzheimer's disease (AD) is a neurodegenerative disorder that severely impairs cognitive function across various age groups, ranging from early to la...

Correlation Between Neurocognitive Outcomes and Neuroaxonal Connectome Alterations After Whole Brain Radiotherapy: A Proof-of-Concept Study.

Connectomics is an evolving branch of neuroscience that determines structural and functional connectivity in the brain. The objective of this prospec...