Radiology

Does restrictive anorexia nervosa impact brain aging? A machine learning approach to estimate age based on brain structure.

Anorexia nervosa (AN), a severe eating disorder marked by extreme weight loss and malnutrition, leads to significant alterations in brain structure. This study used machine learning (ML) to estimate brain age from structural MRI scans and investigated brain-predicted age difference (brain-PAD) as a potential biomarker in AN. Structural MRI scans were collected from female participants aged 10-40 y...

Enhancing Free-hand 3D Photoacoustic and Ultrasound Reconstruction using Deep Learning.

This study introduces a motion-based learning network with a global-local self-attention module (MoGLo-Net) to enhance 3D reconstruction in handheld photoacoustic and ultrasound (PAUS) imaging. Standard PAUS imaging is often limited by a narrow field of view (FoV) and the inability to effectively visualize complex 3D structures. The 3D freehand technique, which aligns sequential 2D images for 3D r...

Uncovering ethical biases in publicly available fetal ultrasound datasets.

We explore biases present in publicly available fetal ultrasound (US) imaging datasets, currently at the disposal of researchers to train deep learnin...

Long-term prognostic value of the CT-derived fractional flow reserve combined with atherosclerotic burden in patients with non-obstructive coronary artery disease.

OBJECTIVES: The long-term prognostic significance of the coronary computed tomography angiography (CCTA)-derived fractional flow reserve (CT-FFR) for ...

Correction: Optimizing breast lesions diagnosis and decision-making with a deep learning fusion model integrating ultrasound and mammography: a dual-center retrospective study.

Deep learning estimations of the production cross sections of Br medical radionuclide.

Bromine-77 has a half-life of 56 h and decays nearly exclusively (99.3 %) by electron capture, with prominent gamma rays at 239.0 and 520.7 keV. Once ...

Same-model and cross-model variability in knee cartilage thickness measurements using 3D MRI systems.

PURPOSE: Magnetic Resonance Imaging (MRI) based three-dimensional analysis of knee cartilage has evolved to become fully automatic. However, when impl...

Multimodal deep learning for enhanced breast cancer diagnosis on sonography.

This study introduces a novel multimodal deep learning model tailored for the differentiation of benign and malignant breast masses using dual-view br...

Fusion of FDG and FMZ PET Reduces False-Positives in Predicting Epileptogenic Zone.

BACKGROUND AND PURPOSE: Epilepsy, a globally prevalent neurologic disorder, necessitates precise identification of the epileptogenic zone (EZ) for eff...

Application of Deep Learning Accelerated Image Reconstruction in T2-Weighted Turbo Spin-Echo Imaging of the Brain at 7T.

Prolonged imaging times and motion sensitivity at 7T necessitate advancements in image acceleration techniques. This study evaluates a 7T deep learnin...

Current status and challenges of minimally invasive ultrasound thermal ablation technology.

Image-guided minimally invasive ultrasound thermal ablation has been widely studied for disease treatment due to its unique advantages, such as large ...

OneTouch Automated Photoacoustic and Ultrasound Imaging of Breast in Standing Pose.

We developed an automated photoacoustic and ultrasound breast tomography system that images the patient in the standing pose. The system, named OneTou...

Minimizing human-induced variability in quantitative angiography for a robust and explainable AI-based occlusion prediction in flow diverter-treated aneurysms.

BACKGROUND: Bias from contrast injection variability is a significant obstacle to accurate intracranial aneurysm (IA) occlusion prediction using quant...

Summary Report of the SNMMI AI Task Force Radiomics Challenge 2024.

In medical imaging, challenges are competitions that aim to provide a fair comparison of different methodologic solutions to a common problem. Challen...

A strategy for the automatic diagnostic pipeline towards feature-based models: a primer with pleural invasion prediction from preoperative PET/CT images.

BACKGROUND: This study aims to explore the feasibility to automate the application process of nomograms in clinical medicine, demonstrated through the...

CT derived fractional flow reserve: Part 2 - Critical appraisal of the literature.

The integration of computed tomography-derived fractional flow reserve (CT-FFR), utilizing computational fluid dynamics and artificial intelligence (A...

Exploring the limit of image resolution for human expert classification of vascular ultrasound images in giant cell arteritis and healthy subjects: the GCA-US-AI project.

OBJECTIVES: Prompt diagnosis of giant cell arteritis (GCA) with ultrasound is crucial for preventing severe ocular and other complications, yet expert...

Spatiotemporal distributions and regional disparities of rheumatoid arthritis in 953 global to local locations, 1980-2040, with deep learning-empowered forecasts and evaluation of interventional policies' benefits.

OBJECTIVES: To investigate global to local socioeconomic-driven distributions and inequalities in burdens of rheumatoid arthritis (RA) and to forecast...

Providing context: Extracting non-linear and dynamic temporal motifs from brain activity.

Approaches studying the dynamics of resting-state functional magnetic resonance imaging (rs-fMRI) activity often focus on time-resolved functional con...

Pretreatment Multi-sequence Contrast-Enhanced MRI to Predict Response to Immunotherapy in Unresectable Hepatocellular Carcinoma Using Transformer: A Multicenter Study.

Targeted combined immunotherapy (TCI) has shown certain antitumor effects in patients with unresectable hepatocellular carcinoma(uHCC), but only a su...