Radiology

Latest AI and machine learning research in radiology for healthcare professionals.

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A CUBS-Compatible Ultrasound Morphology and Uncertainty-Aware Baseline for Carotid Intima-Media Segmentation and Preliminary Risk Prediction

Carotid atherosclerosis is a major contributor to ischemic stroke and transient ischemic attack. Con...

Separating Intrinsic Ambiguity from Estimation Uncertainty in Deep Generative Models for Linear Inverse Problems

Recently, deep generative models have been used for posterior inference in inverse problems, includi...

Prediction of Pivot Shift Grade Using In-Vivo Ultrasound Bone Tracking During Sit-Stand-Sit: A Machine Learning Feasibility Study

Background: The pivot shift (PS) test is the most specific clinical examination for anterolateral ro...

Contactless ultrasound chest vibration mapping discriminates respiratory and cardiac patients from healthy individuals.

Contactless assessment of cardiopulmonary function remains an unmet need, with current approaches re...

NexOP: Joint Optimization of NEX-Aware k-space Sampling and Image Reconstruction for Low-Field MRI

Modern low-field magnetic resonance imaging (MRI) technology offers a compelling alternative to stan...

Pyramid Self-contrastive Learning Framework for Test-time Ultrasound Image Denoising

The inherent electronic and speckle noise complicates clinical interpretation of ultrasound images. ...

When Brains Disagree: Biological Ambiguity Underlies the Challenge of Amyloid PET Synthesis from Structural MRI

Structural MRI-to-amyloid PET synthesis has been proposed as a non-invasive alternative for amyloid ...

Optimization in Sparse 2D to Dense 3D Weakly Supervised Learning: Application to Multi-Label Segmentation of Large ex vivo MRI Data

INTRODUCTION | Fully supervised 3D segmentation of high-resolution ex vivo MRI is limited by the pro...

MRI reveals the hierarchical organization of abdominal biological aging from shared burden to disease-specific organ engagement

Multi-organ biological aging is often represented as parallel organ-specific clocks, but how age gap...

Development of a Deep Learning Model Integrating CT Images and Blood Data for the Diagnosis of Acute Cholecystitis

Purpose: In this study, we aimed to develop and evaluate an artificial intelligence-based diagnostic...

An fMRI dataset of verbalized spontaneous thought with annotated transcripts and self-report trait measures

Spontaneous thought is pervasive in everyday human cognition, yet datasets capturing its neural dyna...

Talk2QSP: Deriving Executable Scenarios from Unstructured Literature via Human-in-the-Loop Agents

Quantitative Systems Pharmacology (QSP) models play an inherently interventional role in pharmaceuti...

Med-StepBench: A Hierarchical Reasoning Framework for Evaluating Hallucinations in Medical Vision-Language Models

Large vision-language models (VLMs) demonstrate strong performance in medical image understanding, b...

Predictive Radiomics for Evaluation of Cancer Immune SignaturE in Glioblastoma: the PRECISE-GBM study

Background: Radiogenomics allows identification of radiological biomarkers for genomic phenotypes. I...

Set-Based Groupwise Registration for Variable-Length, Variable-Contrast Cardiac MRI

Quantitative cardiac magnetic resonance imaging (MRI) enables non-invasive myocardial tissue charact...

Product-of-Gaussian-Mixture Diffusion Models for Joint Nonlinear MRI Reconstruction

Recently, diffusion models have attracted considerable attention for magnetic resonance image recons...

Counterfactual Stress Testing for Image Classification Models

Deep learning models in medical imaging often fail when deployed in new clinical environments due to...

Cortical reconstruction and anatomical parcellation of high-resolution multi-modal postmortem ex vivo MRI of the human infant brain

High-resolution postmortem (ex vivo) magnetic resonance imaging enables detailed examination of brai...

CT Attenuation Map Derived Body Composition Is Associated with Cardiorespiratory Fitness in Multicenter External Validation

Aim: Exercise capacity is a powerful predictor of cardiovascular risk. In patients unable to exercis...

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