Radiology

A predictive signal model for dynamic cardiac magnetic resonance imaging.

Robust dynamic cardiac magnetic resonance imaging (MRI) has been a long-standing endeavor-as real-ti...

Simulator, machine learning, and artificial intelligence: Time has come to assist prenatal ultrasound diagnosis.

In this Commentary authors investigated and extended the role of simulator in assisting obstetric so...

Development of a Deep Learning-Based System for Optic Nerve Characterization in Transorbital Ultrasound Images on a Multicenter Data Set.

OBJECTIVE: Characterization of the optic nerve through measurement of optic nerve diameter (OND) and...

A unified hybrid transformer for joint MRI sequences super-resolution and missing data imputation.

High-resolution multi-modal magnetic resonance imaging (MRI) is crucial in clinical practice for acc...

Accelerated Measurement of Carotid Plaque Volume Using Artificial Intelligence Enhanced 3D Ultrasound.

BACKGROUND: Carotid plaque volume (CPV) can be measured by 3D ultrasound and may be a better predict...

Fetal magnetic resonance imaging artifacts: role of deep learning to improve imaging.

Radiology as a Specialty in the Era of Artificial Intelligence: A Systematic Review and Meta-analysis on Medical Students, Radiology Trainees, and Radiologists.

RATIONALE AND OBJECTIVES: Artificial intelligence (AI) is changing radiology by automating tasks and...

Fully automated segmentation and radiomics feature extraction of hypopharyngeal cancer on MRI using deep learning.

OBJECTIVES: To use convolutional neural network for fully automated segmentation and radiomics featu...

Computerised emergency work-up for mass-like brain MRI lesions: can explainable AI support radiologists?

A comparison of performance between a deep learning model with residents for localization and classification of intracranial hemorrhage.

Intracranial hemorrhage (ICH) from traumatic brain injury (TBI) requires prompt radiological investi...

An automatic interpretable deep learning pipeline for accurate Parkinson's disease diagnosis using quantitative susceptibility mapping and T1-weighted images.

Parkinson's disease (PD) diagnosis based on magnetic resonance imaging (MRI) is still challenging cl...

Bridging the simulation-to-real gap for AI-based needle and target detection in robot-assisted ultrasound-guided interventions.

BACKGROUND: Artificial intelligence (AI)-powered, robot-assisted, and ultrasound (US)-guided interve...

Hemodynamic study of blood flow in the aorta during the interventional robot treatment using fluid-structure interaction.

An interventional robot is a means for vascular diagnosis and treatment, and it can perform dredging...

Consistency of Artificial Intelligence (AI)-based Diagnostic Support Software in Short-term Digital Mammography Reimaging After Core Needle Biopsy.

To evaluate the consistency in the performance of Artificial Intelligence (AI)-based diagnostic supp...

An Optimized Deep Learning Model for Predicting Mild Cognitive Impairment Using Structural MRI.

Early diagnosis of mild cognitive impairment (MCI) with magnetic resonance imaging (MRI) has been sh...

Bloch simulator-driven deep recurrent neural network for magnetization transfer contrast MR fingerprinting and CEST imaging.

PURPOSE: To develop a unified deep-learning framework by combining an ultrafast Bloch simulator and ...

Diagnostic ability of deep learning in detection of pancreatic tumour.

Pancreatic cancer is associated with higher mortality rates due to insufficient diagnosis techniques...

Bidirectional feature matching based on deep pairwise contrastive learning for multiparametric MRI image synthesis.

Multi-parametric MR image synthesis is an effective approach for several clinical applications where...

Automated deep learning auto-segmentation of air volumes for MRI-guided online adaptive radiation therapy of abdominal tumors.

. In the current MR-Linac online adaptive workflow, air regions on the MR images need to be manually...

Utility of accelerated T2-weighted turbo spin-echo imaging with deep learning reconstruction in female pelvic MRI: a multi-reader study.

OBJECTIVES: To determine the clinical feasibility of T2-weighted turbo spin-echo (T2-TSE) imaging wi...

CT Angiography Radiomics Combining Traditional Risk Factors to Predict Brain Arteriovenous Malformation Rupture: a Machine Learning, Multicenter Study.

This study aimed to develop a machine learning model for predicting brain arteriovenous malformation...