Background MRI protocols typically involve many imaging sequences and often require too much time. Purpose To simulate artificial intelligence (AI)-directed stratified scanning for screening breast MRI with various triage thresholds and evaluate its ...
Accurate and timely diagnosis of brain tumors is critical for patient management and treatment planning. Magnetic resonance imaging (MRI) is a widely used modality for brain tumor detection and characterization, often aided by the administration of g...
Cancer imaging : the official publication of the International Cancer Imaging Society
May 23, 2025
BACKGROUND: Accurate differentiation between benign and malignant adnexal masses is crucial for patients to avoid unnecessary surgical interventions. Ultrasound (US) is the most widely utilized diagnostic and screening tool for gynecological diseases...
Purpose To develop an artificial intelligence (AI) model based on gadoxetic acid-enhanced MRI to assist radiologists in hepatocellular carcinoma (HCC) diagnosis. Materials and Methods This retrospective study included patients with focal liver lesion...
OBJECTIVES: Previous deep learning studies have not explored the synergistic effects of ROI dimensions (2D/2.5D/3D), peritumoral expansion levels (0-8 mm), and segmentation scenarios (ROI only vs. ROI original). Our study aims to evaluate the perform...
Clinical and experimental rheumatology
May 1, 2025
Artificial intelligence (AI) is rapidly transforming radiology, with over 200 CE-marked products in the EU and more than 750 AI-based devices authorised by the FDA in the US, mainly used for x-ray, CT, MRI, and ultrasound imaging. Despite regulatory ...
BACKGROUND: To compare liver image quality and lesion detection using an AI-augmented T1-weighted sequence on hepatobiliary-phase gadoxetate-enhanced magnetic resonance imaging (MRI).
OBJECTIVE: To compare the quality of deep learning-reconstructed turbo spin-echo (DL-TSE) and conventionally interpolated turbo spin-echo (Conv-TSE) techniques in contrast-enhanced MRI of the neck.
Purpose To build a deep learning framework using contrast-enhanced MRI for lesion segmentation and automatic molecular subtype classification in breast cancer. Materials and Methods This retrospective multicenter study included patients with biopsy-p...
Purpose To develop an unsupervised deep learning framework for generalizable blood-brain barrier leakage detection using dynamic contrast-enhanced MRI, without requiring pharmacokinetic models and arterial input function estimation. Materials and Met...
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