Journal of computer assisted tomography
May 13, 2025
The applications of machine learning in clinical radiology practice and in particular oncologic imaging practice are steadily evolving. However, there are several potential hurdles for widespread implementation of machine learning in oncologic imagin...
Journal of computer assisted tomography
May 13, 2025
OBJECTIVE: The aim of this study was to evaluate various radiomics-based machine learning classification models using the apparent diffusion coefficient (ADC) and cerebral blood flow (CBF) maps for differentiating between low-grade gliomas (LGGs) and...
Journal of computer assisted tomography
May 13, 2025
OBJECTIVE: To demonstrate the utility of deep learning enhancement (DLE) to achieve diagnostic quality low-dose positron emission tomography (PET)/magnetic resonance (MR) imaging.
Studies in health technology and informatics
May 2, 2025
The goal of segmentation in abdominal imaging for emergency medicine is to accurately identify and delineate organs, as well as to detect and localize pathological areas. This precision is critical for rapid, informed decision-making in acute care sc...
BACKGROUND: Breast magnetic resonance imaging (MRI) protocols often include T2-weighted fat-saturated (T2w-FS) sequences, which support tissue characterization but significantly increase scan time. This study aims to evaluate whether a 2D-U-Net neura...
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...
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.
Magnetic resonance imaging clinics of North America
May 1, 2025
Recent advancements in artificial intelligence (AI) can significantly improve radiologists' workflow, improving efficiency and diagnostic accuracy. Current AI applications within spine imaging are approved to accelerate image acquisition time, improv...
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