Latest AI and machine learning research in radiology for healthcare professionals.
Dedicated MR imaging is highly performant for the evaluation of the primary lesion and should regularly be added to whole-body PET/MR imaging for the initial staging. PET/MR imaging is highly sensitive for the detection of nodal involvement and could be combined with the high specificity of axillary second look ultrasound for the confirmation of the N staging. For M staging, with the exception of ...
Segmentation of intracranial aneurysms (IAs) is an important step for the diagnosis and treatment of IAs. However, the process by which clinicians manually recognize and localize IAs is overly labor intensive. This study aims to develop a deep-learning-based framework (defined as FSTIF-UNet) towards IAs segmentation in un-reconstructed 3D Rotational Angiography (3D-RA) images. 3D-RA sequences from...
BACKGROUND: Proper maintenance of electro-medical devices is crucial for the quality of care to patients and the economic performance of healthcare or...
OBJECTIVES: Virtual monochromatic images (VMI) are increasingly used in clinical practice as they improve contrast-to-noise ratio. However, due to the...
Magnetic resonance imaging (MRI) of the brain has benefited from deep learning (DL) to alleviate the burden on radiologists and MR technologists, and ...
Clinical efforts on precision medicine are driving the need for accurate diagnostic, new prognostic and novel drug predictive assays to inform patient...
Segmenting breast tumors from dynamic contrast-enhanced magnetic resonance (DCE-MR) images is a critical step for early detection and diagnosis of bre...
PURPOSE: This study aimed to predict contrast effects in cardiac computed tomography (CT) from CT localizer radiographs using a deep learning (DL) mod...
PURPOSE: The segmentation of the hepatic arteries (HA) is essential for state-of-the-art pre-interventional planning of selective internal radiation t...
RATIONALE AND OBJECTIVES: Cardiac magnetic resonance imaging is crucial for diagnosing cardiovascular diseases, but lengthy postprocessing and manual ...
OBJECTIVES: The purpose of this study was to evaluate the incremental value of artificial intelligence (AI) compared to the diagnostic accuracy of rad...
The development of high-performance imaging processing algorithms is a core area of photoacoustic tomography. While various deep learning based image ...
OBJECTIVE: In patients with endometriosis, extra pelvic endometriosis is estimated to have an incidence of 11% and a rare subset of extra pelvic lesio...
Due primarily to the excellent soft tissue contrast depictions provided by MRI, the widespread application of head and neck MRI in clinical practice s...
Convolutional neural networks (CNN) have demonstrated good accuracy and speed in spatially registering high signal-to-noise ratio (SNR) structural mag...
Automated demarcation of stoke lesions from monospectral magnetic resonance imaging scans is extremely useful for diverse research and clinical applic...
Deep learning (DL) is a subdomain of artificial intelligence algorithms capable of automatically evaluating subtle graphical features to make highly a...
Near-infrared diffuse optical tomography (DOT) is a promising functional modality for breast cancer imaging; however, the clinical translation of DOT ...
The use of artificial intelligence (AI) in medicine and in urology specifically has increased over the past few years, during which time it has enable...
T3a renal masses include a diverse group of tumors that invade the perirenal and/or sinus fat, pelvicaliceal system, or renal vein. The majority of c...