Radiology

Computational methods to automate the initial interpretation of lower extremity arterial Doppler and duplex carotid ultrasound studies.

BACKGROUND: Lower extremity arterial Doppler (LEAD) and duplex carotid ultrasound studies are used f...

Quantitative Molecular Positron Emission Tomography Imaging Using Advanced Deep Learning Techniques.

The widespread availability of high-performance computing and the popularity of artificial intellige...

Machine Learning-Based Multiparametric Magnetic Resonance Imaging Radiomics for Prediction of H3K27M Mutation in Midline Gliomas.

OBJECTIVE: H3K27M mutation in gliomas has prognostic implications. Previous magnetic resonance imagi...

Reverberation Noise Suppression in Ultrasound Channel Signals Using a 3D Fully Convolutional Neural Network.

Diffuse reverberation is ultrasound image noise caused by multiple reflections of the transmitted pu...

Development and Validation of a Radiomics Model for Differentiating Bone Islands and Osteoblastic Bone Metastases at Abdominal CT.

Background It is important to diagnose sclerotic bone lesions in order to determine treatment strate...

Deep Learning-based Angiogram Generation Model for Cerebral Angiography without Misregistration Artifacts.

Background Digital subtraction angiography (DSA) generates an image by subtracting a mask image from...

Recent Advances in Imaging of Preclinical, Sporadic, and Autosomal Dominant Alzheimer's Disease.

Observing Alzheimer's disease (AD) pathological changes in vivo with neuroimaging provides invaluabl...

Convolutional neural networks for PET functional volume fully automatic segmentation: development and validation in a multi-center setting.

PURPOSE: In this work, we addressed fully automatic determination of tumor functional uptake from po...

Editorial For "Reduction of Contrast Agent Dose in Cardiovascular MR Angiography Using Deep Learning".

Image Quality Enhancement Using a Deep Neural Network for Plane Wave Medical Ultrasound Imaging.

Plane wave imaging (PWI), a typical ultrafast medical ultrasound imaging mode, adopts single plane w...

A deep cascaded segmentation of obstructive sleep apnea-relevant organs from sagittal spine MRI.

PURPOSE: The main purpose of this work was to develop an efficient approach for segmentation of stru...

Correcting Susceptibility Artifacts of MRI Sensors in Brain Scanning: A 3D Anatomy-Guided Deep Learning Approach.

Echo planar imaging (EPI), a fast magnetic resonance imaging technique, is a powerful tool in functi...

Current applications of deep-learning in neuro-oncological MRI.

PURPOSE: Magnetic Resonance Imaging (MRI) provides an essential contribution in the screening, detec...

Artificial Intelligence in Magnetic Resonance Imaging-based Prostate Cancer Diagnosis: Where Do We Stand in 2021?

CONTEXT: Men suspected of harboring prostate cancer (PCa) increasingly undergo multiparametric magne...

AI-based structure-function correlation in age-related macular degeneration.

Sensitive and robust outcome measures of retinal function are pivotal for clinical trials in age-rel...

Emerging methods for the characterization of ischemic heart disease: ultrafast Doppler angiography, micro-CT, photon-counting CT, novel MRI and PET techniques, and artificial intelligence.

After an ischemic event, disruptive changes in the healthy myocardium may gradually develop and may ...

Multi-task weak supervision enables anatomically-resolved abnormality detection in whole-body FDG-PET/CT.

Computational decision support systems could provide clinical value in whole-body FDG-PET/CT workflo...

Automated detection of critical findings in multi-parametric brain MRI using a system of 3D neural networks.

With the rapid growth and increasing use of brain MRI, there is an interest in automated image class...

[Artificial intelligence in psychiatry: predictive value of characteristics on MR imaging of the brain].

The clinical application of neuroimaging for psychological complaints has so far been limited to the...

Application of a Machine Learning Approach for the Analysis of Clinical and Radiomic Features of Pretreatment [F]-FDG PET/CT to Predict Prognosis of Patients with Endometrial Cancer.

PURPOSE: To examine the prognostic significance of pretreatment 2-deoxy-2-[F]fluoro-D-glucose ([F]-F...

Machine Learning in Body Composition Analysis.

Body composition analysis (BCA) generates objective anthropometric data that can inform prognosticat...