Radiology

Conditional Generative Adversarial Networks Aided Motion Correction of Dynamic F-FDG PET Brain Studies.

This work set out to develop a motion-correction approach aided by conditional generative adversaria...

Deformable MR-CBCT prostate registration using biomechanically constrained deep learning networks.

BACKGROUND AND PURPOSE: Radiotherapeutic dose escalation to dominant intraprostatic lesions (DIL) in...

Comparison of 11 automated PET segmentation methods in lymphoma.

Segmentation of lymphoma lesions in FDG PET/CT images is critical in both assessing individual lesio...

Ultrasound transmission tomography image reconstruction with a fully convolutional neural network.

Image reconstruction of ultrasound computed tomography based on the wave equation is able to show mu...

Predicting spatial esophageal changes in a multimodal longitudinal imaging study via a convolutional recurrent neural network.

Acute esophagitis (AE) occurs among a significant number of patients with locally advanced lung canc...

Intensity non-uniformity correction in MR imaging using residual cycle generative adversarial network.

Correcting or reducing the effects of voxel intensity non-uniformity (INU) within a given tissue typ...

Fully automated left atrium segmentation from anatomical cine long-axis MRI sequences using deep convolutional neural network with unscented Kalman filter.

This study proposes a fully automated approach for the left atrial segmentation from routine cine lo...

Current and Potential Applications of Artificial Intelligence in Gastrointestinal Stromal Tumor Imaging.

The most common mesenchymal tumors are gastrointestinal stromal tumors (GISTs), which have malignant...

Prognostic value of texture analysis from cardiac magnetic resonance imaging in patients with Takotsubo syndrome: a machine learning based proof-of-principle approach.

Cardiac magnetic resonance (CMR) imaging has become an important technique for non-invasive diagnosi...

Guidance for Interventional Trials Involving Artificial Intelligence.

Displacement Estimation in Ultrasound Elastography Using Pyramidal Convolutional Neural Network.

In this article, two novel deep learning methods are proposed for displacement estimation in ultraso...

A Pilot Study on Convolutional Neural Networks for Motion Estimation From Ultrasound Images.

In recent years, deep learning (DL) has been successfully applied to the analysis and processing of ...

Deep Neural Network-Based Sinogram Super-Resolution and Bandwidth Enhancement for Limited-Data Photoacoustic Tomography.

Photoacoustic tomography (PAT) is a noninvasive imaging modality combining the benefits of optical c...

A Single-Shot Region-Adaptive Network for Myotendinous Junction Segmentation in Muscular Ultrasound Images.

Tracking the myotendinous junction (MTJ) in consecutive ultrasound images is crucial for understandi...

CohereNet: A Deep Learning Architecture for Ultrasound Spatial Correlation Estimation and Coherence-Based Beamforming.

Deep fully connected networks are often considered "universal approximators" that are capable of lea...

Synthetic Elastography Using B-Mode Ultrasound Through a Deep Fully Convolutional Neural Network.

Shear-wave elastography (SWE) permits local estimation of tissue elasticity, an important imaging ma...

Deep Learning to Obtain Simultaneous Image and Segmentation Outputs From a Single Input of Raw Ultrasound Channel Data.

Single plane wave transmissions are promising for automated imaging tasks requiring high ultrasound ...

Support vector machine-based classification of schizophrenia patients and healthy controls using structural magnetic resonance imaging from two independent sites.

Structural brain alterations have been repeatedly reported in schizophrenia; however, the pathophysi...

A Deep Learning Pipeline to Automate High-Resolution Arterial Segmentation With or Without Intravenous Contrast.

BACKGROUND: Existing methods to reconstruct vascular structures from a computerized tomography (CT) ...

Discriminating pseudoprogression and true progression in diffuse infiltrating glioma using multi-parametric MRI data through deep learning.

Differentiating pseudoprogression from true tumor progression has become a significant challenge in ...

Broad Learning Enhanced H-MRS for Early Diagnosis of Neuropsychiatric Systemic Lupus Erythematosus.

In this paper, we explore the potential of using the multivoxel proton magnetic resonance spectrosco...